Publications

@article{Klucznik2026-nw,

title = {First online real-time motion-including prostate and bladder 

 dose reconstruction during prostate radiotherapy},

author = {Karolina Klucznik and Thomas Ravkilde and Simon Skouboe and Paul Keall and Laura Happersett and Hai Pham and Brian Leong and Pengpeng Zhang and Grace Tang and Per R Poulsen},

year  = {2026},

date = {2026-01-01},

journal = {Radiother. Oncol.},

volume = {214},

number = {111298},

pages = {111298},

publisher = {Elsevier BV},

abstract = {BACKGROUND AND MOTIVATION: Organ motion can distort prostate 

 radiotherapy doses. This study presents the first real-time 

 calculation of the motion-induced dose distortions performed 

 online during prostate radiotherapy. METHODS: Twenty patients 

 were treated with stereotactic prostate radiotherapy of 35 Gy or 

 40 Gy in 5 fractions using intrafractional image guidance for 

 real-time prostate localization and patient repositioning upon 

 prostate misalignments exceeding 1.5 mm. In-house developed 

 software performed motion-including prostate and bladder dose 

 reconstruction during treatment. The reconstructed doses were 

 retrospectively validated against a clinical treatment planning 

 system (TPS) where motion was encoded in treatment plans as 

 multiple 3D isocenter shifts. Hypothetical doses delivered 

 without intra-treatment repositioning were reconstructed 

 post-treatment for comparison to illustrate how the dose 

 reconstruction allows easy assessment of the effectiveness of 

 the used motion mitigation method. RESULTS: Dose reconstruction 

 was performed for 91 fractions either online during treatment (n = 41) or retrospectively using recorded motion (n = 50). The 

 real-time calculated doses (calculated by the in-house software 

 using a simplified algorithm) agreed with TPS calculations with 

 mean ($±$std) differences of 0.1 % ($±$0.9 %) for clinical 

 target volume (CTV) D95% and 0.2 % ($±$0.2 %) for bladder 

 V36Gy. The mean time per online dose-reconstruction ($±$std) 

 was 336 $±$ 86 ms, proving the real-time applicability of the 

 proposed method. The average ($±$std) motion-induced dose 

 distortions for individual fractions with intrafractional image 

 guidance were -0.5 % ($±$1.0 %) for CTV D95% and +0.1 % 

 ($±$0.5 %) for bladder V36Gy for individual fractions. 

 Accumulated across all fractions of each patient, these 

 deviations decreased to 0.0 % ($±$0.7 %) for the CTV and 

 +0.1 % ($±$0.2 %) for the bladder. In contrast, without 

 intratreatment repositioning, deviations would have been -1.3 % 

 ($±$5.0 %) for CTV D95% and +0.5 % ($±$1.5 %) for 

 bladder V36Gy, with individual fractions exhibiting clinically 

 unacceptable CTV D95% decreases of up to 42.5 %. CONCLUSION: 

 This study marks the first clinical realization of real-time 

 motion-including dose reconstruction for both target and 

 organ-at-risk structures paving the way for real-time 

 dose-guided radiotherapy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Keall2026,

title = {The KIM real-time prostate cancer IGRT technology journey: From clinical trials to your clinic},

author = {Paul J. Keall and Jeremy Booth and Thomas Eade and Emily A Hewson and Amar U Kishan and Michael Jameson and Kankean Kandasamy and Andrew Kneebone and Doan Trang Nguyen and Per Rugaard Poulsen and Chandrima Sengupta and Daniel E Spratt and Alison Tree and Pengpeng Zhang and Jarad Martin},

doi = {10.1016/j.ijrobp.2026.01.010},

issn = {0360-3016},

year  = {2026},

date = {2026-01-00},

journal = {International Journal of Radiation Oncology*Biology*Physics},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Buttgen2025-ud,

title = {Toward real-time dose-guided radiation therapy: deformable 

 multileaf collimator tracking using motion-model-derived 

 volumetric images in lung and liver cancer patients},

author = {Laura Esther Büttgen and Chandrima Sengupta and Jonathan Sykes and Danielle Chrystall and Owen Dillon and Jeremy Todd Booth and Maegan Stewart and Jonathan Hindmarsh and René Werner and Paul Keall and Emily A Hewson},

year  = {2025},

date = {2025-12-01},

journal = {Phys. Med. Biol.},

volume = {70},

number = {24},

pages = {245005},

publisher = {IOP Publishing},

abstract = {Objective. Accurate dose delivery in the presence of anatomical 

 motion and deformation remains a major challenge in radiation 

 therapy. Real-time dose-guided radiation therapy addresses this 

 challenge by integrating volumetric imaging, dose accumulation, 

 and adaptive treatment in a quasi-continuous manner. Unlike 

 previous studies limited to rigid motion or phantom data, this 

 study employs a motion model to generate real-time volumetric 

 images, enabling deformable dose-guided multileaf collimator 

 (MLC) tracking in lung and liver cancer patients.Approach. 

 Deformable dose-guided MLC tracking was simulated for ten 

 patients (4 lung, 6 liver). The workflow included: (1) training 

 a patient-specific linear regression-based motion model relating 

 external respiratory signals (RPMs) to deformation vector fields 

 using planning 4DCT data, (2) generating volumetric images using 

 treatment RPM signals, (3) simulating dose-guided MLC tracking 

 comparing deformable, rigid and no tracking, and (4) evaluating 

 the motion model accuracy using fiducial markers and kV imaging. 

 Dosimetric accuracy was assessed using 3D Gamma analysis 

 (2%/2mm) and normalized root mean squared error by comparing 

 simulated delivered dose to a motion-compensated baseline. 

 Motion model error was quantified as the distance between 

 predicted and ground truth marker positions.Main results. 

 Deformable tracking significantly outperformed rigid and no 

 tracking (p<0.001), achieving a mean Gamma passing rate 

 of95.0$±$6.0%, compared to86.3$±$10.0%and74.8$±$15.2%, 

 respectively. Lung patients showed a greater benefit in Gamma 

 passing rate (deformable:98%vs rigid:85%, $±$13%) than 

 liver patients (93%vs89%,+4%). The dosimetric benefit 

 correlated with respiratory amplitude, with larger motion 

 yielding greater improvements. The motion model demonstrated 

 high accuracy, with a mean error of0.1$±$2.9mm.Significance. 

 This study demonstrates the feasibility and dosimetric advantage 

 of deformable dose-guided MLC tracking using 

 motion-model-derived volumetric imaging in patient data. These 

 findings represent a critical step toward the clinical 

 implementation of real-time dose-guided radiation therapy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Trada2025,

title = {Mid-treatment changes in intra-tumoural metabolic heterogeneity correlate to outcomes in oropharyngeal squamous cell carcinoma patients},

author = {Yuvnik Trada and Mark T. Lee and Michael G. Jameson and Phillip Chlap and Paul Keall and Daniel Moses and Peter Lin and Allan Fowler},

doi = {10.1186/s13550-025-01226-6},

issn = {2191-219X},

year  = {2025},

date = {2025-12-00},

journal = {EJNMMI Res},

volume = {15},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {Abstract

          

            Background

            This study evaluated mid-treatment changes in intra-tumoural metabolic heterogeneity and quantitative FDG-PET/CT imaging parameters and correlated the changes with treatment outcomes in oropharyngeal squamous cell cancer (OPSCC) patients. 114 patients from two independent cohorts underwent baseline and mid-treatment (week 3) FDG-PET. Standardized uptake value maximum (SUVmax), standardized uptake value mean (SUVmean), metabolic tumour volume (MTV), and total lesional glycolysis (TLG) were measured. Intra-tumoural metabolic heterogeneity was quantified as the area under a cumulative SUV-volume histogram curve (AUC-CSH). Baseline and relative change (%∆) in imaging features were correlated to locoregional recurrence free survival (LRRFS) using multivariate Cox regression analysis. Patients were stratified into three risk groups utilising ∆AUC-CSH and known prognostic features, then compared using Kaplan-Meier analysis.

          

          

            Results

            Median follow up was 39 months. 18% of patients developed locoregional recurrence at 2 years. A decrease in heterogeneity (∆AUC-CSH: 24%) was observed mid-treatment. There was no statistically significant difference in tumour heterogeneity (AUC-CSH) at baseline (p = 0.134) and change at week 3 (p = 0.306) between p16 positive and p16 negative patients. Baseline imaging features did not correlate to LRRFS. However, ∆MTV (aHR 1.04; 95% CI 1.03–1.06; p < 0.001) and ∆AUC-CSH (aHR 0.96; 95% CI 0.94–0.98; p = 0.004) were correlated to LRRFS. Stratification using ∆AUC-CSH and p16 status into three groups showed significant differences in LRR (2 year LRRFS 94%, 79%, 17%; log rank p < 0.001). Stratification using ∆AUC-CSH and ∆MTV into three groups showed significant differences in LRR (2 year LRRFS 93%, 70%, 17%; log rank p < 0.001).

          

          

            Conclusion

            Mid-treatment changes in intra-tumoural FDG-PET/CT heterogeneity correlated with treatment outcomes in OPSCC and may help with response prediction. These findings suggest potential utility in designing future risk adaptive clinical trials.

          },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Dillon2025,

title = {Real-time spatiotemporal optimization during imaging},

author = {Owen Dillon and Benjamin Lau and Shalini K. Vinod and Paul J. Keall and Tess Reynolds and Jan-Jakob Sonke and Ricky T. O’Brien},

doi = {10.1038/s44172-025-00391-9},

issn = {2731-3395},

year  = {2025},

date = {2025-12-00},

journal = {Commun Eng},

volume = {4},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {Abstract

          High quality imaging is required for high quality medical care, especially in precision applications such as radiation therapy. Patient motion during image acquisition reduces image quality and is either accepted or dealt with retrospectively during image reconstruction. Here we formalize a general approach in which data acquisition is treated as a spatiotemporal optimization problem to solve in real time so that the acquired data has a specific structure that can be exploited during reconstruction. We provide results of the first-in-world clinical trial implementation of our spatiotemporal optimization approach, applied to respiratory correlated 4D cone beam computed tomography for lung cancer radiation therapy (NCT04070586, ethics approval 2019/ETH09968). Performing spatiotemporal optimization allowed us to maintain or improve image quality relative to the current clinical standard while reducing scan time by 63% and reducing scan radiation by 85%, improving clinical throughput and reducing the risk of secondary tumors. This result motivates application of the general spatiotemporal optimization approach to other types of patient motion such as cardiac signals and other modalities such as CT and MRI.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hewson2025-iw,

title = {Adaptive radiation therapy for real-time deformations using 

 dose-optimised multileaf collimator tracking: anin-silicoproof 

 of concept for multiple lung lesions},

author = {Emily A Hewson and Lars Mejnertsen and Jeremy T Booth and Paul J Keall},

year  = {2025},

date = {2025-11-01},

journal = {Phys. Med. Biol.},

volume = {70},

number = {22},

pages = {225006},

publisher = {IOP Publishing},

abstract = {Objective.Anatomy continuously deforms during radiation therapy. 

 Although real-time volumetric imaging approaches are emerging, 

 there is a lack of adaptive strategies that account for 

 intrafraction deformations. The purpose of this study was to 

 develop a multileaf collimator (MLC) tracking method that adapts 

 to deformations and evaluate the performance for lung cancer 

 with multiple lesions.Approach.Dose-optimised deformable MLC 

 tracking was developed using a fast dose calculation to 

 accumulate dose at each timestep. The accumulated planned doses 

 were deformed to represent the desired dose distribution for the 

 deformed anatomy and the MLC leaf positions were optimised to 

 minimise the difference between the delivered and deformed 

 planned dose. Dose-optimised deformable MLC tracking was 

 evaluated using four lung cancer cases generated using the 4D 

 XCAT digital phantom. Stereotactic ablative radiotherapy 

 treatment plans were created using a planning target volume 

 (PTV) margin expansion of 5 mm on the gross tumour volumes 

 (GTV). Treatments were simulated using three patient-measured 

 motions for each phantom. The doses accumulated using the fast 

 dose calculation model with MLC tracking were compared to an 

 internal target volume (ITV)-based approach.Main results.The 

 volume of the PTVs were reduced by an average of 34% using 

 dose-optimised deformable MLC tracking compared to the ITV-based 

 approach. The mean differences and standard deviations from the 

 planned doses were -0.5% $±$ 0.6% for the GTV D100%and 

 -1.1% $±$ 0.6% for the PTV D98%when dose-optimised 

 deformable MLC tracking was used, and -5.2% $±$ 8.8% for the 

 ITV D100%and -13.8% $±$ 12.9% for the PTV D98%when no 

 tracking was used.Significance.The study demonstrated a proof of 

 concept for dose-optimised deformable MLC tracking to reduce 

 dosimetric errors for deforming anatomy. The proposed method 

 could enable the safe reduction of treatment margins for 

 multiple independently moving targets in the lung compared to 

 the standard of care.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chrystall2025-of,

title = {Experimental investigation of real-time 3D beam's eye view 

 image-guided radiotherapy for prostate SBRT},

author = {Danielle Chrystall and Maegan Stewart and Freeman Jin and Chandrima Sengupta and Maria D'Oliveira and Alannah Kejda and Levi Madden and Doan Trang Nguyen and Paul Keall and Jeremy Booth},

year  = {2025},

date = {2025-11-01},

journal = {Med. Phys.},

volume = {52},

number = {11},

pages = {e70086},

publisher = {Wiley},

abstract = {BACKGROUND: Real-time image-guided radiotherapy (IGRT) is 

 critical for accurate dose delivery during stereotactic body 

 radiotherapy (SBRT). Beam's eye view (BEV) imaging offers the 

 unique advantage of reporting motion in the most dosimetrically 

 relevant frame of reference without additional imaging dose. 

 However, its clinical use is limited by poor contrast-to-noise 

 ratio and marker occlusion by treatment beam apertures. Deep 

 learning enables fast identification of indistinct marker 

 features even in low contrast images, facilitating real-time 

 BEV-IGRT. To support integration with standard-equipped linear 

 accelerators, accurate 3D localization is 

 essential-necessitating the development of a 3D BEV-IGRT system. 

 PURPOSE: This study aimed to develop and experimentally evaluate 

 a novel real-time 3D BEV-IGRT system for potential clinical 

 implementation during prostate SBRT. METHODS: A real-time 3D 

 BEV-IGRT system was developed by integrating a deep 

 learning-based 2D MV marker segmentation method with a 3D IGRT 

 framework. Marker positions were segmented on MV images using a 

 convolutional neural network (CNN) and used to predict 3D motion 

 via a Gaussian maximum likelihood estimation method. A failure 

 mode and effects analysis (FMEA) was performed by a 

 multidisciplinary team. Mitigation strategies were implemented 

 for high-risk failure modes, and risk priority numbers (RPN) 

 were recalculated. Experimental system evaluation was guided by 

 failure modes identified through the FMEA. An anthropomorphic 

 pelvic phantom with three implanted gold markers was mounted on 

 a 3D motion-programmable platform. System performance was 

 assessed under static and dynamic conditions, using treatment 

 plans of increasing complexity, ranging from open fields to 

 patient-representative volumetric modulated arc therapy plans. 

 Dynamic performance was evaluated using four patient-derived 

 prostate motion traces. Localization accuracy (mean error $±$ 

 1 SD) was assessed by comparing system-reported positions to 

 ground truth derived from known motion trajectories or static 

 displacements. 5th/95th error percentiles were calculated. 

 System latency was measured as the time delay between motion 

 initiation and system-reported displacement. Clinically 

 acceptable accuracy was defined as within $±$ 2 mm in the 

 superior-inferior (SI), anterior-posterior (AP) and left-right 

 (LR) directions, and latency $łeq$ 500 ms. RESULTS: Forty-six 

 failure modes were identified through the FMEA. High-risk 

 failure causes included algorithmic limitations, algorithmic 

 errors, human error, and marker occlusion. Incorporation of 

 mitigation strategies-including eligibility screening, staff 

 training, and workflow formalization-resulted in an average RPN 

 reduction of 43% across the top ten high-risk failure modes. A 

 risk-informed quality assurance program was designed to support 

 clinical implementation. Overall 3D BEV-IGRT system accuracy was 

 0.1 $±$ 0.7 mm (SI), -0.1 $±$ 0.8 mm (AP), and 0.1 $±$ 0.7 

 mm (LR). Accuracy remained within $±$ 2 mm in all directions 

 across all individual tests. Overall 5th/95th percentile errors 

 were [-1.0, 1.3] mm (SI), [-1.2, 0.9] mm (AP), and [-0.9, 1.0] 

 mm (LR). System latency was 300 $±$ 100 ms. CONCLUSIONS: The 

 3D BEV-IGRT system was experimentally validated, demonstrating 

 clinically acceptable localization accuracy and latency, 

 supporting its feasibility for clinical implementation. 

 Integrated risk mitigation strategies effectively reduced 

 workflow risk and promoted understanding of system 

 vulnerabilities. Deployment is planned for a prostate SBRT 

 clinical trial.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hindmarsh2025-ip,

title = {Systematic review of prospective hazard analysis in radiation 

 therapy},

author = {Jonathan Hindmarsh and Sonja Dieterich and Jeremy Booth and Paul Keall},

year  = {2025},

date = {2025-09-01},

journal = {Med. Phys.},

volume = {52},

number = {9},

pages = {e18110},

publisher = {Wiley},

abstract = {INTRODUCTION: Prospective hazard analysis (PHA) was introduced 

 to the wider medical physics community by the initiation of 

 American association of physicists in medicine task group 100 in 

 2003. Since then, there has been increasing interest in the 

 applicability of PHA to radiotherapy for the purpose of keeping 

 patients safe and assessing the risks within the whole practice 

 of radiotherapy. The purpose of this research was to review the 

 PHA literature focusing on which techniques and technologies 

 have been assessed, how they have been assessed, and what can be 

 learnt. METHODS: The search for English language, peer-reviewed, 

 full-text articles was conducted across five databases and the 

 citations of three seminal papers using a common search 

 strategy. The collation, filtration, and analysis of articles 

 was conducted in accordance with the preferred reporting items 

 for systematic reviews and meta-analyses (PRISMA) statement 

 reporting standard utilizing the following PICOS approach: 

 Population: x-ray external beam radiation therapy, Intervention: 

 prospective hazard analysis, Comparison: none, Outcome: patient 

 safety, Study characteristics: details of applied technique. 

 RESULTS: 689 unique studies were identified. 62 were determined 

 to be eligible for inclusion. PHA has been applied to C-arm 

 treatment systems (17), stereotactic radiosurgery (8), 

 TomoTherapy (6), stereotactic body radiotherapy (5), Ethos (5), 

 Halcyon (3), MRIdian (3), review activities (3), commissioning 

 (2), unity (1), volumetric modulated arc therapy (1), surface 

 guidance (1), CyberKnife (1), RefleXion (1) and other novel 

 software and hardware systems (6). Disciplines involved in the 

 studies were physicists (92%), physicians (75%), radiation 

 therapists or dosimetrists (71%), external experts (38%), and 

 facilitators (33%). Failure mode and effects analysis (FMEA) 

 was used in 75% of studies, 10% used FMEA derived methods, 

 10% used system theoretic process analysis, and 5% used other 

 methods. From the FMEA studies, 579 high-risk failure modes were 

 extracted covering all aspects of the radiotherapy process, 50% 

 applied to patient treatment delivery sessions and 25% applied 

 to contouring and treatment planning. The mitigation strategies 

 recommended by studies tended to add to the departmental 

 workload. CONCLUSIONS: 62 studies were identified that used PHA 

 in radiotherapy, within the included studies: patient journey 

 was the most analyzed process, of the disciplines physicists 

 were involved in the most studies, FMEA the most common 

 technique, and the delivery of patient treatment was the 

 greatest source of high-risk failure modes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Cheng2025-rt,

title = {Volumetric imaging during head and neck radiation therapy using 

 a Kalman filter tracking approach},

author = {Chen Cheng and Mark Gardner and Owen Dillon and Youssef Ben Bouchta and Purnima Sundaresan and Paul Keall},

year  = {2025},

date = {2025-08-01},

journal = {Phys. Med. Biol.},

volume = {70},

number = {17},

pages = {175006},

publisher = {IOP Publishing},

abstract = {Objective.Head and neck (H&N) volumetric imaging using a 

 standard linear accelerator during treatment delivery could 

 enable motion management strategies to improve geometric 

 accuracy and avoid critical organs at risk (OARs). We developed 

 a Kalman filter (KF) tracking approach to image 3D H&N anatomy 

 in real-time using 2D x-ray images.Approach.Our method produces 

 a 3D image corresponding to each 2D projection at the time the 

 projection is acquired. A KF incorporates 3D-to-2D projection 

 geometry to weight the prior 3D deformable vector field (DVF) 

 and 2D DVF measurement generated by registering each 2D x-ray 

 image to a 2D image generated using the planning computed 

 tomography (CT) image. We performed a simulation study using the 

 planning CT image and 5 minute surface motion traces for 12 H&N 

 cancer patients. 2D intra-treatment images served as input to 

 obtain the 3D DVF and resultant 3D images. We evaluated the KF 

 tracking performance on the target and critical structures using 

 centroid error, Hausdorff distance and Dice coefficient metrics. 

 The results were compared to those from the current standard of 

 care, no tracking.Results.Using KF tracking, we obtained a mean 

 tumour centroid error of -0.2 $±$ 0.6 mm, 0.3 $±$ 0.8 mm, 

 and 0.1 $±$ 0.4 mm in the left-right, anterior-posterior and 

 superior-inferior axes respectively, compared to 0.9 $±$ 1.0 

 mm, -0.8 $±$ 1.1 mm, and -0.1 $±$ 1.2 mm with no tracking. 

 The 95th percentile Hausdorff distance for the tumour contour 

 was 1.2 $±$ 0.6 mm with KF tracking and 2.0 $±$ 1.2 mm 

 without tracking. The Dice coefficients were 0.91 $±$ 0.08, 

 0.93 $±$ 0.04, 0.93 $±$ 0.05, 0.79 $±$ 0.18, and 0.89 

 $±$ 0.06 for the tumour, brainstem, left parotid gland, optic 

 chiasm, and spinal cord respectively with KF tracking and 0.78 

 $±$ 0.17, 0.84 $±$ 0.06, 0.83 $±$ 0.08, 0.39 $±$ 0.14, 

 and 0.70 $±$ 0.13 respectively with no 

 tracking.Significance.Volumetric imaging with a standard linear 

 accelerator was investigated using KF tracking with H&N patient 

 data. The findings demonstrate accurate tracking of both the 

 tumour and OARs, indicating potential applicability for the 

 proposed method in a clinical translation setting.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Wang2025-fs,

title = {TrackRAD2025 challenge dataset: real-time tumor tracking for 

 MRI-guided radiotherapy},

author = {Yiling Wang and Elia Lombardo and Adrian Thummerer and Tom Blöcker and Yu Fan and Yue Zhao and Christianna Iris Papadopoulou and Coen Hurkmans and Rob H N Tijssen and Pia A W Görts and Shyama U Tetar and Davide Cusumano and Martijn Pw Intven and Pim Borman and Marco Riboldi and Denis Dudáš and Hilary Byrne and Lorenzo Placidi and Marco Fusella and Michael Jameson and Miguel Palacios and Paul Cobussen and Tobias Finazzi and Cornelis J A Haasbeek and Paul Keall and Christopher Kurz and Guillaume Landry and Matteo Maspero},

year  = {2025},

date = {2025-07-01},

journal = {Med. Phys.},

volume = {52},

number = {7},

pages = {e17964},

publisher = {Wiley},

abstract = {PURPOSE: Magnetic resonance imaging (MRI) to visualize 

 anatomical motion is becoming increasingly important when 

 treating cancer patients with radiotherapy. Hybrid MRI-linear 

 accelerator (MRI-linac) systems allow real-time motion 

 management during irradiation. This paper presents a 

 multi-institutional real-time MRI time series dataset from 

 different MRI-linac vendors. The dataset is designed to support 

 developing and evaluating real-time tumor localization 

 (tracking) algorithms for MRI-guided radiotherapy within the 

 TrackRAD2025 challenge ( 

 https://trackrad2025.grand-challenge.org/). ACQUISITION AND 

 VALIDATION METHODS: The dataset consists of sagittal 2D cine 

 MRIs (20-20543 frames per scan) in 585 patients from six centers 

 (3 Dutch, 1 German, 1 Australian, and 1 Chinese). Tumors in the 

 thorax, abdomen, and pelvis acquired on two commercially 

 available MRI-linacs (0.35 T and 1.5 T) were included. For 108 

 cases, irradiation targets or tracking surrogates were manually 

 segmented on each temporal frame. The dataset was randomly split 

 into a public training set of 527 cases (477 unlabeled and 50 

 labeled) and a private testing set of 58 cases (all labeled). 

 DATA FORMAT AND USAGE NOTES: The data is publicly available 

 under the TrackRAD2025 collection: 

 https://doi.org/10.57967/hf/4539. Both the images and 

 segmentations for each patient are available in metadata format. 

 POTENTIAL APPLICATIONS: This novel clinical dataset will enable 

 the development and evaluation of real-time tumor localization 

 algorithms for MRI-guided radiotherapy. By enabling more 

 accurate motion management and adaptive treatment strategies, 

 this dataset has the potential to advance the field of 

 radiotherapy significantly.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Klucznik2025-cz,

title = {Cone-beam CT-based estimations of prostate motion and dose 

 distortion during radiotherapy},

author = {Karolina A Klucznik and Thomas Ravkilde and Simon Skouboe and Ditte S Møller and Steffen Hokland and Paul Keall and Simon Buus and Lise Bentzen and Per R Poulsen},

year  = {2025},

date = {2025-07-01},

journal = {Phys. Imaging Radiat. Oncol.},

volume = {35},

number = {100798},

pages = {100798},

publisher = {Elsevier BV},

abstract = {Background and purpose: Intra-fractional prostate translational 

 and rotational (6DoF) motion can cause dose distortions. As 

 intra-fractional motion monitoring is often unavailable, this 

 study compares three methods to use pre- and post-treatment cone 

 beam CTs (CBCT) to estimate prostate positioning errors during 

 treatment and their dosimetric impact. Material and Methods: 

 Eighteen patients received prostate radiotherapy with 

 pre-treatment CBCT setup. For 7-10 fractions per patient 

 (total:174), triggered kV-images were acquired every 3 s during 

 beam-on and a CBCT was acquired post-treatment. The 6DoF 

 prostate position error during treatment was determined from the 

 kV-images (ground truth) and estimated from the CBCTs assuming a 

 static position as in the pre-CBCT(Scenario1), a linear drift 

 between pre- and post-CBCT position(Scenario2) or a static 

 position as in the post-CBCT(Scenario3). The positioning errors 

 and prostate dose from each scenario were compared with the 

 ground truth. Results: Scenario1 was inferior to the others with 

 prostate position root-mean-square errors of 1.1 mm(LR), 1.7 

 mm(AP) and 1.8 mm(CC). Scenario2 and 3 were similarly accurate 

 with root-mean-square errors of 0.5 mm(LR), 0.9 mm(AP) and 0.8 

 mm(CC) (Scenario2) and 0.6 mm(LR), 1.1 mm(AP) and 0.9 mm(CC) 

 (Scenario3). The prostate position errors reduced the CTV 

 D99.5% by more than 2/3 % at 24/15 % of the fractions, 

 respectively. The sensitivity in detecting these dose deficits 

 was low for Scenario1 (9-16 %) and considerably higher for 

 Scenario2 (68-76 %) and Scenario3 (86-91 %). All scenarios 

 showed high specificity (93-99 %). Conclusion: Using the 

 post-CBCT prostate position, acquired right after treatment, 

 performed best in detecting intra-fractional prostate position 

 errors and CTV dose deficits. It offers a scalable and 

 conservative estimate of motion-induced dose distortions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Keall2025-dm,

title = {Real-time dose-guided radiation therapy},

author = {Paul J Keall and Issam El Naqa and Martin F Fast and Emily A Hewson and Nicholas Hindley and Per Poulsen and Chandrima Sengupta and Neelam Tyagi and David E J Waddington},

year  = {2025},

date = {2025-07-01},

journal = {Int. J. Radiat. Oncol. Biol. Phys.},

volume = {122},

number = {4},

pages = {787–801},

publisher = {Elsevier BV},

abstract = {Dramatic strides have been made in real-time adaptive radiation 

 therapy, where treating single tumors as dynamic but rigid 

 bodies has demonstrated a halving of toxicities for prostate 

 cancer. However, the human body is much more complex than a 

 rigid body. This review explores the ongoing development and 

 future potential of dose-guided radiation therapy, where the 

 three core process steps of volumetric imaging of the patient, 

 dose accumulation, and dose-guided treatment adaptation occur 

 quasi-continuously during treatment, fully accounting for the 

 complexity of the dynamic human body. The clinical evidence 

 supporting real-time adaptive radiation therapy was reviewed. 

 The foundational studies, status, and potential of real-time 

 volumetric imaging using both x-ray and magnetic resonance 

 imaging technology were described. The development of real-time 

 dose accumulation to the dynamic patient was evaluated, and a 

 method to measure real-time dose delivery was assessed. The 

 growth of real-time treatment adaptation was examined. 

 Literature demonstrates continued improvements in patient 

 outcomes because the treatment becomes more conformal to the 

 dynamic patient. Real-time volumetric imaging using both x-ray 

 and magnetic resonance imaging technology is poised for broader 

 implementation. Real-time dose accumulation has demonstrated 

 clinical feasibility, with approximations made to achieve 

 real-time operation. Real-time treatment adaptation to deforming 

 targets and multiple targets has been experimentally 

 demonstrated. Tying together the inputs of the real-time 

 volumetric anatomy and dose accumulation is real-time treatment 

 adaptation that uses the available degrees of freedom to 

 optimize the dose delivered to the patient, maximizing the 

 treatment intent. Opportunities exist for artificial 

 intelligence to accelerate the application of dose-guided 

 radiation therapy to broader patient use. In summary, the 

 emerging field of real-time dose-guided radiation therapy has 

 the potential to significantly improve patient outcomes. The 

 advances are primarily software-driven and therefore could be 

 widely available and cost-effective upgrades to improve imaging 

 and targeting cancer.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hindley2025-xn,

title = {An open-source deep learning framework for respiratory motion 

 monitoring and volumetric imaging during radiation therapy},

author = {Nicholas Hindley and Paul J Keall},

year  = {2025},

date = {2025-07-01},

journal = {Med. Phys.},

volume = {52},

number = {7},

pages = {e18015},

publisher = {Wiley},

abstract = {BACKGROUND: Real-time image-guided radiation therapy (IGRT) was 

 first clinically implemented more than 25 years ago but is yet 

 to find widespread adoption. Existing approaches to real-time 

 IGRT require dedicated or specialized equipment that is not 

 available in most treatment centers and most techniques focus 

 exclusively on targets without tracking the surrounding 

 organs-at-risk (OARs). PURPOSE: To address the need for 

 inexpensive real-time IGRT, we developed Voxelmap, a deep 

 learning framework that achieves 3D respiratory motion 

 estimation and volumetric imaging using the data and resources 

 already available in standard clinical settings. This framework 

 can also be adapted to other imaging modalities such as 

 MRI-Linacs. In contrast with existing approaches, which 

 constrain the solution space with linear priors, Voxelmap 

 encourages diffeomorphic mappings that are topology-preserving 

 and invertible. METHODS: Deformable image registration and 

 forward-projection or slice extraction were used to generate 

 patient-specific training datasets of 3D deformation vector 

 fields (DVFs) and 2D images (or k-space data) from pretreatment 

 4D-CT or 4D-MRI scans. The XCAT and CoMBAT digital phantoms and 

 SPARE Grand Challenge Dataset provided synthetic and patient 

 data, respectively. Five network architectures were used to 

 predict 3D DVFs from 2D imaging data. Networks A-C were trained 

 on x-ray images, Network D was trained on MR images and Network 

 E was trained on k-space data. Using Voxelmap, network-generated 

 3D DVFs were used to warp both structures contoured on the 

 peak-exhale pretreatment image and the image itself to enable 

 simultaneous target and OAR tracking and volumetric imaging. 

 Using the standard-of-care approach, contours were expanded to 

 internal target volumes. RESULTS: Validating on digital phantom 

 data for x-ray guided treatments of cardiac arrhythmia, mean 

 Dice similarity between predicted and ground-truth target and 

 OAR contours for Networks A-C ranged from 0.81 $±$ 0.05 to 

 0.82 $±$ 0.05 and 0.78 $±$ 0.04 to 0.81 $±$ 0.04, 

 respectively, while target centroid error ranged from 2.0 $±$ 

 0.5 to 2.3 $±$ 0.9 mm. For MRI-based digital phantom data, 

 mean Dice similarity for target and OAR contours was 0.91 $±$ 

 0.06 and 0.90 $±$ 0.02 for both Networks D and E, while target 

 centroid error ranged from 1.7 $±$ 0.8 to 1.8 $±$ 0.8 mm. 

 For x-ray-based lung cancer patient data, mean Dice similarity 

 for target and OAR contours for Networks A-C ranged from 0.86 

 $±$ 0.05 to 0.89 $±$ 0.04 and 0.94 $±$ 0.01 to 0.97 $±$ 

 0.01, respectively. However, in terms of target centroid error, 

 only Network A outperformed an ITV-based approach at 1.8 $±$ 

 0.7 mm while Networks B and C exhibited large errors of 2.7 

 $±$ 1.2 to 3.5 $±$ 1.4 mm, respectively. Target volumes 

 dynamically shifted using Voxelmap were 31 % smaller than the 

 standard-of-care. CONCLUSIONS: Voxelmap provides a generalized, 

 open-source tool for intrafraction respiratory motion monitoring 

 and volumetric imaging. Comparing tracking errors across 

 synthetic and patient data revealed that certain network 

 architectures are more robust to the scatter and noise profiles 

 encountered in typical clinical settings. These learnings will 

 inform future developments in real-time motion tracking. Our 

 code is available at 

 https://github.com/Image-X-Institute/Voxelmap .},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gardner2025-ui,

title = {Deep learning-based real-time detection of head and neck tumors 

 during radiation therapy},

author = {Mark Gardner and Youssef Ben Bouchta and Daniel Truant and Adam Mylonas and Jonathan Sykes and Purnima Sundaresan and Paul J Keall},

year  = {2025},

date = {2025-07-01},

journal = {Phys. Med. Biol.},

volume = {70},

number = {15},

pages = {155007},

publisher = {IOP Publishing},

abstract = {Objective.Clinical drivers for real-time head and neck (H&N) 

 tumor tracking during radiation therapy (RT) are accounting for 

 motion caused by changes to the immobilization mask fit, and to 

 reduce mask-related patient distress by replacing the masks with 

 patient motion management methods. The purpose of this paper is 

 to investigate a deep learning-based method to segment H&N 

 tumors in patient kilovoltage (kV) x-ray images to enable 

 real-time H&N tumor tracking during RT.Approach.An 

 ethics-approved clinical study collected data from 17 H&N 

 cancer patients undergoing conventional H&N RT. For each 

 patient, personalized conditional generative adversarial 

 networks (cGANs) were trained to segment H&N tumors in kV x-ray 

 images. Network training data were derived from each patient's 

 planning CT and contoured gross tumor volumes (GTV). For each 

 training epoch, the planning CT and GTV were deformed and 

 forward projected to create the training dataset. The testing 

 data consisted of kV x-ray images used to reconstruct the 

 pre-treatment CBCT volume for the first, middle and end 

 fractions. The ground truth tumor locations were derived by 

 deformably registering the planning CT to the pre-treatment CBCT 

 and then deforming the GTV and forward projecting the deformed 

 GTV. The generated cGAN segmentations were compared to ground 

 truth tumor segmentations using the absolute magnitude of the 

 centroid error and the mean surface distance (MSD) metrics.Main results.The centroid error for the nasopharynx (n= 4), oropharynx (n= 9) and larynx (n= 4) patients was 1.5 $±$ 0.9 

 mm, 2.4 $±$ 1.6 mm, 3.5 $±$ 2.2 mm respectively and the MSD 

 was 1.5 $±$ 0.3 mm, 1.9 $±$ 0.9 mm and 2.3 $±$ 1.0 mm 

 respectively. There was a weak correlation between the centroid error and the LR tumor location (r= 0.41), which was higher for oropharynx patients (r= 0.77).Significance.The paper reports on 

 markerless H&N tumor detection accuracy using kV images. 

 Accurate tracking of H&N tumors can enable more precise RT 

 leading to mask-free RT enabling better patient outcomes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Brighi2025-kt,

title = {Evaluating the technical feasibility of biology-guided dose 

 painting in proton therapy},

author = {Caterina Brighi and Giovanni Parrella and Letizia Morelli and Silvia Molinelli and Giuseppe Magro and Sara Lillo and Alberto Iannalfi and Mario Ciocca and Sara Imparato and David E J Waddington and Paul Keall and Chiara Paganelli and Ester Orlandi and Guido Baroni},

year  = {2025},

date = {2025-07-01},

journal = {Phys. Imaging Radiat. Oncol.},

volume = {35},

number = {100832},

pages = {100832},

publisher = {Elsevier BV},

abstract = {Biology-guided voxel-level inverse prescription mapping for dose 

 painting (DP) using diffusion-weighted magnetic resonance 

 imaging was evaluated for technical feasibility in proton 

 therapy for 10 skull-base chordoma patients. Patient-specific DP 

 prescriptions were generated from tumour cellularity and 

 implemented in a clinical treatment planning system. Compared 

 with uniform plans, DP achieved lower conformity (although >97 

 %), improved target dose metrics, reduced doses to most organs 

 at risk, and increased tumour control probability without 

 exceeding clinical constraints. DP proton therapy is technically 

 feasible and may enhance treatment effectiveness.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ahmed2025-vg,

title = {Patient-specific deep learning tracking for real-time 2D 

 pancreas localisation in kV-guided radiotherapy},

author = {Abdella M Ahmed and Levi Madden and Maegan Stewart and Brian V Y Chow and Adam Mylonas and Ryan Brown and Gabrielle Metz and Meegan Shepherd and Carlito Coronel and Leigh Ambrose and Alex Turk and Maiko Crispin and Andrew Kneebone and George Hruby and Paul Keall and Jeremy T Booth},

year  = {2025},

date = {2025-07-01},

journal = {Phys. Imaging Radiat. Oncol.},

volume = {35},

number = {100794},

pages = {100794},

publisher = {Elsevier BV},

abstract = {Background and purpose: In pancreatic stereotactic body 

 radiotherapy (SBRT), accurate motion management is crucial for 

 the safe delivery of high doses per fraction. Intra-fraction 

 tracking with magnetic resonance imaging-guidance for gated SBRT 

 has shown potential for improved local control. Visualisation of 

 pancreas (and surrounding organs) remains challenging in 

 intra-fraction kilo-voltage (kV) imaging, requiring implanted 

 fiducials. In this study, we investigate patient-specific 

 deep-learning approaches to track the gross-tumour-volume (GTV), 

 pancreas-head and the whole-pancreas in intra-fraction kV 

 images. Materials and methods: 

 Conditional-generative-adversarial-networks were trained and 

 tested on data from 25 patients enrolled in an ethics-approved 

 pancreatic SBRT trial for contour prediction on intra-fraction 

 2D kV images. Labelled digitally-reconstructed-radiographs 

 (DRRs) were generated from contoured 

 planning-computed-tomography (CTs) (CT-DRRs) and cone-beam-CTs 

 (CBCT-DRRs). A population model was trained using CT-DRRs of 19 

 patients. Two patient-specific model types were created for six 

 additional patients by fine-tuning the population model using 

 CBCT-DRRs (CBCT-models) or CT-DRRs (CT-models) acquired in 

 exhale-breath-hold. Model predictions on unseen triggered-kV 

 images from the corresponding six patients were evaluated 

 against projected-contours using Dice-Similarity-Coefficient 

 (DSC), centroid-error (CE), average Hausdorff-distance (AHD), 

 and Hausdorff-distance at 95th-percentile (HD95). Results: The 

 mean $±$ 1SD (standard-deviation) DSCs were 0.86 $±$ 0.09 

 (CBCT-models) and 0.78 $±$ 0.12 (CT-models). For AHD and CE, 

 the CBCT-model predicted contours within 2.0 mm $geq$90.3 % of 

 the time, while HD95 was within 5.0 mm $geq$90.0 % of the 

 time, and had a prediction time of 29.2 $±$ 3.7 ms per 

 contour. Conclusion: The patient-specific CBCT-models 

 outperformed the CT-models and predicted the three contours with 

 90th-percentile error $łeq$2.0 mm, indicating the potential for 

 clinical real-time application.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mylonas2025-ry,

title = {Patient-specific prostate segmentation in kilovoltage images for 

 radiation therapy intrafraction monitoring via deep learning},

author = {Adam Mylonas and Zeyao Li and Marco Mueller and Jeremy T Booth and Ryan Brown and Mark Gardner and Andrew Kneebone and Thomas Eade and Paul J Keall and Doan Trang Nguyen},

year  = {2025},

date = {2025-06-01},

journal = {Commun. Med. (Lond.)},

volume = {5},

number = {1},

pages = {212},

abstract = {BACKGROUND: During radiation therapy, the natural movement of 

 organs can lead to underdosing the cancer and overdosing the 

 healthy tissue, compromising treatment efficacy. Real-time 

 image-guided adaptive radiation therapy can track the tumour and 

 account for the motion. Typically, fiducial markers are implanted 

 as a surrogate for the tumour position due to the low 

 radiographic contrast of soft tissues in kilovoltage (kV) images. 

 A segmentation approach that does not require markers would 

 eliminate the costs, delays, and risks associated with marker 

 implantation. METHODS: We trained patient-specific conditional 

 Generative Adversarial Networks for prostate segmentation in kV 

 images. The networks were trained using synthetic kV images 

 generated from each patient's own imaging and planning data, 

 which are available prior to the commencement of treatment. We 

 validated the networks on two treatment fractions from 30 

 patients using multi-centre data from two clinical trials. 

 RESULTS: Here, we present a large-scale proof-of-principle study 

 of x-ray-based markerless prostate segmentation for globally 

 available cancer therapy systems. Our results demonstrate the 

 feasibility of a deep learning approach using kV images to track 

 prostate motion across the entire treatment arc for 30 patients 

 with prostate cancer. The mean absolute deviation is 1.4 and 1.6 

 mm in the anterior-posterior/lateral and superior-inferior 

 directions, respectively. CONCLUSIONS: Markerless segmentation 

 via deep learning may enable real-time image guidance on 

 conventional cancer therapy systems without requiring implanted 

 markers or additional hardware, thereby expanding access to 

 real-time adaptive radiation therapy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gardner2025-qe,

title = {Evaluation of 4D cone-beam CT reconstruction methods for 

 lung images acquired using rapid cone-beam CT acquisition: a 

 phantom study},

author = {Mark Gardner and Owen Dillon and Tess Reynolds and John Kipritidis and Magdalena Bazalova-Carter and Hilary Byrne and Maegan Stewart and Jeremy Booth and Paul J Keall},

year  = {2025},

date = {2025-06-01},

journal = {Phys. Med. Biol.},

volume = {70},

number = {13},

pages = {135004},

publisher = {IOP Publishing},

abstract = {Objective. Cone-beam CT (CBCT) technological advances for linear 

 accelerators (Linacs) have led to CBCT imaging in <20 s, which 

 can reduce radiation therapy treatment times. However, these 

 rapid CBCT scans only allow for 3DCBCT images. In this paper we 

 evaluate 4DCBCT reconstruction methods for rapid acquisition 

 3DCBCT protocol scans using an anthropomorphic breathing 

 phantom.Approach. We evaluate two previously developed 

 motion-compensated Feldkamp-Davis-Kress (MCFDK) methods, using a 

 prior-motion model (MCFDK-Prior) and a data-driven MCFDK method 

 (MCFDK-DD), on CBCT images of the phantom using an Ethos linac. 

 The deformable phantom lungs contained three synthetic tumours 

 and a commercial phantom motion platform with a sinusoidal 

 breathing pattern. The phantom was imaged in free-breathing with 

 rapid (16.6 s) and standard (30.8 s) thorax 3DCBCT acquisition 

 protocols, then reimaged while stationary at inhale and exhale, 

 which were the ground truth reconstructions. MCFDK 

 reconstructions were compared with conventional 3D-FDK and 

 4D-FDK reconstructions. Image quality was compared between all 

 reconstructions using mean square error (MSE), structural 

 similarity index measurement (SSIM), peak signal-to-noise 

 (PSNR), edge response width (ERW) for the diaphragm-lung border 

 for the right lung, tumour centroid accuracy, tumour dice 

 similarity coefficient and sphericity.Main results. For all 

 metrics the MCFDK-Prior reconstructions performed better than 

 the 3D-FDK reconstructions. Similarly for all tumour-related 

 metrics as well as ERW the MCFDK-DD reconstructions performed 

 better than then 3D-FDK reconstructions, but the overall MSE, 

 SSIM and PSNR were similar for the MCFDK-DD and 3D-FDK 

 reconstructions. For all metrics except for tumour centroid 

 error the MCFDK-Prior method produced better quality 

 reconstructions than the MCFDK-DD method. 4D-FDK reconstructions 

 produced poor quality volumes.Significance. We demonstrated that 

 4DCBCT reconstruction for rapid CBCT acquisition protocols is 

 possible and leads to reduced motion artefacts and more accurate 

 reconstructions when compared to 3DCBCT reconstructions. The 

 4DCBCT methods demonstrated in this paper will allow for fast, 

 accurate 4DCBCT acquisition for new linacs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Krim2025-la,

title = {Monte Carlo simulation of a novel medical linac concept for 

 highly conformal x-ray FLASH cancer radiotherapy},

author = {Deae-Eddine Krim and Brendan Whelan and Mindy Harkness and Karl Otto and Billy W Jr Loo and Magdalena Bazalova-Carter},

year  = {2025},

date = {2025-05-01},

journal = {Sci. Rep.},

volume = {15},

number = {1},

pages = {17604},

abstract = {A growing body of pre-clinical research has demonstrated the 

 potential of ultra-high dose-rate (UHDR) radiotherapy to reduce 

 normal tissue toxicity while maintaining tumor control. However, 

 owing to a wide range of technical difficulties, no existing 

 x-ray systems are capable of highly conformal UHDR radiotherapy 

 to humans. In this work, we designed and simulated a novel x-ray 

 UHDR system representing a next-generation solution for rapid and 

 highly conformal treatment delivery. This system comprises 16 

 stationary beamlines employing a new class of highly efficient 

 linear accelerator to generate 12 MeV electron beams 

 electromagnetically steered onto a bremsstrahlung target, a 

 collimator with channels to create a divergent array of x-ray 

 beamlets, and a translating patient couch. The system design was 

 tuned to maximize the dose rate while minimizing beam penumbra 

 and cross-channel leakage. A simulation framework was developed 

 to facilitate iteration of machine parameters during 

 optimization. A treatment plan for a case of locally advanced 

 lung cancer was generated using an in-house optimizer to assess 

 the capabilities of the x-ray UHDR system. Individual beamlets of 

 10, 15, and 20-mm in size can produce isocenter dose rates of 

 17.3, 18.7, and 19.7-Gy/mAs and cross-channel leakage of 2.3, 

 1.9, and [Formula: see text], respectively. The novel UHDR 

 10-Gy/fraction plan exhibited comparable or improved conformity, 

 homogeneity, and mean dose to organs at risk compared to the 

 clinically used plan and it was delivered in 500 ms with more 

 than [Formula: see text] of target volume receiving local dose 

 rates higher than 40Gy/s.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Trada2025-jn,

title = {Mid-treatment changes in intra-tumoural metabolic heterogeneity 

 correlate to outcomes in oropharyngeal squamous cell carcinoma 

 patients},

author = {Yuvnik Trada and Mark T Lee and Michael G Jameson and Phillip Chlap and Paul Keall and Daniel Moses and Peter Lin and Allan Fowler},

year  = {2025},

date = {2025-04-01},

journal = {EJNMMI Res.},

volume = {15},

number = {1},

pages = {31},

publisher = {Springer Science and Business Media LLC},

abstract = {BACKGROUND: This study evaluated mid-treatment changes in 

 intra-tumoural metabolic heterogeneity and quantitative 

 FDG-PET/CT imaging parameters and correlated the changes with 

 treatment outcomes in oropharyngeal squamous cell cancer (OPSCC) 

 patients. 114 patients from two independent cohorts underwent 

 baseline and mid-treatment (week 3) FDG-PET. Standardized uptake 

 value maximum (SUVmax), standardized uptake value mean 

 (SUVmean), metabolic tumour volume (MTV), and total lesional 

 glycolysis (TLG) were measured. Intra-tumoural metabolic 

 heterogeneity was quantified as the area under a cumulative 

 SUV-volume histogram curve (AUC-CSH). Baseline and relative 

 change (%∆) in imaging features were correlated to locoregional 

 recurrence free survival (LRRFS) using multivariate Cox 

 regression analysis. Patients were stratified into three risk 

 groups utilising ∆AUC-CSH and known prognostic features, then 

 compared using Kaplan-Meier analysis. RESULTS: Median follow up 

 was 39 months. 18% of patients developed locoregional 

 recurrence at 2 years. A decrease in heterogeneity (∆AUC-CSH: 

 24%) was observed mid-treatment. There was no statistically 

 significant difference in tumour heterogeneity (AUC-CSH) at baseline (p = 0.134) and change at week 3 (p = 0.306) between 

 p16 positive and p16 negative patients. Baseline imaging 

 features did not correlate to LRRFS. However, ∆MTV (aHR 1.04; 

 95% CI 1.03-1.06; p < 0.001) and ∆AUC-CSH (aHR 0.96; 95% CI 0.94-0.98; p = 0.004) were correlated to LRRFS. Stratification 

 using ∆AUC-CSH and p16 status into three groups showed 

 significant differences in LRR (2 year LRRFS 94%, 79%, 17%; 

 log rank p < 0.001). Stratification using ∆AUC-CSH and ∆MTV into 

 three groups showed significant differences in LRR (2 year LRRFS 

 93%, 70%, 17%; log rank p < 0.001). CONCLUSION: Mid-treatment 

 changes in intra-tumoural FDG-PET/CT heterogeneity correlated 

 with treatment outcomes in OPSCC and may help with response 

 prediction. These findings suggest potential utility in 

 designing future risk adaptive clinical trials.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Janowicz2025-nn,

title = {Enhanced detection of glioblastoma vasculature with 

 superparamagnetic iron oxide nanoparticles and MRI},

author = {Phillip W Janowicz and Thomas Boele and Richard T Maschmeyer and Yaser H Gholami and Emma G Kempe and Brett W Stringer and Shihani P Stoner and Marie Zhang and Taymin Toit-Thompson and Fern Williams and Aude Touffu and Lenka Munoz and Zdenka Kuncic and Caterina Brighi and David E J Waddington},

year  = {2025},

date = {2025-04-01},

journal = {Sci. Rep.},

volume = {15},

number = {1},

pages = {14283},

publisher = {Springer Science and Business Media LLC},

abstract = {Detecting glioblastoma infiltration in the brain is challenging 

 due to limited MRI contrast beyond the enhancing tumour core. 

 This study aims to investigate the potential of 

 superparamagnetic iron oxide nanoparticles (SPIONs) as contrast 

 agents for improved detection of diffuse brain cancer. We 

 examine the distribution and pharmacokinetics of SPIONs in 

 glioblastoma models with intact and disrupted blood-brain 

 barriers. Using MRI, we imaged RN1-luc and U87MG mice injected 

 with Gadovist and SPIONs, observing differences in blood-brain 

 barrier permeability. Peripheral imaging showed strong uptake of 

 nanoparticles in the liver and spleen, while vascular and renal 

 signals were transient. Susceptibility gradient mapping enabled 

 positive nanoparticle contrast within tumours and provided 

 additional information on tumour angiogenesis. This approach 

 offers a novel method for detecting diffuse brain cancer. Our 

 findings demonstrate that SPIONs enhance glioblastoma detection 

 beyond conventional MRI, providing insights into tumour 

 angiogenesis and opening new avenues for early diagnosis and 

 targeted treatment strategies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hindmarsh2025-ec,

title = {A dosimetric comparison of helical tomotherapy treatment 

 delivery with real-time adaption and no motion correction},

author = {Jonathan Hindmarsh and Scott Crowe and Julia Johnson and Chandrima Sengupta and Jemma Walsh and Sonja Dieterich and Jeremy Booth and Paul Keall},

year  = {2025},

date = {2025-04-01},

journal = {Phys. Imaging Radiat. Oncol.},

volume = {34},

number = {100741},

pages = {100741},

publisher = {Elsevier BV},

abstract = {This study assesses the ability of a helical tomotherapy system 

 equipped with kV imaging and optical surface guidance to adapt 

 to motion traces in real-time. To assess the delivery accuracy 

 with motion, a unified testing framework was used. The average 2 

 %/2 mm γ-fail rates across all lung traces were 0.1 % 

 for motion adapted and 17.4 % for no motion correction. Average 

 2 %/2 mm γ-fail rates across all prostate traces were 

 0.4 % for motion adapted and 12.2 % for no motion correction. 

 Real-time motion adaption was shown to improve the accuracy of 

 dose delivered to a moving phantom compared with no motion 

 adaption. MeSH Keywords: Radiotherapy, image-guided; Radiation 

 therapy, targeted.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gardner2025-er,

title = {Investigation of cardiac substructure automatic segmentation 

 methods on synthetically generated 4D cone-beam CT images},

author = {Mark Gardner and Robert N Finnegan and Owen Dillon and Vicky Chin and Tess Reynolds and Paul J Keall},

year  = {2025},

date = {2025-04-01},

journal = {Med. Phys.},

volume = {52},

number = {4},

pages = {2224–2237},

publisher = {Wiley},

abstract = {BACKGROUND: STereotactic Arrhythmia Radioablation (STAR) is a 

 novel noninvasive method for treating arrythmias in which 

 external beam radiation is directed towards subregions of the 

 heart. Challenges for accurate STAR targeting include small 

 target volumes and relatively large patient motion, which can 

 lead to radiation related patient toxicities. 4D Cone-beam CT 

 (CBCT) images are used for stereotactic lung treatments to 

 account for respiration-related patient motion. 4D-CBCT imaging 

 could similarly be used to account for respiration-related 

 patient motion in STAR; however, the poor contrast of heart 

 tissue in CBCT makes identifying cardiac substructures in 

 4D-CBCT images challenging. If cardiac structures can be 

 identified in pre-treatment 4D-CBCT images, then the location of 

 the target volume can be more accurately identified for 

 different phases of the respiration cycle, leading to more 

 accurate targeting and a reduction in patient toxicities. 

 PURPOSE: The aim of this simulation study is to investigate the 

 accuracy of different cardiac substructure segmentation methods 

 for 4D-CBCT images. METHODS: Repeat 4D-CT scans from 13 lung 

 cancer patients were obtained from The Cancer Imaging Archive. 

 Synthetic 4D-CBCT images for each patient were simulated by 

 forward projecting and reconstructing each respiration phase of 

 a chosen ``testing'' 4D-CT scan. Eighteen cardiac structures 

 were segmented from each respiration phase image in the testing 

 4D-CT using the previously validated platipy toolkit. The 

 platipy segmentations from the testing 4D-CT were defined as the 

 ground truth segmentations for the synthetic 4D-CBCT images. 

 Five different 4D-CBCT cardiac segmentation methods were 

 investigated: 3D Rigid Alignment, 4D Rigid Alignment, Direct 

 CBCT Segmentation, Contour Transformation, and Synthetic CT 

 Segmentation methods. For all methods except the Direct CBCT 

 segmentation method, a separate 4D-CT (Planning CT) was used to 

 assist in generating 4D-CBCT segmentations. Segmentation 

 performance was measured using the Dice similarity coefficient 

 (DSC), Hausdorff distance (HD), mean surface distance (MSD), and 

 volume ratio (VR) metrics. RESULTS: The mean $±$ standard 

 deviation DSC for all cardiac substructures for the 3D Rigid 

 Alignment, 4D Rigid Alignment, Direct CBCT Segmentation, Contour 

 Transformation, and Synthetic CT Segmentation methods were 0.48 

 $±$ 0.29, 0.52 $±$ 0.29, 0.37 $±$ 0.32, 0.53 $±$ 0.29, 

 0.57 $±$ 0.28, respectively. Similarly, the HD values were 

 10.9 $±$ 3.6 , 9.9 $±$ 2.6 , 17.3 $±$ 5.3 , 9.9 $±$ 2.8 

 , 9.3 $±$ 3.0 mm, the MSD values were 2.9 $±$ 0.6 , 2.9 

 $±$ 0.6 , 6.3 $±$ 2.5 , 2.5 $±$ 0.6 , 2.4 $±$ 0.8 mm, 

 and the VR Values were 0.81 $±$ 0.12, 0.78 $±$ 0.14, 1.10 

 $±$ 0.47, 0.72 $±$ 0.15, 0.98 $±$ 0.44, respectively. Of 

 the five methods investigated the Synthetic CT segmentation 

 method generated the most accurate segmentations for all 

 calculated segmentation metrics. CONCLUSION: This simulation 

 study investigates the accuracy of different cardiac 

 substructure segmentation methods for 4D-CBCT images. Accurate 

 4D-CBCT cardiac segmentation will provide more accurate 

 information on the location of cardiac anatomy during STAR 

 treatments which can lead to safer and more effective STAR. As 

 the data and segmentation methods used in this study are all 

 open source, this study provides a useful benchmarking tool to 

 evaluate other CBCT cardiac segmentation methods.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Byrne2025-sm,

title = {More accessible functional lung imaging: non-contrast 

 CT-ventilation demonstrates strong association and agreement 

 with PET-ventilation},

author = {Hilary L Byrne and Nina Eikelis and Jonathan Dusting and Andreas Fouras and Paul J Keall and Piraveen Pirakalathanan},

year  = {2025},

date = {2025-04-01},

journal = {Respir. Res.},

volume = {26},

number = {1},

pages = {163},

publisher = {Springer Science and Business Media LLC},

abstract = {BACKGROUND: Computed Tomography (CT) ventilation imaging (CTVI) 

 is an emerging ventilation imaging technique. CTVI 

 implementations have been widely validated against alternative 

 ventilation imaging techniques but have been limited to clinical 

 research only. The first CTVI commercial product, CT LVAS 

 (4DMedical, Melbourne, Australia), was recently released 

 enabling its use in clinical practice. This study quantitatively 

 compares ventilation images from CT LVAS and previously 

 validated research CTVI algorithms to Galligas PET ventilation. 

 METHODS: 16 patients with Galligas PET and paired inhale/exhale 

 breath-hold CT images were taken from a publicly available 

 dataset on The Cancer Imaging Archive. Ventilation images were 

 produced using CT LVAS and two previously published algorithms: 

 (1) utilising the Hounsfield Unit difference (CTVI_HU); and (2) 

 utilising the Jacobian determinant (CTVI_Jac). CTVI images were 

 compared to the reference standard Galligas PET using 

 Bland-Altman analysis of lobar ventilation, voxel-wise Spearman 

 correlation, and Dice similarity coefficient (DSC) of regions of 

 interest representing the top 85% and 15% of ventilation 

 function. RESULTS: Bland-Altman analysis showed overall bias of 

 < 0.01% for all CTVI methods (95% confidence interval: 

 $±$7.4% for CT LVAS, $±$ 9.1% for CTVI_HU, $±$ 7.9% for 

 CTVI_Jac). The mean Spearman correlation between CTVI and 

 Galligas PET was 0.61 $±$ 0.14 (p < 0.01) for CT LVAS, 0.68 

 $±$ 0.10 (p < 0.01) for CTVI_HU, and 0.57 $±$ 0.15 (p < 

 0.01) for CTVI_Jac. The mean DSC for the top 85% was 0.91 $±$ 

 0.03 for CT LVAS, 0.92 $±$ 0.02 for CTVI_HU, and 0.91 $±$ 

 0.03 for CTVI_Jac, with the DSC for CTVI_HU significantly higher 

 than the other two CTVI methods. The DSC for the top 15% was 

 0.47 $±$ 0.17 for CT LVAS, 0.53 $±$ 0.16 for CTVI_HU, and 

 0.47 $±$ 0.18 for CTVI_Jac. CONCLUSIONS: In a comparison to 

 Galligas PET ventilation imaging, CT LVAS performs similarly to 

 previous CTVI methods. Bland-Altman analysis for quantification 

 of lobar ventilation demonstrates negligible bias. Mean 

 voxel-wise Spearman correlations are moderate to good. DSC of 

 functionally thresholded lung regions are similar for all CTVI 

 methods. These results warrant further investigation of CT LVAS 

 as a readily available ventilation imaging tool in disease 

 characterisation, lung health assessment, and surgical and 

 targeted treatment planning. TRIAL REGISTRATION: Australian New 

 Zealand Clinical Trials Registry (ANZCTR) registration number 

 ACTRN12612000775819, registered on 23/07/2012.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Byrne2025-eh,

title = {More accessible functional lung imaging: non-contrast 

 CT-ventilation demonstrates strong association and agreement 

 with PET-ventilation},

author = {Hilary L Byrne and Nina Eikelis and Jonathan Dusting and Andreas Fouras and Paul J Keall and Piraveen Pirakalathanan},

year  = {2025},

date = {2025-04-01},

journal = {Respir. Res.},

volume = {26},

number = {1},

pages = {163},

publisher = {Springer Science and Business Media LLC},

abstract = {BACKGROUND: Computed Tomography (CT) ventilation imaging (CTVI) 

 is an emerging ventilation imaging technique. CTVI 

 implementations have been widely validated against alternative 

 ventilation imaging techniques but have been limited to clinical 

 research only. The first CTVI commercial product, CT LVAS 

 (4DMedical, Melbourne, Australia), was recently released 

 enabling its use in clinical practice. This study quantitatively 

 compares ventilation images from CT LVAS and previously 

 validated research CTVI algorithms to Galligas PET ventilation. 

 METHODS: 16 patients with Galligas PET and paired inhale/exhale 

 breath-hold CT images were taken from a publicly available 

 dataset on The Cancer Imaging Archive. Ventilation images were 

 produced using CT LVAS and two previously published algorithms: 

 (1) utilising the Hounsfield Unit difference (CTVI_HU); and (2) 

 utilising the Jacobian determinant (CTVI_Jac). CTVI images were 

 compared to the reference standard Galligas PET using 

 Bland-Altman analysis of lobar ventilation, voxel-wise Spearman 

 correlation, and Dice similarity coefficient (DSC) of regions of 

 interest representing the top 85% and 15% of ventilation 

 function. RESULTS: Bland-Altman analysis showed overall bias of 

 < 0.01% for all CTVI methods (95% confidence interval: 

 $±$7.4% for CT LVAS, $±$ 9.1% for CTVI_HU, $±$ 7.9% 

 for CTVI_Jac). The mean Spearman correlation between CTVI and 

 Galligas PET was 0.61 $±$ 0.14 (p < 0.01) for CT LVAS, 0.68 

 $±$ 0.10 (p < 0.01) for CTVI_HU, and 0.57 $±$ 0.15 (p < 

 0.01) for CTVI_Jac. The mean DSC for the top 85% was 0.91 

 $±$ 0.03 for CT LVAS, 0.92 $±$ 0.02 for CTVI_HU, and 0.91 

 $±$ 0.03 for CTVI_Jac, with the DSC for CTVI_HU 

 significantly higher than the other two CTVI methods. The DSC 

 for the top 15% was 0.47 $±$ 0.17 for CT LVAS, 0.53 $±$ 

 0.16 for CTVI_HU, and 0.47 $±$ 0.18 for CTVI_Jac. 

 CONCLUSIONS: In a comparison to Galligas PET ventilation 

 imaging, CT LVAS performs similarly to previous CTVI methods. 

 Bland-Altman analysis for quantification of lobar ventilation 

 demonstrates negligible bias. Mean voxel-wise Spearman 

 correlations are moderate to good. DSC of functionally 

 thresholded lung regions are similar for all CTVI methods. These 

 results warrant further investigation of CT LVAS as a readily 

 available ventilation imaging tool in disease characterisation, 

 lung health assessment, and surgical and targeted treatment 

 planning. TRIAL REGISTRATION: Australian New Zealand Clinical 

 Trials Registry (ANZCTR) registration number 

 ACTRN12612000775819, registered on 23/07/2012.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gardner2025-ps,

title = {Investigation of cardiac substructure automatic segmentation  methods on synthetically generated 4D cone-beam CT images},

author = {Mark Gardner and Robert N Finnegan and Owen Dillon and Vicky Chin and Tess Reynolds and Paul J Keall},

doi = {10.1002/mp.17596},

year  = {2025},

date = {2025-04-01},

urldate = {2025-04-01},

journal = {Med. Phys.},

volume = {52},

number = {4},

pages = {2224–2237},

publisher = {Wiley},

abstract = {BACKGROUND: STereotactic Arrhythmia Radioablation (STAR) is a 

 novel noninvasive method for treating arrythmias in which 

 external beam radiation is directed towards subregions of the 

 heart. Challenges for accurate STAR targeting include small 

 target volumes and relatively large patient motion, which can 

 lead to radiation related patient toxicities. 4D Cone-beam CT 

 (CBCT) images are used for stereotactic lung treatments to 

 account for respiration-related patient motion. 4D-CBCT imaging 

 could similarly be used to account for respiration-related 

 patient motion in STAR; however, the poor contrast of heart 

 tissue in CBCT makes identifying cardiac substructures in 

 4D-CBCT images challenging. If cardiac structures can be 

 identified in pre-treatment 4D-CBCT images, then the location of 

 the target volume can be more accurately identified for 

 different phases of the respiration cycle, leading to more 

 accurate targeting and a reduction in patient toxicities. 

 PURPOSE: The aim of this simulation study is to investigate the 

 accuracy of different cardiac substructure segmentation methods 

 for 4D-CBCT images. METHODS: Repeat 4D-CT scans from 13 lung 

 cancer patients were obtained from The Cancer Imaging Archive. 

 Synthetic 4D-CBCT images for each patient were simulated by 

 forward projecting and reconstructing each respiration phase of 

 a chosen ``testing'' 4D-CT scan. Eighteen cardiac structures 

 were segmented from each respiration phase image in the testing 

 4D-CT using the previously validated platipy toolkit. The 

 platipy segmentations from the testing 4D-CT were defined as the 

 ground truth segmentations for the synthetic 4D-CBCT images. 

 Five different 4D-CBCT cardiac segmentation methods were 

 investigated: 3D Rigid Alignment, 4D Rigid Alignment, Direct 

 CBCT Segmentation, Contour Transformation, and Synthetic CT 

 Segmentation methods. For all methods except the Direct CBCT 

 segmentation method, a separate 4D-CT (Planning CT) was used to 

 assist in generating 4D-CBCT segmentations. Segmentation 

 performance was measured using the Dice similarity coefficient 

 (DSC), Hausdorff distance (HD), mean surface distance (MSD), and 

 volume ratio (VR) metrics. RESULTS: The mean $±$ standard 

 deviation DSC for all cardiac substructures for the 3D Rigid 

 Alignment, 4D Rigid Alignment, Direct CBCT Segmentation, Contour 

 Transformation, and Synthetic CT Segmentation methods were 0.48 

 $±$ 0.29, 0.52 $±$ 0.29, 0.37 $±$ 0.32, 0.53 $±$ 0.29, 

 0.57 $±$ 0.28, respectively. Similarly, the HD values were 

 10.9 $±$ 3.6 , 9.9 $±$ 2.6 , 17.3 $±$ 5.3 , 9.9 $±$ 2.8 

 , 9.3 $±$ 3.0 mm, the MSD values were 2.9 $±$ 0.6 , 2.9 

 $±$ 0.6 , 6.3 $±$ 2.5 , 2.5 $±$ 0.6 , 2.4 $±$ 0.8 mm, 

 and the VR Values were 0.81 $±$ 0.12, 0.78 $±$ 0.14, 1.10 

 $±$ 0.47, 0.72 $±$ 0.15, 0.98 $±$ 0.44, respectively. Of 

 the five methods investigated the Synthetic CT segmentation 

 method generated the most accurate segmentations for all 

 calculated segmentation metrics. CONCLUSION: This simulation 

 study investigates the accuracy of different cardiac 

 substructure segmentation methods for 4D-CBCT images. Accurate 

 4D-CBCT cardiac segmentation will provide more accurate 

 information on the location of cardiac anatomy during STAR 

 treatments which can lead to safer and more effective STAR. As 

 the data and segmentation methods used in this study are all 

 open source, this study provides a useful benchmarking tool to 

 evaluate other CBCT cardiac segmentation methods.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gardner2024b,

title = {Investigation of cardiac substructure automatic segmentation methods on synthetically generated 4D cone‐beam CT images},

author = {Mark Gardner and Robert N. Finnegan and Owen Dillon and Vicky Chin and Tess Reynolds and Paul J. Keall},

doi = {10.1002/mp.17596},

issn = {2473-4209},

year  = {2025},

date = {2025-04-00},

journal = {Medical Physics},

volume = {52},

number = {4},

pages = {2224--2237},

publisher = {Wiley},

abstract = {AbstractBackgroundSTereotactic Arrhythmia Radioablation (STAR) is a novel noninvasive method for treating arrythmias in which external beam radiation is directed towards subregions of the heart. Challenges for accurate STAR targeting include small target volumes and relatively large patient motion, which can lead to radiation related patient toxicities. 4D Cone‐beam CT (CBCT) images are used for stereotactic lung treatments to account for respiration‐related patient motion. 4D‐CBCT imaging could similarly be used to account for respiration‐related patient motion in STAR; however, the poor contrast of heart tissue in CBCT makes identifying cardiac substructures in 4D‐CBCT images challenging. If cardiac structures can be identified in pre‐treatment 4D‐CBCT images, then the location of the target volume can be more accurately identified for different phases of the respiration cycle, leading to more accurate targeting and a reduction in patient toxicities.PurposeThe aim of this simulation study is to investigate the accuracy of different cardiac substructure segmentation methods for 4D‐CBCT images.MethodsRepeat 4D‐CT scans from 13 lung cancer patients were obtained from The Cancer Imaging Archive. Synthetic 4D‐CBCT images for each patient were simulated by forward projecting and reconstructing each respiration phase of a chosen “testing” 4D‐CT scan. Eighteen cardiac structures were segmented from each respiration phase image in the testing 4D‐CT using the previously validated platipy toolkit. The platipy segmentations from the testing 4D‐CT were defined as the ground truth segmentations for the synthetic 4D‐CBCT images. Five different 4D‐CBCT cardiac segmentation methods were investigated: 3D Rigid Alignment, 4D Rigid Alignment, Direct CBCT Segmentation, Contour Transformation, and Synthetic CT Segmentation methods. For all methods except the Direct CBCT segmentation method, a separate 4D‐CT (Planning CT) was used to assist in generating 4D‐CBCT segmentations. Segmentation performance was measured using the Dice similarity coefficient (DSC), Hausdorff distance (HD), mean surface distance (MSD), and volume ratio (VR) metrics.ResultsThe mean ± standard deviation DSC for all cardiac substructures for the 3D Rigid Alignment, 4D Rigid Alignment, Direct CBCT Segmentation, Contour Transformation, and Synthetic CT Segmentation methods were 0.48 ± 0.29, 0.52 ± 0.29, 0.37 ± 0.32, 0.53 ± 0.29, 0.57 ± 0.28, respectively. Similarly, the HD values were 10.9 ± 3.6 , 9.9 ± 2.6 , 17.3 ± 5.3 , 9.9 ± 2.8 , 9.3 ± 3.0 mm, the MSD values were 2.9 ± 0.6 , 2.9 ± 0.6 , 6.3 ± 2.5 , 2.5 ± 0.6 , 2.4 ± 0.8 mm, and the VR Values were 0.81 ± 0.12, 0.78 ± 0.14, 1.10 ± 0.47, 0.72 ± 0.15, 0.98 ± 0.44, respectively. Of the five methods investigated the Synthetic CT segmentation method generated the most accurate segmentations for all calculated segmentation metrics.ConclusionThis simulation study investigates the accuracy of different cardiac substructure segmentation methods for 4D‐CBCT images. Accurate 4D‐CBCT cardiac segmentation will provide more accurate information on the location of cardiac anatomy during STAR treatments which can lead to safer and more effective STAR. As the data and segmentation methods used in this study are all open source, this study provides a useful benchmarking tool to evaluate other CBCT cardiac segmentation methods.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hindmarsh2025,

title = {A dosimetric comparison of helical tomotherapy treatment delivery with real-time adaption and no motion correction},

author = {Jonathan Hindmarsh and Scott Crowe and Julia Johnson and Chandrima Sengupta and Jemma Walsh and Sonja Dieterich and Jeremy Booth and Paul Keall},

doi = {10.1016/j.phro.2025.100741},

issn = {2405-6316},

year  = {2025},

date = {2025-04-00},

journal = {Physics and Imaging in Radiation Oncology},

volume = {34},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sengupta2025-ek,

title = {The TROG 15.01 stereotactic prostate adaptive radiotherapy 

 utilizing kilovoltage intrafraction monitoring (SPARK) 

 clinical trial database},

author = {Chandrima Sengupta and Doan Trang Nguyen and Yifan Li and Emily Hewson and Helen Ball and Ricky O'Brien and Jeremy Booth and John Kipritidis and Thomas Eade and Andrew Kneebone and George Hruby and Regina Bromley and Peter Greer and Jarad Martin and Perry Hunter and Lee Wilton and Trevor Moodie and Amy Hayden and Sandra Turner and Nicholas Hardcastle and Shankar Siva and Keen-Hun Tai and Sankar Arumugam and Mark Sidhom and Per Poulsen and Val Gebski and Alisha Moore and Paul Keall},

year  = {2025},

date = {2025-03-01},

journal = {Med. Phys.},

volume = {52},

number = {3},

pages = {1941–1949},

publisher = {Wiley},

abstract = {PURPOSE: The US National Institutes of Health state that Sharing 

 of clinical trial data has great potential to accelerate 

 scientific progress and ultimately improve public health by 

 generating better evidence on the safety and effectiveness of 

 therapies for patients 

 (https://www.ncbi.nlm.nih.gov/books/NBK285999/ accessed 

 2024-01-24.). Aligned with this initiative, the Trial Management 

 Committee of the Trans-Tasman Radiation Oncology Group (TROG) 

 15.01 Stereotactic Prostate Adaptive Radiotherapy utilizing 

 Kilovoltage intrafraction monitoring (KIM) (SPARK) clinical 

 trial supported the public sharing of the clinical trial data. 

 ACQUISITION AND VALIDATION METHODS: The data originate from the 

 TROG 15.01 SPARK clinical trial. The SPARK trial was a phase II 

 prospective multi-institutional clinical trial (NCT02397317). 

 The aim of the SPARK clinical trial was to measure the geometric 

 and dosimetric cancer targeting accuracy achieved with a 

 real-time image-guided radiotherapy technology named KIM for 48 

 prostate cancer patients treated in 5 treatment sessions. During 

 treatment, real-time tumor translational and rotational motion 

 were determined from x-ray images using the KIM technology. A 

 dose reconstruction method was used to evaluate the dose 

 delivered to the target and organs-at-risk. Patient-reported 

 outcomes and toxicity data were monitored up to 2 years after 

 the completion of the treatment. DATA FORMAT AND USAGE NOTES: 

 The dataset contains planning CT images, treatment plans, 

 structure sets, planned and motion-included dose-volume 

 histograms, intrafraction kilovoltage, and megavoltage 

 projection images, tumor translational and rotational motion 

 determined by KIM, tumor motion ground truth data, the linear 

 accelerator trajectory traces, and patient treatment outcomes. 

 The dataset is publicly hosted by the University of Sydney 

 eScholarship Repository at https://doi.org/10.25910/qg5d-6058. 

 POTENTIAL APPLICATIONS: The 3.6 Tb dataset, with approximately 1 

 million patient images, could be used for a variety of 

 applications, including the development of real-time 

 image-guided methods, adaptation strategies, tumor, and normal 

 tissue control modeling, and prostate-specific antigen kinetics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lassen2025-uh,

title = {Positron range correction helps enhance the image quality of 

 cardiac 82Rb PET/CT},

author = {Martin Lyngby Lassen and Hunor Kertész and Ivo Rausch and Vladimir Panin and Maurizio Conti and Sven Zuehlsdorff and Jorge Cabello and Deepak Bharkhada and Robert DeKemp and Andreas Kjaer and Thomas Beyer and Philip Hasbak},

year  = {2025},

date = {2025-03-01},

journal = {J. Nucl. Med.},

volume = {66},

number = {3},

pages = {466–472},

publisher = {Society of Nuclear Medicine},

abstract = {The image quality and quantitative accuracy of 82Rb myocardial 

 perfusion imaging (MPI) using PET is challenged by the extensive 

 positron range (PR) effects, with the PR of 82Rb being about 7 

 mm in soft tissues. This study explored the feasibility of 

 applying postacquisition PR correction (PRC) to routine 82Rb 

 PET/CT MPI acquisitions and assessed its impact on diagnostic 

 accuracy and image quality. Methods: We implemented a PRC method 

 adjusted to 82Rb into a vendor-provided reconstruction toolbox, 

 using tissue-specific corrections for soft tissue, bone, and 

 air/lungs. The PRC was evaluated in 2 cohorts: the first 

 comprised 25 healthy volunteers who underwent repeated 82Rb MPI 

 within 2 wk, and the second included 66 patients with known or 

 suspected coronary artery disease. We measured the 

 signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) 

 for the volunteer cohort. In the patient cohort, the impact of 

 PRC was evaluated as changes in the area under the receiver 

 operating characteristic curve (AUC), using fractional flow 

 reserve as the gold standard (values < 80% were considered 

 significantly reduced). We calculated AUCs for stress and 

 ischemic total perfusion deficits. Results: In the volunteer 

 cohort, PRC-based reconstructions (standard reconstruction [STD] 

 + PRC) demonstrated significantly improved SNR and CNR compared 

 with STD, with median increases of 22% and 47% for SNR and 

 CNR, respectively (P < 0.05). For the patient cohort, comparable 

 AUCs were reported for STD- versus PRC-based reconstructions (stress total perfusion deficits, 0.84 vs. 0.83 [P = 0.49]; ischemic total perfusion deficits, 0.87 vs. 0.87 [P = 0.80]). 

 Conclusion: PRC significantly enhances SNR and CNR compared with 

 STD without affecting the diagnostic accuracy of the scans. 

 Given the significantly improved image quality, PRC may be 

 recommended for MPI using 82Rb PET/CT 

 clinical-routine-assessment interpretation of TPD.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Dillon2025-mb,

title = {Real-time spatiotemporal optimization during imaging},

author = {Owen Dillon and Benjamin Lau and Shalini K Vinod and Paul J Keall and Tess Reynolds and Jan-Jakob Sonke and Ricky T O'Brien},

year  = {2025},

date = {2025-03-01},

journal = {Commun Eng},

volume = {4},

number = {1},

pages = {61},

publisher = {Springer Science and Business Media LLC},

abstract = {High quality imaging is required for high quality medical care, 

 especially in precision applications such as radiation therapy. 

 Patient motion during image acquisition reduces image quality 

 and is either accepted or dealt with retrospectively during 

 image reconstruction. Here we formalize a general approach in 

 which data acquisition is treated as a spatiotemporal 

 optimization problem to solve in real time so that the acquired 

 data has a specific structure that can be exploited during 

 reconstruction. We provide results of the first-in-world 

 clinical trial implementation of our spatiotemporal optimization 

 approach, applied to respiratory correlated 4D cone beam 

 computed tomography for lung cancer radiation therapy 

 (NCT04070586, ethics approval 2019/ETH09968). Performing 

 spatiotemporal optimization allowed us to maintain or improve 

 image quality relative to the current clinical standard while 

 reducing scan time by 63% and reducing scan radiation by 85%, 

 improving clinical throughput and reducing the risk of secondary 

 tumors. This result motivates application of the general 

 spatiotemporal optimization approach to other types of patient 

 motion such as cardiac signals and other modalities such as CT 

 and MRI.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sengupta2024,

title = {The TROG 15.01 stereotactic prostate adaptive radiotherapy utilizing kilovoltage intrafraction monitoring (SPARK) clinical trial database},

author = {Chandrima Sengupta and Doan Trang Nguyen and Yifan Li and Emily Hewson and Helen Ball and Ricky O'Brien and Jeremy Booth and John Kipritidis and Thomas Eade and Andrew Kneebone and George Hruby and Regina Bromley and Peter Greer and Jarad Martin and Perry Hunter and Lee Wilton and Trevor Moodie and Amy Hayden and Sandra Turner and Nicholas Hardcastle and Shankar Siva and Keen‐Hun Tai and Sankar Arumugam and Mark Sidhom and Per Poulsen and Val Gebski and Alisha Moore and Paul Keall},

doi = {10.1002/mp.17529},

issn = {2473-4209},

year  = {2025},

date = {2025-03-00},

journal = {Medical Physics},

volume = {52},

number = {3},

pages = {1941--1949},

publisher = {Wiley},

abstract = {AbstractPurposeThe US National Institutes of Health state that Sharing of clinical trial data has great potential to accelerate scientific progress and ultimately improve public health by generating better evidence on the safety and effectiveness of therapies for patients (https://www.ncbi.nlm.nih.gov/books/NBK285999/ accessed 2024‐01‐24.). Aligned with this initiative, the Trial Management Committee of the Trans‐Tasman Radiation Oncology Group (TROG) 15.01 Stereotactic Prostate Adaptive Radiotherapy utilizing Kilovoltage intrafraction monitoring (KIM) (SPARK) clinical trial supported the public sharing of the clinical trial data.Acquisition and Validation MethodsThe data originate from the TROG 15.01 SPARK clinical trial. The SPARK trial was a phase II prospective multi‐institutional clinical trial (NCT02397317). The aim of the SPARK clinical trial was to measure the geometric and dosimetric cancer targeting accuracy achieved with a real‐time image‐guided radiotherapy technology named KIM for 48 prostate cancer patients treated in 5 treatment sessions. During treatment, real‐time tumor translational and rotational motion were determined from x‐ray images using the KIM technology. A dose reconstruction method was used to evaluate the dose delivered to the target and organs‐at‐risk. Patient‐reported outcomes and toxicity data were monitored up to 2 years after the completion of the treatment.Data Format and Usage NotesThe dataset contains planning CT images, treatment plans, structure sets, planned and motion‐included dose‐volume histograms, intrafraction kilovoltage, and megavoltage projection images, tumor translational and rotational motion determined by KIM, tumor motion ground truth data, the linear accelerator trajectory traces, and patient treatment outcomes. The dataset is publicly hosted by the University of Sydney eScholarship Repository at https://doi.org/10.25910/qg5d‐6058.Potential ApplicationsThe 3.6 Tb dataset, with approximately 1 million patient images, could be used for a variety of applications, including the development of real‐time image‐guided methods, adaptation strategies, tumor, and normal tissue control modeling, and prostate‐specific antigen kinetics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lassen2025,

title = {Positron Range Correction Helps Enhance the Image Quality of Cardiac^{82}Rb PET/CT},

author = {Martin Lyngby Lassen and Hunor Kertész and Ivo Rausch and Vladimir Panin and Maurizio Conti and Sven Zuehlsdorff and Jorge Cabello and Deepak Bharkhada and Robert DeKemp and Andreas Kjaer and Thomas Beyer and Philip Hasbak},

doi = {10.2967/jnumed.124.267855},

issn = {2159-662X},

year  = {2025},

date = {2025-03-00},

journal = {J Nucl Med},

volume = {66},

number = {3},

pages = {466--472},

publisher = {Society of Nuclear Medicine},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Wilding-McBride2025-sc,

title = {CT ventilation images produced by a 3D neural network show 

 improvement over the Jacobian and HU DIR-based methods to 

 predict quantized lung function},

author = {Daryl Wilding-McBride and Jeremy Lim and Hilary Byrne and Ricky O'Brien},

year  = {2025},

date = {2025-02-01},

journal = {Med. Phys.},

volume = {52},

number = {2},

pages = {889–898},

publisher = {Wiley},

abstract = {BACKGROUND: Radiation-induced pneumonitis affects up to 33% of 

 non-small cell lung cancer (NSCLC) patients, with fatal 

 pneumonitis occurring in 2% of patients. Pneumonitis risk is 

 related to the dose and volume of lung irradiated. Clinical 

 radiotherapy plans assume lungs are functionally homogeneous, 

 but evidence suggests that avoidance of high-functioning lung 

 during radiotherapy can reduce the risk of radiation-induced 

 pneumonitis. Radiotherapy avoidance structures can be 

 constructed based on high-function regions indicated in a 

 ventilation map, which can be produced from CT images. PURPOSE: 

 Existing methods of deriving such a CT ventilation image (CTVI) 

 require the use of deformable image registration (DIR) of 

 peak-inhale and -exhale CT images, which is susceptible to 

 inaccuracy for small or low-intensity regions, and sensitive to 

 image artefacts. To overcome these problems, we use a neural 

 network to predict a ventilation map from breath-hold CT (BHCT). 

 METHODS: We used the nnU-Net pipeline to train five-fold 

 cross-validated ensemble models to predict a ventilation map 

 (CTVInnU-Net). The training data were comprised of registered 

 BHCT and Galligas PET images from 20 patients. Three training 

 sets were created to ensure performance was averaged over 

 different test patients. For each set, images from two randomly 

 selected test patients were set aside, and models were trained 

 on the remaining images. The ground truth was established by 

 quantizing the Galligas PET images, assigning each voxel a label 

 of high-function (>70th percentile of intensity), 

 medium-function (between 30th and 70th percentile), or 

 low-function (<30th percentile). For comparison, we created a 

 CTVI with a 2D U-Net (CTVInnU-Net-2D), and with the Jacobian 

 (CTVIJac) and Hounsfield Units (CTVIHU) DIR-based methods which 

 we quantized and labeled in the same way. The Dice similarity 

 coefficient (DSC) and Hausdorff Distance 95th percentile (HD95) 

 of each CTVI with the ground truth were measured separately for 

 each lung function subregion. RESULTS: CTVInnU-Net had the 

 highest similarity to the quantized Galligas PET with a mean 

 (range) DSC over all three categories of lung function at 0.68 

 (0.56 to 0.82), compared with 0.64 (0.47 to 0.75) for 

 CTVInnU-Net-2D, 0.60 (0.38 to 0.73) for CTVIJac, and 0.56 (0.30 

 to 0.75) for CTVIHU. CTVInnU-Net had the equal-lowest spatial 

 distance to the quantized Galligas PET averaged over the three 

 categories, with HD95 of 22 mm (9 to 64 mm), compared with 23 mm 

 (9 to 72 mm) for CTVInnU-Net-2D, 22 mm (12 to 63 mm) for 

 CTVIJac, and 26 mm (12 to 58 mm) for CTVIHU. CONCLUSION: Our 3D 

 neural network produces a quantized CTVI with higher similarity 

 to the ground truth than the 2D U-Net and DIR-based Jacobian and 

 HU methods. As it produces a quantized CTVI directly, 

 CTVInnU-Net avoids the need for thresholding to identify 

 high-function lung regions. With faster evaluation and improved 

 accuracy, neural networks show promise for the clinical 

 implementation of functional lung avoidance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hood2025-va,

title = {First linac-mounted photon counting detector for image guided 

 radiotherapy: Planar image quality characterization},

author = {Sean Hood and Matthew Newall and Phil Butler and Ricky O'Brien and Marco Petasecca and Owen Dillon and Anatoly Rosenfeld and Nicholas Hardcastle and Michael Jackson and Peter Metcalfe and Saree Alnaghy},

year  = {2025},

date = {2025-02-01},

journal = {Med. Phys.},

volume = {52},

number = {2},

pages = {1159–1171},

publisher = {Wiley},

abstract = {BACKGROUND: Image guided radiotherapy (IGRT) with cone-beam 

 computed tomography (CBCT) is limited by the sub-optimal 

 soft-tissue contrast and spatial resolution of 

 energy-integrating flat panel detectors (FPDs) which produce 

 quasi-quantitative CT numbers. Spectral CT with high resolution 

 photon-counting detectors (PCDs) could improve tumor delineation 

 by enhancing the soft-tissue contrast, spatial resolution, 

 dose-efficiency, and CT number accuracy. PURPOSE: This study 

 presents the first linac-mounted PCD. On the journey to 

 developing spectral cone-beam CT for IGRT, the planar image 

 quality of a linac-mounted PCD is first fundamentally 

 characterized and compared to an FPD in terms of the 2D spatial 

 resolution, noise, and contrast. METHODS: A Medipix3RX-based PCD 

 was mounted to the kV FPD of an x-ray volume imaging (XVI) 

 system on an Elekta linac and the PCD acquisition was 

 synchronized with the pulsed kV source. The energy calibration 

 of the Medipix3RX was determined with various radioisotope gamma 

 emissions up to 60 keV. To compare the 2D spatial resolution and 

 noise between the PCD and FPD, the pre-sampling modulation 

 transfer function (MTF) and normalized noise power spectrum 

 (NPS) were measured using an RQA5 spectrum and a fluoroscopy 

 phantom was imaged to determine the limiting resolution of line 

 pairs. Spectral planar images of phantom inserts containing two 

 different concentrations of calcium (60 and 240 mg/cc) and 

 iodine (5 and 15 mg/cc) were optimally energy weighted to 

 maximize the contrast using tube voltages of 60, 80, 100, and 

 120 kV. To account for drifts in the sensor temperature, the PCD 

 was dynamically translated in and out of the insert shadow 

 during acquisitions to obtain flat field corrections per frame. 

 The raw contrast of the resultant planar images was compared to 

 the energy-integrating FPD. RESULTS: The energy calibration of 

 the Medipix3RX was observed to be linear up to 60 keV. The 

 limiting resolution observed on the fluoroscopy phantom was 2 

 lp/mm for the FPD and 5 lp/mm for the PCD. The pre-sampling MTF 

 was higher across all frequencies comparing the PCD to the FPD. 

 The normalized NPS of the PCD did not vary with frequency, 

 whereas the spectrum for the FPD decreased monotonically and was 

 lower than the PCD noise power across most of the spatial 

 frequency range studied due to optical light spreading. Optimal 

 energy weights were applied to the dynamically acquired PCD 

 images and the raw contrast of the 60 mg/cc calcium insert 

 increased by factors of 1.12 $±$ 0.09 $1.12± 0.09$ and 1.52 

 $±$ 0.22 $1.52± 0.22$ at 60 and 120 kV respectively compared 

 to the FPD. CONCLUSIONS: A Medipix3RX-based PCD was successfully 

 integrated with the kilovoltage imaging system on an Elekta 

 linac. The initial planar image quality characterization 

 indicated improvements in the MTF and energy-weighted contrast 

 compared to the FPD. Future work will focus on obtaining 

 linac-mounted spectral CBCT images with a translate-rotate 

 geometry, however this initial study indicates that variations 

 in the PCD sensor response during acquisitions must be addressed 

 to realise the full potential of linac-mounted spectral CBCT.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hood2024b,

title = {First linac‐mounted photon counting detector for image guided radiotherapy: Planar image quality characterization},

author = {Sean Hood and Matthew Newall and Phil Butler and Ricky O'Brien and Marco Petasecca and Owen Dillon and Anatoly Rosenfeld and Nicholas Hardcastle and Michael Jackson and Peter Metcalfe and Saree Alnaghy},

doi = {10.1002/mp.17540},

issn = {2473-4209},

year  = {2025},

date = {2025-02-00},

journal = {Medical Physics},

volume = {52},

number = {2},

pages = {1159--1171},

publisher = {Wiley},

abstract = {AbstractBackgroundImage guided radiotherapy (IGRT) with cone‐beam computed tomography (CBCT) is limited by the sub‐optimal soft‐tissue contrast and spatial resolution of energy‐integrating flat panel detectors (FPDs) which produce quasi‐quantitative CT numbers. Spectral CT with high resolution photon‐counting detectors (PCDs) could improve tumor delineation by enhancing the soft‐tissue contrast, spatial resolution, dose‐efficiency, and CT number accuracy.PurposeThis study presents the first linac‐mounted PCD. On the journey to developing spectral cone‐beam CT for IGRT, the planar image quality of a linac‐mounted PCD is first fundamentally characterized and compared to an FPD in terms of the 2D spatial resolution, noise, and contrast.MethodsA Medipix3RX‐based PCD was mounted to the kV FPD of an x‐ray volume imaging (XVI) system on an Elekta linac and the PCD acquisition was synchronized with the pulsed kV source. The energy calibration of the Medipix3RX was determined with various radioisotope gamma emissions up to 60 keV. To compare the 2D spatial resolution and noise between the PCD and FPD, the pre‐sampling modulation transfer function (MTF) and normalized noise power spectrum (NPS) were measured using an RQA5 spectrum and a fluoroscopy phantom was imaged to determine the limiting resolution of line pairs. Spectral planar images of phantom inserts containing two different concentrations of calcium (60 and 240 mg/cc) and iodine (5 and 15 mg/cc) were optimally energy weighted to maximize the contrast using tube voltages of 60, 80, 100, and 120 kV. To account for drifts in the sensor temperature, the PCD was dynamically translated in and out of the insert shadow during acquisitions to obtain flat field corrections per frame. The raw contrast of the resultant planar images was compared to the energy‐integrating FPD.ResultsThe energy calibration of the Medipix3RX was observed to be linear up to 60 keV. The limiting resolution observed on the fluoroscopy phantom was 2 lp/mm for the FPD and 5 lp/mm for the PCD. The pre‐sampling MTF was higher across all frequencies comparing the PCD to the FPD. The normalized NPS of the PCD did not vary with frequency, whereas the spectrum for the FPD decreased monotonically and was lower than the PCD noise power across most of the spatial frequency range studied due to optical light spreading. Optimal energy weights were applied to the dynamically acquired PCD images and the raw contrast of the 60 mg/cc calcium insert increased by factors of  and  at 60 and 120 kV respectively compared to the FPD.ConclusionsA Medipix3RX‐based PCD was successfully integrated with the kilovoltage imaging system on an Elekta linac. The initial planar image quality characterization indicated improvements in the MTF and energy‐weighted contrast compared to the FPD. Future work will focus on obtaining linac‐mounted spectral CBCT images with a translate‐rotate geometry, however this initial study indicates that variations in the PCD sensor response during acquisitions must be addressed to realise the full potential of linac‐mounted spectral CBCT.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Wilding‐McBride2024,

title = {CT ventilation images produced by a 3D neural network show improvement over the Jacobian and HU DIR‐based methods to predict quantized lung function},

author = {Daryl Wilding‐McBride and Jeremy Lim and Hilary Byrne and Ricky O'Brien},

doi = {10.1002/mp.17532},

issn = {2473-4209},

year  = {2025},

date = {2025-02-00},

journal = {Medical Physics},

volume = {52},

number = {2},

pages = {889--898},

publisher = {Wiley},

abstract = {AbstractBackgroundRadiation‐induced pneumonitis affects up to 33% of non‐small cell lung cancer (NSCLC) patients, with fatal pneumonitis occurring in 2% of patients. Pneumonitis risk is related to the dose and volume of lung irradiated. Clinical radiotherapy plans assume lungs are functionally homogeneous, but evidence suggests that avoidance of high‐functioning lung during radiotherapy can reduce the risk of radiation‐induced pneumonitis. Radiotherapy avoidance structures can be constructed based on high‐function regions indicated in a ventilation map, which can be produced from CT images.PurposeExisting methods of deriving such a CT ventilation image (CTVI) require the use of deformable image registration (DIR) of peak‐inhale and ‐exhale CT images, which is susceptible to inaccuracy for small or low‐intensity regions, and sensitive to image artefacts. To overcome these problems, we use a neural network to predict a ventilation map from breath‐hold CT (BHCT).MethodsWe used the nnU‐Net pipeline to train five‐fold cross‐validated ensemble models to predict a ventilation map (CTVInnU‐Net). The training data were comprised of registered BHCT and Galligas PET images from 20 patients. Three training sets were created to ensure performance was averaged over different test patients. For each set, images from two randomly selected test patients were set aside, and models were trained on the remaining images. The ground truth was established by quantizing the Galligas PET images, assigning each voxel a label of high‐function (>70th percentile of intensity), medium‐function (between 30th and 70th percentile), or low‐function (<30th percentile). For comparison, we created a CTVI with a 2D U‐Net (CTVInnU‐Net‐2D), and with the Jacobian (CTVIJac) and Hounsfield Units (CTVIHU) DIR‐based methods which we quantized and labeled in the same way. The Dice similarity coefficient (DSC) and Hausdorff Distance 95th percentile (HD95) of each CTVI with the ground truth were measured separately for each lung function subregion.ResultsCTVInnU‐Net had the highest similarity to the quantized Galligas PET with a mean (range) DSC over all three categories of lung function at 0.68 (0.56 to 0.82), compared with 0.64 (0.47 to 0.75) for CTVInnU‐Net‐2D, 0.60 (0.38 to 0.73) for CTVIJac, and 0.56 (0.30 to 0.75) for CTVIHU. CTVInnU‐Net had the equal‐lowest spatial distance to the quantized Galligas PET averaged over the three categories, with HD95 of 22 mm (9 to 64 mm), compared with 23 mm (9 to 72 mm) for CTVInnU‐Net‐2D, 22 mm (12 to 63 mm) for CTVIJac, and 26 mm (12 to 58 mm) for CTVIHU.ConclusionOur 3D neural network produces a quantized CTVI with higher similarity to the ground truth than the 2D U‐Net and DIR‐based Jacobian and HU methods. As it produces a quantized CTVI directly, CTVInnU‐Net avoids the need for thresholding to identify high‐function lung regions. With faster evaluation and improved accuracy, neural networks show promise for the clinical implementation of functional lung avoidance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hood2024,

title = {First linac‐mounted photon counting detector for image guided radiotherapy: Planar image quality characterization},

author = {Sean Hood and Matthew Newall and Phil Butler and Ricky O'Brien and Marco Petasecca and Owen Dillon and Anatoly Rosenfeld and Nicholas Hardcastle and Michael Jackson and Peter Metcalfe and Saree Alnaghy},

doi = {10.1002/mp.17540},

issn = {2473-4209},

year  = {2025},

date = {2025-02-00},

journal = {Medical Physics},

volume = {52},

number = {2},

pages = {1159--1171},

publisher = {Wiley},

abstract = {AbstractBackgroundImage guided radiotherapy (IGRT) with cone‐beam computed tomography (CBCT) is limited by the sub‐optimal soft‐tissue contrast and spatial resolution of energy‐integrating flat panel detectors (FPDs) which produce quasi‐quantitative CT numbers. Spectral CT with high resolution photon‐counting detectors (PCDs) could improve tumor delineation by enhancing the soft‐tissue contrast, spatial resolution, dose‐efficiency, and CT number accuracy.PurposeThis study presents the first linac‐mounted PCD. On the journey to developing spectral cone‐beam CT for IGRT, the planar image quality of a linac‐mounted PCD is first fundamentally characterized and compared to an FPD in terms of the 2D spatial resolution, noise, and contrast.MethodsA Medipix3RX‐based PCD was mounted to the kV FPD of an x‐ray volume imaging (XVI) system on an Elekta linac and the PCD acquisition was synchronized with the pulsed kV source. The energy calibration of the Medipix3RX was determined with various radioisotope gamma emissions up to 60 keV. To compare the 2D spatial resolution and noise between the PCD and FPD, the pre‐sampling modulation transfer function (MTF) and normalized noise power spectrum (NPS) were measured using an RQA5 spectrum and a fluoroscopy phantom was imaged to determine the limiting resolution of line pairs. Spectral planar images of phantom inserts containing two different concentrations of calcium (60 and 240 mg/cc) and iodine (5 and 15 mg/cc) were optimally energy weighted to maximize the contrast using tube voltages of 60, 80, 100, and 120 kV. To account for drifts in the sensor temperature, the PCD was dynamically translated in and out of the insert shadow during acquisitions to obtain flat field corrections per frame. The raw contrast of the resultant planar images was compared to the energy‐integrating FPD.ResultsThe energy calibration of the Medipix3RX was observed to be linear up to 60 keV. The limiting resolution observed on the fluoroscopy phantom was 2 lp/mm for the FPD and 5 lp/mm for the PCD. The pre‐sampling MTF was higher across all frequencies comparing the PCD to the FPD. The normalized NPS of the PCD did not vary with frequency, whereas the spectrum for the FPD decreased monotonically and was lower than the PCD noise power across most of the spatial frequency range studied due to optical light spreading. Optimal energy weights were applied to the dynamically acquired PCD images and the raw contrast of the 60 mg/cc calcium insert increased by factors of  and  at 60 and 120 kV respectively compared to the FPD.ConclusionsA Medipix3RX‐based PCD was successfully integrated with the kilovoltage imaging system on an Elekta linac. The initial planar image quality characterization indicated improvements in the MTF and energy‐weighted contrast compared to the FPD. Future work will focus on obtaining linac‐mounted spectral CBCT images with a translate‐rotate geometry, however this initial study indicates that variations in the PCD sensor response during acquisitions must be addressed to realise the full potential of linac‐mounted spectral CBCT.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hewson2025-jj,

title = {Six-degrees-of-freedom pelvic bone monitoring on 2D kV 

 intrafraction images to enable multi-target tracking for locally 

 advanced prostate cancer},

author = {Emily A Hewson and Owen Dillon and Per R Poulsen and Jeremy T Booth and Paul J Keall},

year  = {2025},

date = {2025-01-01},

journal = {Med. Phys.},

volume = {52},

number = {1},

pages = {77–87},

publisher = {Wiley},

abstract = {BACKGROUND: Patients with locally advanced prostate cancer 

 require the prostate and pelvic lymph nodes to be irradiated 

 simultaneously during radiation therapy treatment. However, 

 relative motion between treatment targets decreases dosimetric 

 conformity. Current treatment methods mitigate this error by 

 having large treatment margins and often prioritize the prostate 

 at patient setup at the cost of lymph node coverage. PURPOSE: 

 Treatment accuracy can be improved through real-time 

 multi-target adaptation which requires simultaneous motion 

 monitoring of both the prostate and lymph node targets. This 

 study developed and evaluated an intrafraction pelvic bone 

 motion monitoring method as a surrogate for pelvic lymph node 

 displacement to be combined with prostate motion monitoring to 

 enable multi-target six-degrees-of-freedom (6DoF) tracking using 

 2D kV projections acquired during treatment. MATERIAL AND 

 METHODS: A method to monitor pelvic bone translation and 

 rotation was developed and retrospectively applied to images 

 from 20 patients treated in the TROG 15.01 Stereotactic Prostate 

 Ablative Radiotherapy with Kilovoltage Intrafraction Monitoring 

 (KIM) trial. The pelvic motion monitoring method performed 

 template matching to calculate the 6DoF position of the pelvis 

 from 2D kV images. The method first generated a library of 

 digitally reconstructed radiographs (DRRs) for a range of 

 imaging angles and pelvic rotations. The normalized 2D 

 cross-correlations were then calculated for each incoming kV 

 image and a subset of DRRs and the DRR with the maximum 

 correlation coefficient was used to estimate the pelvis 

 translation and rotation. Translation of the pelvis in the 

 unresolved direction was calculated using a 3D Gaussian 

 probability estimation method. Prostate motion was measured 

 using the KIM marker tracking method. The pelvic motion 

 monitoring method was compared to the ground truth obtained from 

 a 6DoF rigid registration of the CBCT and CT. RESULTS: The 

 geometric errors of the pelvic motion monitoring method 

 demonstrated sub-mm and sub-degree accuracy and precision in the 

 translational directions ( T LR 

 $T_mathrmLR$ , T SI 

 $T_mathrmSI$ , T AP 

 $T_mathrmAP$ ) and rotational directions ( 

 R LR $R_mathrmLR$ , R SI 

 $R_mathrmSI$ , R AP 

 $R_mathrmAP$ ). The 3D relative 

 displacement between the prostate and pelvic bones exceeded 2, 

 3, 5, and 7 mm for approximately 66%, 44%, 12%, and 7% of 

 the images. CONCLUSIONS: Accurate intrafraction pelvic bone 

 motion monitoring in 6DoF was demonstrated on 2D kV images, 

 providing a necessary tool for real-time multi-target 

 motion-adapted treatment.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Akwo2025-ew,

title = {Does access to prior mammograms improve the performance of 

 radiographers in interpreting screening mammograms?},

author = {J D Akwo and P D Yun Trieu and M L Barron and T Reynolds and S J Lewis},

year  = {2025},

date = {2025-01-01},

journal = {Radiography (Lond.)},

volume = {31},

number = {1},

pages = {247–253},

publisher = {Elsevier BV},

abstract = {INTRODUCTION: The impact of previous screening mammograms on 

 radiographers' performance in mammography interpretation is 

 unknown. This study assesses the impact that previous screening 

 mammograms has on radiographers' interpretation of mammograms. 

 METHODS: Thirteen Australian radiographers working for the 

 national breast screening service independently interpreted a 

 mammography test-set containing mammograms of 28 women based on 

 the Royal Australian and New Zealand College of Radiologists' 

 classification. Twelve radiographers completed the ``No prior 

 test-set'' (no previous mammograms available) while one 

 radiographer completed the ``Prior test-set'' (most current 

 screening mammograms with access to previous mammograms) in the 

 first reading session. In the second reading session, 12 

 radiographers completed the ``Prior test-set'' and one 

 radiographer completed the ``No prior test-set''. Their 

 performance with and without previous mammograms were calculated 

 and compared. RESULTS: The availability of prior mammograms 

 significantly improved specificity [81(range:58-95) vs. 60(range:37-79); p = 0.002], ROC [91(range:80-99) vs. 82 (range:57-91); p = 0.003], and JAFROC 87(range:73-99) vs. 79 (range:52-91); p = 0.01]. Prior mammograms also significantly reduced false positives (p = 0.002). No differences were 

 observed between readings with and without previous mammograms in terms of sensitivity (p = 0.70) and lesion sensitivity (p = 

 0.82). Years qualified as a radiographer did not modify the 

 influence of previous mammograms on specificity, ROC, and false 

 positives. Years specialised as breast radiographer slightly 

 modified the influence of previous mammograms in radiographers 

 with $geq$25 years of experience but not those with <25 years 

 of experience as breast radiographers. CONCLUSIONS: The 

 availability of previous screening mammograms improves 

 radiographers' ability to discriminate between normal and 

 abnormal mammograms and reduce the false positive rate without 

 affecting the detection of breast cancer. IMPLICATIONS FOR 

 PRACTICE: The findings highlight the need for practices to store 

 screening mammograms and for radiographers to actively refer to 

 previous screening mammograms when interpreting mammograms from 

 the current screening round. It also highlights the need for 

 policies to establish a national accessible mammographic 

 database platform for integrated clinics and to account for 

 population mobility across states.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hewson2024,

title = {Six‐degrees‐of‐freedom pelvic bone monitoring on 2D kV intrafraction images to enable multi‐target tracking for locally advanced prostate cancer},

author = {Emily A Hewson and Owen Dillon and Per R Poulsen and Jeremy T Booth and Paul J Keall},

doi = {10.1002/mp.17465},

issn = {2473-4209},

year  = {2025},

date = {2025-01-00},

journal = {Medical Physics},

volume = {52},

number = {1},

pages = {77--87},

publisher = {Wiley},

abstract = {AbstractBackgroundPatients with locally advanced prostate cancer require the prostate and pelvic lymph nodes to be irradiated simultaneously during radiation therapy treatment. However, relative motion between treatment targets decreases dosimetric conformity. Current treatment methods mitigate this error by having large treatment margins and often prioritize the prostate at patient setup at the cost of lymph node coverage.PurposeTreatment accuracy can be improved through real‐time multi‐target adaptation which requires simultaneous motion monitoring of both the prostate and lymph node targets. This study developed and evaluated an intrafraction pelvic bone motion monitoring method as a surrogate for pelvic lymph node displacement to be combined with prostate motion monitoring to enable multi‐target six‐degrees‐of‐freedom (6DoF) tracking using 2D kV projections acquired during treatment.Material and methodsA method to monitor pelvic bone translation and rotation was developed and retrospectively applied to images from 20 patients treated in the TROG 15.01 Stereotactic Prostate Ablative Radiotherapy with Kilovoltage Intrafraction Monitoring (KIM) trial. The pelvic motion monitoring method performed template matching to calculate the 6DoF position of the pelvis from 2D kV images. The method first generated a library of digitally reconstructed radiographs (DRRs) for a range of imaging angles and pelvic rotations. The normalized 2D cross‐correlations were then calculated for each incoming kV image and a subset of DRRs and the DRR with the maximum correlation coefficient was used to estimate the pelvis translation and rotation. Translation of the pelvis in the unresolved direction was calculated using a 3D Gaussian probability estimation method. Prostate motion was measured using the KIM marker tracking method. The pelvic motion monitoring method was compared to the ground truth obtained from a 6DoF rigid registration of the CBCT and CT.ResultsThe geometric errors of the pelvic motion monitoring method demonstrated sub‐mm and sub‐degree accuracy and precision in the translational directions (, , ) and rotational directions (, , ). The 3D relative displacement between the prostate and pelvic bones exceeded 2, 3, 5, and 7 mm for approximately 66%, 44%, 12%, and 7% of the images.ConclusionsAccurate intrafraction pelvic bone motion monitoring in 6DoF was demonstrated on 2D kV images, providing a necessary tool for real‐time multi‐target motion‐adapted treatment.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Akwo2025,

title = {Does access to prior mammograms improve the performance of radiographers in interpreting screening mammograms?},

author = {J.D. Akwo and P.D. (Yun) Trieu and M.L. Barron and T. Reynolds and S.J. Lewis},

doi = {10.1016/j.radi.2024.11.025},

issn = {1078-8174},

year  = {2025},

date = {2025-01-00},

journal = {Radiography},

volume = {31},

number = {1},

pages = {247--253},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Shen2024,

title = {Enhancing organ and vascular contrast in preclinical ultra-low field MRI using superparamagnetic iron oxide nanoparticles},

author = {Sheng Shen and Neha Koonjoo and Thomas Boele and Jiaqi Lu and David E. J. Waddington and Marie Zhang and Matthew S. Rosen},

doi = {10.1038/s42003-024-06884-1},

issn = {2399-3642},

year  = {2024},

date = {2024-12-00},

journal = {Commun Biol},

volume = {7},

number = {1},

publisher = {Springer Science and Business Media LLC},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Reynolds2024b,

title = {Universal non-circular cone beam CT orbits for metal artifact reduction imaging during image-guided procedures},

author = {Tess Reynolds and Yiqun Ma and Andrew Kanawati and Owen Dillon and Kenzie Baer and Grace Gang and Joseph Stayman},

doi = {10.1038/s41598-024-77964-9},

issn = {2045-2322},

year  = {2024},

date = {2024-12-00},

journal = {Sci Rep},

volume = {14},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractInnovation in image-guided procedures has been driven by advances in robotic Cone Beam Computed Tomography (CBCT) systems. A fundamental challenge for CBCT imaging is metal artifacts arising from surgical tools and implanted hardware. Here, we outline how two universal non-circular imaging orbits, optimized for metal artifact reduction, can be implemented in real-time on clinical robotic CBCT systems. Demonstrating potential clinical utility, the universal orbits were implemented during a pedicle screw cervical spine fixation and hip arthroplasty performed on a porcine and ovine cadaver respectively. In both procedures, the universal non-circular orbits noticeably reduced the metal artifacts surrounding the implanted orthopedic hardware, revealing anatomy and soft tissue obscured in current conventional CBCT imaging. This work represents a key step in clinically translating universal orbits, unlocking high quality in-room procedural verification to increase broader use of robotic CBCT systems and reduce the occurrence of secondary corrective surgeries.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Reynolds2024,

title = {Investigating 4D respiratory cone-beam CT imaging for thoracic interventions on robotic C-arm systems: a deformable phantom study},

author = {Tess Reynolds and Owen Dillon and Yiqun Ma and Nicholas Hindley and J. Webster Stayman and Magdalena Bazalova-Carter},

doi = {10.1007/s13246-024-01491-0},

issn = {2662-4737},

year  = {2024},

date = {2024-12-00},

journal = {Phys Eng Sci Med},

volume = {47},

number = {4},

pages = {1751--1762},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractIncreasingly, interventional thoracic workflows utilize cone-beam CT (CBCT) to improve navigational and diagnostic yield. Here, we investigate the feasibility of implementing free-breathing 4D respiratory CBCT for motion mitigated imaging in patients unable to perform a breath-hold or without suspending mechanical ventilation during thoracic interventions. Circular 4D respiratory CBCT imaging trajectories were implemented on a clinical robotic CBCT system using additional real-time control hardware. The circular trajectories consisted of 1 × 360° circle at 0° tilt with fixed gantry velocities of 2°/s, 10°/s, and 20°/s. The imaging target was an in-house developed anthropomorphic breathing thorax phantom with deformable lungs and 3D-printed imaging targets. The phantom was programmed to reproduce 3 patient-measured breathing traces. Following image acquisition, projections were retrospectively binned into ten respiratory phases and reconstructed using filtered back projection, model-based, and iterative motion compensated algorithms. A conventional circular acquisition on the system of the free-breathing phantom was used as comparator. Edge Response Width (ERW) of the imaging target boundaries and Contrast-to-Noise Ratio (CNR) were used for image quality quantification. All acquisitions across all traces considered displayed visual evidence of motion blurring, and this was reflected in the quantitative measurements. Additionally, all the 4D respiratory acquisitions displayed a lower contrast compared to the conventional acquisitions for all three traces considered. Overall, the current implementation of 4D respiratory CBCT explored in this study with various gantry velocities combined with motion compensated algorithms improved image sharpness for the slower gantry rotations considered (2°/s and 10°/s) compared to conventional acquisitions over a variety of patient traces.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Plant2024,

title = {Radio-opaque contrast agents for liver cancer targeting with KIM during radiation therapy (ROCK-RT): an observational feasibility study},

author = {Natalie Plant and Adam Mylonas and Chandrima Sengupta and Doan Trang Nguyen and Shona Silvester and David Pryor and Peter Greer and Yoo Young Lee and Prabhakar Ramachandran and Venkatakrishnan Seshadri and Yuvnik Trada and Richard Khor and Tim Wang and Nicholas Hardcastle and Paul Keall},

doi = {10.1186/s13014-024-02524-4},

issn = {1748-717X},

year  = {2024},

date = {2024-12-00},

journal = {Radiat Oncol},

volume = {19},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {Abstract

                Background

                This observational study aims to establish the feasibility of using x-ray images of radio-opaque chemoembolisation deposits in patients as a method for real-time image-guided radiation therapy of hepatocellular carcinoma.

              

                Methods

                This study will recruit 50 hepatocellular carcinoma patients who have had or will have stereotactic ablative radiation therapy and have had transarterial chemoembolisation with a radio-opaque agent. X-ray and computed tomography images of the patients will be analysed retrospectively. Additionally, a deep learning method for real-time motion tracking will be developed. We hypothesise that: (i) deep learning software can be developed that will successfully track the contrast agent mass on two thirds of cone beam computed tomography (CBCT) projection and intra-treatment images (ii), the mean and standard deviation (mm) difference in the location of the mass between ground truth and deep learning detection are ≤ 2 mm and ≤ 3 mm respectively and (iii) statistical modelling of study data will predict tracking success in 85% of trial participants.

              

                Discussion

                Developing a real-time tracking method will enable increased targeting accuracy, without the need for additional invasive procedures to implant fiducial markers.

              

                Trial registration

                Registered to ClinicalTrials.gov (NCT05169177) 12th October 2021.

              },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Grover2024,

title = {Super-resolution neural networks improve the spatiotemporal resolution of adaptive MRI-guided radiation therapy},

author = {James Grover and Paul Liu and Bin Dong and Shanshan Shan and Brendan Whelan and Paul Keall and David E. J. Waddington},

doi = {10.1038/s43856-024-00489-9},

issn = {2730-664X},

year  = {2024},

date = {2024-12-00},

journal = {Commun Med},

volume = {4},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {Abstract

                Background

                Magnetic resonance imaging (MRI) offers superb non-invasive, soft tissue imaging of the human body. However, extensive data sampling requirements severely restrict the spatiotemporal resolution achievable with MRI. This limits the modality’s utility in real-time guidance applications, particularly for the rapidly growing MRI-guided radiation therapy approach to cancer treatment. Recent advances in artificial intelligence (AI) could reduce the trade-off between the spatial and the temporal resolution of MRI, thus increasing the clinical utility of the imaging modality.

              

                Methods

                We trained deep learning-based super-resolution neural networks to increase the spatial resolution of real-time MRI. We developed a framework to integrate neural networks directly onto a 1.0 T MRI-linac enabling real-time super-resolution imaging. We integrated this framework with the targeting system of the MRI-linac to demonstrate real-time beam adaptation with super-resolution-based imaging. We tested the integrated system using large publicly available datasets, healthy volunteer imaging, phantom imaging, and beam tracking experiments using bicubic interpolation as a baseline comparison.

              

                Results

                Deep learning-based super-resolution increases the spatial resolution of real-time MRI across a variety of experiments, offering measured performance benefits compared to bicubic interpolation. The temporal resolution is not compromised as measured by a real-time adaptation latency experiment. These two effects, an increase in the spatial resolution with a negligible decrease in the temporal resolution, leads to a net increase in the spatiotemporal resolution.

              

                Conclusions

                Deployed super-resolution neural networks can increase the spatiotemporal resolution of real-time MRI. This has applications to domains such as MRI-guided radiation therapy and interventional procedures.

              },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chin2024c,

title = {Overview of cardiac toxicity from radiation therapy},

author = {Vicky Chin and Robert N Finnegan and Paul Keall and James Otton and Geoff P Delaney and Shalini K Vinod},

doi = {10.1111/1754-9485.13757},

issn = {1754-9485},

year  = {2024},

date = {2024-12-00},

journal = {J Med Imag Rad Onc},

volume = {68},

number = {8},

pages = {987--1000},

publisher = {Wiley},

abstract = {AbstractRadiotherapy is an essential part of treatment for many patients with thoracic cancers. However, proximity of the heart to tumour targets can lead to cardiac side effects, with studies demonstrating link between cardiac radiation dose and adverse outcomes. Although reducing cardiac dose can reduce associated risks, most cardiac constraint recommendations in clinical use are generally based on dose to the whole heart, as dose assessment at cardiac substructure levels on individual patients has been limited historically. Furthermore, estimation of an individual's cardiac risk is complex and multifactorial, which includes radiation dose alongside baseline risk factors, and the impact of systemic therapies. This review gives an overview of the epidemiological impact of cancer and cardiac disease, risk factors contributing to radiation‐related cardiotoxicity, the evidence for cardiac side effects and future directions in cardiotoxicity research. A better understanding of the interactions between risk factors, balancing treatment benefit versus toxicity and the ongoing management of cardiac risk is essential for optimal clinical care. The emerging field of cardio‐oncology is thus a multidisciplinary collaborative effort to enable better understanding of cardiac risks and outcomes for better‐informed patient management decisions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Akwo2024,

title = {Access to prior screening mammograms affects the specificity but not sensitivity of radiologists' performance},

author = {J.D. Akwo and P. D. (Yun) Trieu and M.L. Barron and T. Reynolds and S.J. Lewis},

doi = {10.1016/j.crad.2024.09.007},

issn = {0009-9260},

year  = {2024},

date = {2024-12-00},

journal = {Clinical Radiology},

volume = {79},

number = {12},

pages = {e1549--e1556},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Whelan2024,

title = {Open‐source hardware and software for the measurement, characterization, reporting, and correction of geometric distortion in MRI},

author = {Brendan M. Whelan and Paul Z. Y. Liu and Shanshan Shan and David E. J. Waddington and Bin Dong and Michael G. Jameson and Paul J. Keall},

doi = {10.1002/mp.17342},

issn = {2473-4209},

year  = {2024},

date = {2024-11-00},

journal = {Medical Physics},

volume = {51},

number = {11},

pages = {8399--8410},

publisher = {Wiley},

abstract = {AbstractBackgroundGeometric distortion is a serious problem in MRI, particularly in MRI guided therapy. A lack of affordable and adaptable tools in this area limits research progress and harmonized quality assurance.PurposeTo develop and test a suite of open‐source hardware and software tools for the measurement, characterization, reporting, and correction of geometric distortion in MRI.MethodsAn open‐source python library was developed, comprising modules for parametric phantom design, data processing, spherical harmonics, distortion correction, and interactive reporting. The code was used to design and manufacture a distortion phantom consisting of 618 oil filled markers covering a sphere of radius 150 mm. This phantom was imaged on a CT scanner and a novel split‐bore 1.0 T MRI magnet. The CT images provide distortion‐free dataset. These data were used to test all modules of the open‐source software.ResultsAll markers were successfully extracted from all images. The distorted MRI markers were mapped to undistorted CT data using an iterative search approach. Spherical harmonics reconstructed the fitted gradient data to 1.0 ± 0.6% of the input data. High resolution data were reconstructed via spherical harmonics and used to generate an interactive report. Finally, distortion correction on an independent data set reduced distortion inside the DSV from 5.5 ± 3.1 to 1.6 ± 0.8 mm.ConclusionOpen‐source hardware and software for the measurement, characterization, reporting, and correction of geometric distortion in MRI have been developed. The utility of these tools has been demonstrated via their application on a novel 1.0 T split bore magnet.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}