Publications

@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{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{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{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{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}

}

@article{Klucznik2024,

title = {Quantifying dose perturbations in high‐risk prostate radiotherapy due to translational and rotational motion of prostate and pelvic lymph nodes},

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

doi = {10.1002/mp.17366},

issn = {2473-4209},

year  = {2024},

date = {2024-11-00},

journal = {Medical Physics},

volume = {51},

number = {11},

pages = {8423--8433},

publisher = {Wiley},

abstract = {AbstractBackgroundRadiotherapy of the prostate and the pelvic lymph nodes (LN) is a part of the standard of care treatment for high‐risk prostate cancer. The independent translational and rotational (i.e., six‐degrees‐of‐freedom, [6DoF]) motion of the prostate and LN target during and between fractions can perturb the dose distribution. However, no standard dose reconstruction method accounting for differential 6DoF target motion is available.PurposeWe present a framework for monitoring motion‐induced dose perturbations for two independently moving target volumes in 6DoF. The framework was used to determine the dose perturbation for the prostate and the LN target caused by differential 6DoF motion for a cohort of high‐risk prostate cancer patients. As a potential first step toward real‐time dose‐guided high‐risk prostate radiotherapy, we furthermore investigated if the dose reconstruction was fast enough for real‐time application for both targets.MethodsTwenty high‐risk prostate cancer patients were treated with 3‐arc volumetric modulated arc therapy (VMAT). Kilovoltage intrafraction monitoring (KIM) with triggered kilovoltage (kV) images acquired every 3 throughout 7–10 fractions per patient was used for retrospective 6DoF intrafraction prostate motion estimation. The 6DoF interfraction LN motion was determined from a pelvic bone match between the planning CT and a post‐treatment cone beam CT (CBCT). Using the retrospectively extracted motion, real‐time 6DoF motion‐including dose reconstruction was simulated using the in‐house developed software DoseTracker. A data stream with the 6DoF target positions and linac parameters was broadcasted at a 3‐Hz frequency to DoseTracker. In a continuous loop, DoseTracker calculated the target dose increments including the specified motion and, for comparison, without motion. The motion‐induced change in D99.5% for the prostate CTV (ΔD99.5%) and in D98% for the LN CTV (ΔD98%) was calculated using the final cumulative dose of each fraction and averaged over all imaged fractions. The real‐time reconstructed dose distribution of DoseTracker was benchmarked against a clinical treatment planning system (TPS) and it was investigated whether the calculation speed was fast enough to keep up with the incoming data stream.ResultsTranslational motion was largest in cranio‐caudal (CC) direction (prostate: [‐5.9, +8.4] mm; LN: [‐9.9; +11.0] mm) and anterior–posterior (AP) direction (prostate:[‐5.6; +6.9] mm; LN: [‐9.6; +11.0] mm). The pitch was the largest rotation (prostate: [‐22.5; +25.2] deg; LN: [‐3.9; +5.5] deg). The prostate CTV ΔD99.5% was [‐16.2; +2.5]% for single fractions and [‐3.0; +1.7]% when averaged over all imaged fractions. The LN CTV ΔD98% was [‐19.8; +1.2]% for single fractions and [‐3.1; +0.9]% after averaging. Mean (Standard deviation) absolute dose errors in DoseTracker of 107.8% (Std: 1.9%) for the prostate and 105.5% (Std:1.4%) for the LN were corrected during dose reconstruction by automatically calculated normalization factors. It resulted in accurate calculation of the motion‐induced dose errors with relative differences between DoseTracker and TPS dose calculations of ‐0.1% (Std: 0.5%) (prostate CTV ΔD99.5%) and ‐0.2% (Std: 0.5%) (LN CTV ΔD98%). The DoseTracker calculation was fast enough to keep up with the incoming inputs for all but two out of 107 184 dose calculations.ConclusionUsing the developed framework for dose perturbation monitoring, we found that the differential 6DoF target motion caused substantial dose perturbation for individual fractions, which largely averaged out after several fractions. The framework was shown to provide reliable dose calculations and a sufficiently high‐dose reconstruction speed to be applicable in real‐time.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Choi2024,

title = {Dynamic contrast enhanced high field magnetic resonance imaging for canine primary intracranial neoplasia},

author = {Simon Choi and Caterina Brighi and Sam Long},

doi = {10.3389/fvets.2024.1468831},

issn = {2297-1769},

year  = {2024},

date = {2024-10-04},

journal = {Front. Vet. Sci.},

volume = {11},

publisher = {Frontiers Media SA},

abstract = {IntroductionDistinguishing meningiomas from other intracranial neoplasms is clinically relevant as the prognostic and therapeutic implications differ greatly and influence clinical decision making. Dynamic contrast-enhanced MRI (DCE-MRI) is an imaging technique that assists with characterisation of physiologic alterations such as blood flow and tissue vascular permeability. Quantitative pharmacokinetic analysis utilising DCE-MRI has not been studied in canine neuro-oncology.MethodsA retrospective study was performed in canine patients that underwent DCE-MRI with an imaging diagnosis of an intracranial meningioma and surgery for histopathological diagnosis. Kinetic parameters Ktrans and cerebral blood flow were measured and compared to assess whether differences could be identified between meningiomas and other intracranial neoplasms.ResultsSix dogs with meningiomas and 3 dogs with other intracranial neoplasms were included for statistical analysis. Cerebral blood flow values were found to be statistically higher within meningiomas compared to other intracranial neoplasms. Ktrans values were higher within meningiomas than in other types of intracranial tumours, however this difference did not reach statistical significance.DiscussionBased on the results of this study cerebral blood flow measurement can be utilised to differentiate canine intracranial meningiomas from other similar appearing intracranial tumours.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chin2024,

title = {Cardiac Substructure Dose and Survival in Stereotactic Radiotherapy for Lung Cancer: Results of the Multi-Centre SSBROC Trial},

author = {V. Chin and P. Chlap and R. Finnegan and E. Hau and A. Ong and X. Ma and J. Descallar and J. Otton and L. Holloway and G.P. Delaney and S.K. Vinod},

doi = {10.1016/j.clon.2024.07.005},

issn = {0936-6555},

year  = {2024},

date = {2024-10-00},

journal = {Clinical Oncology},

volume = {36},

number = {10},

pages = {642--650},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gardner2024,

title = {Data-driven rapid 4D cone-beam CT reconstruction for new generation linacs},

author = {Mark Gardner and Owen Dillon and Hilary Byrne and Paul Keall and Ricky O’Brien},

doi = {10.1088/1361-6560/ad780a},

issn = {1361-6560},

year  = {2024},

date = {2024-09-21},

journal = {Phys. Med. Biol.},

volume = {69},

number = {18},

publisher = {IOP Publishing},

abstract = {Abstract

               

                  Objective. Newer generation linear accelerators (Linacs) allow 20 s cone-beam CT (CBCT) acquisition which reduces radiation therapy treatment time. However, the current clinical application of these rapid scans is only 3DCBCT. In this paper we propose a novel data-driven rapid 4DCBCT reconstruction method for new generation linacs. Approach. This method relies on estimating the magnitude of the diaphragm motion from an initial 3D reconstruction. This estimated motion is used to linearly approximate a deformation vector field (DVF) for each respiration phase. These DVFs are then used for motion compensated Feldkamp–Davis–Kress (MCFDK) reconstructions. This method, named MCFDK Data Driven (MCFDK-DD), was compared to a MCFDK reconstruction using a prior motion model (MCFDK-Prior), a 3D-FDK reconstruction, and a conventional acquisition (4 mins) conventional reconstruction 4DCBCT (4D-FDK). The data used in this paper were derived from 4DCT volumes from 12 patients from The Cancer Imaging Archives. Image quality was quantified using RMSE of line plots centred on the tumour, tissue interface width (TIW), the mean square error (MSE) and structural similarity index measurement (SSIM). Main Results. The tumour line plots in the Superior-Inferior direction showed reduced RMSE for the MCFDK-DD compared to the 3D-FDK method, indicating the MCFDK-DD method provided a more accurate tumour location. Similarly, the TIW values from the MCFDK-DD reconstructions (median 8.6 mm) were significantly reduced for the MCFDK-DD method compared to the 3D-FDK reconstructions (median 14.8 mm, (p < 0.001). The MCFDK-DD, MCFDK-Prior and 3D-FDK had median MSE values of 

                     

                     

                        

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                   respectively. The corresponding median SSIM values were 0.93, 0.92 and 0.92 respectively indicating the MCFDK-DD had good agreement with the conventional 4D-FDK reconstructions. Significance. These results demonstrate the feasibility of creating accurate data-driven 4DCBCT images for rapid scans on new generation linacs. These findings could lead to increased clinical usage of 4D information on newer generation linacs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Huang2024,

title = {Radiotherapy protocol compliance in routine clinical practice for patients with stages I–III non‐small‐cell lung cancer},

author = {Xiaoshui Huang and Matthew Field and Shalini Vinod and Helen Ball and Vikneswary Batumalai and Paul Keall and Lois Holloway},

doi = {10.1111/1754-9485.13727},

issn = {1754-9485},

year  = {2024},

date = {2024-09-00},

journal = {J Med Imag Rad Onc},

volume = {68},

number = {6},

pages = {729--739},

publisher = {Wiley},

abstract = {AbstractIntroductionDespite the availability of radiotherapy treatment protocols for lung cancer, considerable treatment variation occurs in clinical practice. This study assessed compliance with a radiotherapy protocol for the treatment of patients with stages I–III non‐small‐cell lung cancer (NSCLC) in routine clinical practice and to identify factors that were associated with compliance.MethodsThe Cancer Institute New South Wales eviQ treatment protocol for external beam radiotherapy of stages I–III NSCLC was taken as the reference to measure compliance. All inoperable patients with stages I–III NSCLC and documented ECOG performance status treated with radiotherapy between 2007 and 2019 at two radiotherapy facilities were available for analysis. Protocol compliance rates were calculated. Univariate and multivariate logistic regression models with 23 input factors were used to determine factors significantly associated with compliance. Survival analysis was conducted for both compliant and non‐compliant treatments.ResultsOverall, 656 patients met the inclusion criteria. Protocol compliance was 16%. Alternative dose/fractionation was responsible for 49% of non‐compliant treatments with 30% receiving an alternative curative fractionation. Five of 23 factors (age at the start of radiotherapy, stage group, ECOG performance status, tumour location and alcoholism history) showed significant associations with protocol compliance on multivariate analysis. There was no significant difference in median survival between patients receiving protocol compliant treatment (15.1 months) and non‐compliant treatment (15.6 months).ConclusionAdherence to the eviQ curative radiotherapy protocol for stages I–III NSCLC was low. Alternative dose/fractionation schemes were the main reason for non‐compliance. Protocol compliance was not associated with outcome.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Byrne2024,

title = {Prospective Randomized Trial Comparing 2 Devices for Deep Inspiration Breath Hold Management in Breast Radiation Therapy: Results of the BRAVEHeart Trial},

author = {Hilary L. Byrne and Elisabeth Steiner and Jeremy Booth and Gillian Lamoury and Marita Morgia and Susan Carroll and Kylie Richardson and Leigh Ambrose and Kuldeep Makhija and Cameron Stanton and Benjamin Zwan and Michael Carr and Maegan Stewart and Regina Bromley and John Atyeo and Shona Silvester and Natalie Plant and Paul Keall},

doi = {10.1016/j.adro.2024.101572},

issn = {2452-1094},

year  = {2024},

date = {2024-09-00},

journal = {Advances in Radiation Oncology},

volume = {9},

number = {9},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chin2024b,

title = {Dosimetric Impact of Delineation and Motion Uncertainties on the Heart and Substructures in Lung Cancer Radiotherapy},

author = {V. Chin and R.N. Finnegan and P. Chlap and L. Holloway and D.I. Thwaites and J. Otton and G.P. Delaney and S.K. Vinod},

doi = {10.1016/j.clon.2024.04.002},

issn = {0936-6555},

year  = {2024},

date = {2024-07-00},

journal = {Clinical Oncology},

volume = {36},

number = {7},

pages = {420--429},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hindley2024,

title = {A statistical learning framework for mapping indirect measurements of ergodic systems to emergent properties},

author = {Nicholas Hindley and Stephen J. DeVience and Ella Zhang and Leo L. Cheng and Matthew S. Rosen},

doi = {10.1016/j.jmro.2024.100151},

issn = {2666-4410},

year  = {2024},

date = {2024-06-00},

journal = {Journal of Magnetic Resonance Open},

volume = {19},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Abdel-Wahab2024,

title = {Addressing challenges in low-income and middle-income countries through novel radiotherapy research opportunities},

author = {May Abdel-Wahab and C Norman Coleman and Jesper Grau Eriksen and Peter Lee and Ryan Kraus and Ekaterina Harsdorf and Becky Lee and Adam Dicker and Ezra Hahn and Jai Prakash Agarwal and Pataje G S Prasanna and Michael MacManus and Paul Keall and Nina A Mayr and Barbara Alicja Jereczek-Fossa and Francesco Giammarile and In Ah Kim and Ajay Aggarwal and Grant Lewison and Jiade J Lu and Douglas Guedes de Castro and Feng-Ming (Spring) Kong and Haidy Afifi and Hamish Sharp and Verna Vanderpuye and Tajudeen Olasinde and Fadi Atrash and Luc Goethals and Benjamin W Corn},

doi = {10.1016/s1470-2045(24)00038-x},

issn = {1470-2045},

year  = {2024},

date = {2024-06-00},

journal = {The Lancet Oncology},

volume = {25},

number = {6},

pages = {e270--e280},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Madden2024,

title = {CBCT-DRRs superior to CT-DRRs for target-tracking applications for pancreatic SBRT},

author = {Levi Madden and Abdella Ahmed and Maegan Stewart and Danielle Chrystall and Adam Mylonas and Ryan Brown and Doan Trang Nguyen and Paul Keall and Jeremy Booth},

doi = {10.1088/2057-1976/ad3bb9},

issn = {2057-1976},

year  = {2024},

date = {2024-05-01},

journal = {Biomed. Phys. Eng. Express},

volume = {10},

number = {3},

publisher = {IOP Publishing},

abstract = {Abstract

               

                  Objective. In current radiograph-based intra-fraction markerless target-tracking, digitally reconstructed radiographs (DRRs) from planning CTs (CT-DRRs) are often used to train deep learning models that extract information from the intra-fraction radiographs acquired during treatment. Traditional DRR algorithms were designed for patient alignment (i.e. bone matching) and may not replicate the radiographic image quality of intra-fraction radiographs at treatment. Hypothetically, generating DRRs from pre-treatment Cone-Beam CTs (CBCT-DRRs) with DRR algorithms incorporating physical modelling of on-board-imagers (OBIs) could improve the similarity between intra-fraction radiographs and DRRs by eliminating inter-fraction variation and reducing image-quality mismatches between radiographs and DRRs. In this study, we test the two hypotheses that intra-fraction radiographs are more similar to CBCT-DRRs than CT-DRRs, and that intra-fraction radiographs are more similar to DRRs from algorithms incorporating physical models of OBI components than DRRs from algorithms omitting these models.

               

                  Approach. DRRs were generated from CBCT and CT image sets collected from 20 patients undergoing pancreas stereotactic body radiotherapy. CBCT-DRRs and CT-DRRs were generated replicating the treatment position of patients and the OBI geometry during intra-fraction radiograph acquisition. To investigate whether the modelling of physical OBI components influenced radiograph-DRR similarity, four DRR algorithms were applied for the generation of CBCT-DRRs and CT-DRRs, incorporating and omitting different combinations of OBI component models. The four DRR algorithms were: a traditional DRR algorithm, a DRR algorithm with source-spectrum modelling, a DRR algorithm with source-spectrum and detector modelling, and a DRR algorithm with source-spectrum, detector and patient material modelling. Similarity between radiographs and matched DRRs was quantified using Pearson’s correlation and Czekanowski’s index, calculated on a per-image basis. Distributions of correlations and indexes were compared to test each of the hypotheses. Distribution differences were determined to be statistically significant when Wilcoxon’s signed rank test and the Kolmogorov-Smirnov two sample test returned p ≤ 0.05 for both tests.

               

                  Main results. Intra-fraction radiographs were more similar to CBCT-DRRs than CT-DRRs for both metrics across all algorithms, with all p ≤ 0.007. Source-spectrum modelling improved radiograph-DRR similarity for both metrics, with all p < 10−6. OBI detector modelling and patient material modelling did not influence radiograph-DRR similarity for either metric.

               

                  Significance. Generating DRRs from pre-treatment CBCT-DRRs is feasible, and incorporating CBCT-DRRs into markerless target-tracking methods may promote improved target-tracking accuracies. Incorporating source-spectrum modelling into a treatment planning system’s DRR algorithms may reinforce the safe treatment of cancer patients by aiding in patient alignment.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ma2024,

title = {Fully automatic online geometric calibration for non‐circular cone‐beam CT orbits using fiducials with unknown placement},

author = {Yiqun Q. Ma and Tess Reynolds and Tina Ehtiati and Clifford Weiss and Kelvin Hong and Nicholas Theodore and Grace J. Gang and J. Webster Stayman},

doi = {10.1002/mp.17041},

issn = {2473-4209},

year  = {2024},

date = {2024-05-00},

journal = {Medical Physics},

volume = {51},

number = {5},

pages = {3245--3264},

publisher = {Wiley},

abstract = {AbstractBackgroundCone‐beam CT (CBCT) with non‐circular scanning orbits can improve image quality for 3D intraoperative image guidance. However, geometric calibration of such scans can be challenging. Existing methods typically require a prior image, specialized phantoms, presumed repeatable orbits, or long computation time.PurposeWe propose a novel fully automatic online geometric calibration algorithm that does not require prior knowledge of fiducial configuration. The algorithm is fast, accurate, and can accommodate arbitrary scanning orbits and fiducial configurations.MethodsThe algorithm uses an automatic initialization process to eliminate human intervention in fiducial localization and an iterative refinement process to ensure robustness and accuracy. We provide a detailed explanation and implementation of the proposed algorithm. Physical experiments on a lab test bench and a clinical robotic C‐arm scanner were conducted to evaluate spatial resolution performance and robustness under realistic constraints.ResultsQualitative and quantitative results from the physical experiments demonstrate high accuracy, efficiency, and robustness of the proposed method. The spatial resolution performance matched that of our existing benchmark method, which used a 3D‐2D registration‐based geometric calibration algorithm.ConclusionsWe have demonstrated an automatic online geometric calibration method that delivers high spatial resolution and robustness performance. This methodology enables arbitrary scan trajectories and should facilitate translation of such acquisition methods in a clinical setting.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Brighi2024,

title = {Reply to Letter from Price et al, re: Translation of focused ultrasound for blood-brain barrier opening in glioma},

author = {Caterina Brighi and Ekaterina Salimova and Michael de Veer and Simon Puttick and Gary Egan},

doi = {10.1016/j.jconrel.2022.06.042},

issn = {0168-3659},

year  = {2024},

date = {2024-02-00},

journal = {Journal of Controlled Release},

volume = {366},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lau2023b,

title = {Faster and lower dose imaging: evaluating adaptive, constant gantry velocity and angular separation in fast low‐dose 4D cone beam CT imaging},

author = {Benjamin K. F. Lau and Owen Dillon and Shalini K. Vinod and Ricky T. O'Brien and Tess Reynolds},

doi = {10.1002/mp.16585},

issn = {2473-4209},

year  = {2024},

date = {2024-02-00},

journal = {Medical Physics},

volume = {51},

number = {2},

pages = {1364--1382},

publisher = {Wiley},

abstract = {AbstractBackgroundThe adoption of four‐dimensional cone beam computed tomography (4DCBCT) for image‐guided lung cancer radiotherapy is increasing, especially for hypofractionated treatments. However, the drawbacks of 4DCBCT include long scan times (∼240 s), inconsistent image quality, higher imaging dose than necessary, and streaking artifacts. With the emergence of linear accelerators that can acquire 4DCBCT scans in a short period of time (9.2 s) there is a need to examine the impact that these very fast gantry rotations have on 4DCBCT image quality.PurposeThis study investigates the impact of gantry velocity and angular separation between x‐ray projections on image quality and its implication for fast low‐dose 4DCBCT with emerging systems, such as the Varian Halcyon that provide fast gantry rotation and imaging. Large and uneven angular separation between x‐ray projections is known to reduce 4DCBCT image quality through increased streaking artifacts. However, it is not known when angular separation starts degrading image quality. The study assesses the impact of constant and adaptive gantry velocity and determines the level when angular gaps impair image quality using state‐of‐the‐art reconstruction methods.MethodsThis study considers fast low‐dose 4DCBCT acquisitions (60–80 s, 200‐projection scans). To assess the impact of adaptive gantry rotations, the angular position of x‐ray projections from adaptive 4DCBCT acquisitions from a 30‐patient clinical trial were analyzed (referred to as patient angular gaps). To assess the impact of angular gaps, variable and static angular gaps (20°, 30°, 40°) were introduced into evenly separated 200 projections (ideal angular separation). To simulate fast gantry rotations, which are on emerging linacs, constant gantry velocity acquisitions (9.2 s, 60 s, 120 s, 240 s) were simulated by sampling x‐ray projections at constant intervals using the patient breathing traces from the ADAPT clinical trial (ACTRN12618001440213). The 4D Extended Cardiac‐Torso (XCAT) digital phantom was used to simulate projections to remove patient‐specific image quality variables.Image reconstruction was performed using Feldkamp‐Davis‐Kress (FDK), McKinnon‐Bates (MKB), and Motion‐Compensated‐MKB (MCMKB) algorithms. Image quality was assessed using Structural Similarity‐Index‐Measure (SSIM), Contrast‐to‐Noise‐Ratio (CNR), Signal‐to‐Noise‐Ratio (SNR), Tissue‐Interface‐Width‐Diaphragm (TIW‐D), and Tissue‐Interface‐Width‐Tumor (TIW‐T).ResultsPatient angular gaps and variable angular gap reconstructions produced similar results to ideal angular separation reconstructions, while static angular gap reconstructions produced lower image quality metrics. For MCMKB‐reconstructions, average patient angular gaps produced SSIM‐0.98, CNR‐13.6, SNR‐34.8, TIW‐D‐1.5 mm, and TIW‐T‐2.0 mm, static angular gap 40° produced SSIM‐0.92, CNR‐6.8, SNR‐6.7, TIW‐D‐5.7 mm, and TIW‐T‐5.9 mm and ideal produced SSIM‐1.00, CNR‐13.6, SNR‐34.8, TIW‐D‐1.5 mm, and TIW‐T‐2.0 mm. All constant gantry velocity reconstructions produced lower image quality metrics than ideal angular separation reconstructions regardless of the acquisition time. Motion compensated reconstruction (MCMKB) produced the highest contrast images with low streaking artifacts.ConclusionVery fast 4DCBCT scans can be acquired provided that the entire scan range is adaptively sampled, and motion‐compensated reconstruction is performed. Importantly, the angular separation between x‐ray projections within each individual respiratory bin had minimal effect on the image quality of fast low‐dose 4DCBCT imaging. The results will assist the development of future 4DCBCT acquisition protocols that can now be achieved in very short time frames with emerging linear accelerators.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lau2023,

title = {Faster and lower dose imaging: evaluating adaptive, constant gantry velocity and angular separation in fast low‐dose 4D cone beam CT imaging},

author = {Benjamin K. F. Lau and Owen Dillon and Shalini K. Vinod and Ricky T. O'Brien and Tess Reynolds},

doi = {10.1002/mp.16585},

issn = {2473-4209},

year  = {2024},

date = {2024-02-00},

journal = {Medical Physics},

volume = {51},

number = {2},

pages = {1364--1382},

publisher = {Wiley},

abstract = {AbstractBackgroundThe adoption of four‐dimensional cone beam computed tomography (4DCBCT) for image‐guided lung cancer radiotherapy is increasing, especially for hypofractionated treatments. However, the drawbacks of 4DCBCT include long scan times (∼240 s), inconsistent image quality, higher imaging dose than necessary, and streaking artifacts. With the emergence of linear accelerators that can acquire 4DCBCT scans in a short period of time (9.2 s) there is a need to examine the impact that these very fast gantry rotations have on 4DCBCT image quality.PurposeThis study investigates the impact of gantry velocity and angular separation between x‐ray projections on image quality and its implication for fast low‐dose 4DCBCT with emerging systems, such as the Varian Halcyon that provide fast gantry rotation and imaging. Large and uneven angular separation between x‐ray projections is known to reduce 4DCBCT image quality through increased streaking artifacts. However, it is not known when angular separation starts degrading image quality. The study assesses the impact of constant and adaptive gantry velocity and determines the level when angular gaps impair image quality using state‐of‐the‐art reconstruction methods.MethodsThis study considers fast low‐dose 4DCBCT acquisitions (60–80 s, 200‐projection scans). To assess the impact of adaptive gantry rotations, the angular position of x‐ray projections from adaptive 4DCBCT acquisitions from a 30‐patient clinical trial were analyzed (referred to as patient angular gaps). To assess the impact of angular gaps, variable and static angular gaps (20°, 30°, 40°) were introduced into evenly separated 200 projections (ideal angular separation). To simulate fast gantry rotations, which are on emerging linacs, constant gantry velocity acquisitions (9.2 s, 60 s, 120 s, 240 s) were simulated by sampling x‐ray projections at constant intervals using the patient breathing traces from the ADAPT clinical trial (ACTRN12618001440213). The 4D Extended Cardiac‐Torso (XCAT) digital phantom was used to simulate projections to remove patient‐specific image quality variables.Image reconstruction was performed using Feldkamp‐Davis‐Kress (FDK), McKinnon‐Bates (MKB), and Motion‐Compensated‐MKB (MCMKB) algorithms. Image quality was assessed using Structural Similarity‐Index‐Measure (SSIM), Contrast‐to‐Noise‐Ratio (CNR), Signal‐to‐Noise‐Ratio (SNR), Tissue‐Interface‐Width‐Diaphragm (TIW‐D), and Tissue‐Interface‐Width‐Tumor (TIW‐T).ResultsPatient angular gaps and variable angular gap reconstructions produced similar results to ideal angular separation reconstructions, while static angular gap reconstructions produced lower image quality metrics. For MCMKB‐reconstructions, average patient angular gaps produced SSIM‐0.98, CNR‐13.6, SNR‐34.8, TIW‐D‐1.5 mm, and TIW‐T‐2.0 mm, static angular gap 40° produced SSIM‐0.92, CNR‐6.8, SNR‐6.7, TIW‐D‐5.7 mm, and TIW‐T‐5.9 mm and ideal produced SSIM‐1.00, CNR‐13.6, SNR‐34.8, TIW‐D‐1.5 mm, and TIW‐T‐2.0 mm. All constant gantry velocity reconstructions produced lower image quality metrics than ideal angular separation reconstructions regardless of the acquisition time. Motion compensated reconstruction (MCMKB) produced the highest contrast images with low streaking artifacts.ConclusionVery fast 4DCBCT scans can be acquired provided that the entire scan range is adaptively sampled, and motion‐compensated reconstruction is performed. Importantly, the angular separation between x‐ray projections within each individual respiratory bin had minimal effect on the image quality of fast low‐dose 4DCBCT imaging. The results will assist the development of future 4DCBCT acquisition protocols that can now be achieved in very short time frames with emerging linear accelerators.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sengupta2024c,

title = {The first clinical implementation of real-time 6 degree-of-freedom image-guided radiotherapy for liver SABR patients},

author = {Chandrima Sengupta and Doan Trang Nguyen and Trevor Moodie and Daniel Mason and Jianjie Luo and Trent Causer and Sau Fan Liu and Elizabeth Brown and Lauren Inskip and Maryam Hazem and Menglei Chao and Tim Wang and Yoo Y. Lee and Kirsten van Gysen and Emma Sullivan and Eireann Cosgriff and Prabhakar Ramachandran and Per Poulsen and Jeremy Booth and Ricky O'Brien and Peter Greer and Paul Keall},

doi = {10.1016/j.radonc.2023.110031},

issn = {0167-8140},

year  = {2024},

date = {2024-01-00},

journal = {Radiotherapy and Oncology},

volume = {190},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lombardo2024b,

title = {Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects},

author = {Elia Lombardo and Jennifer Dhont and Denis Page and Cristina Garibaldi and Luise A. Künzel and Coen Hurkmans and Rob H.N. Tijssen and Chiara Paganelli and Paul Z.Y. Liu and Paul J. Keall and Marco Riboldi and Christopher Kurz and Guillaume Landry and Davide Cusumano and Marco Fusella and Lorenzo Placidi},

doi = {10.1016/j.radonc.2023.109970},

issn = {0167-8140},

year  = {2024},

date = {2024-01-00},

journal = {Radiotherapy and Oncology},

volume = {190},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sengupta2024b,

title = {The first clinical implementation of real-time 6 degree-of-freedom image-guided radiotherapy for liver SABR patients},

author = {Chandrima Sengupta and Doan Trang Nguyen and Trevor Moodie and Daniel Mason and Jianjie Luo and Trent Causer and Sau Fan Liu and Elizabeth Brown and Lauren Inskip and Maryam Hazem and Menglei Chao and Tim Wang and Yoo Y. Lee and Kirsten van Gysen and Emma Sullivan and Eireann Cosgriff and Prabhakar Ramachandran and Per Poulsen and Jeremy Booth and Ricky O'Brien and Peter Greer and Paul Keall},

doi = {10.1016/j.radonc.2023.110031},

issn = {0167-8140},

year  = {2024},

date = {2024-01-00},

journal = {Radiotherapy and Oncology},

volume = {190},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lombardo2024,

title = {Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects},

author = {Elia Lombardo and Jennifer Dhont and Denis Page and Cristina Garibaldi and Luise A. Künzel and Coen Hurkmans and Rob H.N. Tijssen and Chiara Paganelli and Paul Z.Y. Liu and Paul J. Keall and Marco Riboldi and Christopher Kurz and Guillaume Landry and Davide Cusumano and Marco Fusella and Lorenzo Placidi},

doi = {10.1016/j.radonc.2023.109970},

issn = {0167-8140},

year  = {2024},

date = {2024-01-00},

journal = {Radiotherapy and Oncology},

volume = {190},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{BenBouchta2024,

title = {The Remove-the-Mask Open-Source head and neck Surface-Guided radiation therapy system},

author = {Youssef Ben Bouchta and Mark Gardner and Chandrima Sengupta and Julia Johnson and Paul Keall},

doi = {10.1016/j.phro.2024.100541},

issn = {2405-6316},

year  = {2024},

date = {2024-01-00},

journal = {Physics and Imaging in Radiation Oncology},

volume = {29},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Shan2024,

title = {Image Reconstruction With B₀ Inhomogeneity Using a Deep Unrolled Network on an Open-Bore MRI-Linac},

author = {Shanshan Shan and Yang Gao and David Waddington and Hongli Chen and Brendan Whelan and Paul Liu and Yaohui Wang and Chunyi Liu and Hongping Gan and Mingyuan Gao and Feng Liu},

doi = {10.1109/tim.2024.3481545},

issn = {1557-9662},

year  = {2024},

date = {2024-00-00},

journal = {IEEE Trans. Instrum. Meas.},

volume = {73},

pages = {1--9},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Brighi2023b,

title = {The MANGO study: a prospective investigation of oxygen enhanced and blood-oxygen level dependent MRI as imaging biomarkers of hypoxia in glioblastoma},

author = {Caterina Brighi and David E. J. Waddington and Paul J. Keall and Jeremy Booth and Kieran O’Brien and Shona Silvester and Jonathon Parkinson and Marco Mueller and Jackie Yim and Dale L. Bailey and Michael Back and James Drummond},

doi = {10.3389/fonc.2023.1306164},

issn = {2234-943X},

year  = {2023},

date = {2023-12-19},

journal = {Front. Oncol.},

volume = {13},

publisher = {Frontiers Media SA},

abstract = {BackgroundGlioblastoma (GBM) is the most aggressive type of brain cancer, with a 5-year survival rate of ~5% and most tumours recurring locally within months of first-line treatment. Hypoxia is associated with worse clinical outcomes in GBM, as it leads to localized resistance to radiotherapy and subsequent tumour recurrence. Current standard of care treatment does not account for tumour hypoxia, due to the challenges of mapping tumour hypoxia in routine clinical practice. In this clinical study, we aim to investigate the role of oxygen enhanced (OE) and blood-oxygen level dependent (BOLD) MRI as non-invasive imaging biomarkers of hypoxia in GBM, and to evaluate their potential role in dose-painting radiotherapy planning and treatment response assessment.MethodsThe primary endpoint is to evaluate the quantitative and spatial correlation between OE and BOLD MRI measurements and [18F]MISO values of uptake in the tumour. The secondary endpoints are to evaluate the repeatability of MRI biomarkers of hypoxia in a test-retest study, to estimate the potential clinical benefits of using MRI biomarkers of hypoxia to guide dose-painting radiotherapy, and to evaluate the ability of MRI biomarkers of hypoxia to assess treatment response. Twenty newly diagnosed GBM patients will be enrolled in this study. Patients will undergo standard of care treatment while receiving additional OE/BOLD MRI and [18F]MISO PET scans at several timepoints during treatment. The ability of OE/BOLD MRI to map hypoxic tumour regions will be evaluated by assessing spatial and quantitative correlations with areas of hypoxic tumour identified via [18F]MISO PET imaging.DiscussionMANGO (Magnetic resonance imaging of hypoxia for radiation treatment guidance in glioblastoma multiforme) is a diagnostic/prognostic study investigating the role of imaging biomarkers of hypoxia in GBM management. The study will generate a large amount of longitudinal multimodal MRI and PET imaging data that could be used to unveil dynamic changes in tumour physiology that currently limit treatment efficacy, thereby providing a means to develop more effective and personalised treatments.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Trada2023,

title = {Changes in serial multiparametric MRI and FDG-PET/CT functional imaging during radiation therapy can predict treatment response in patients with head and neck cancer},

author = {Yuvnik Trada and Paul Keall and Michael Jameson and Daniel Moses and Peter Lin and Phillip Chlap and Lois Holloway and Myo Min and Dion Forstner and Allan Fowler and Mark T. Lee},

doi = {10.1007/s00330-023-09843-2},

issn = {1432-1084},

year  = {2023},

date = {2023-12-00},

journal = {Eur Radiol},

volume = {33},

number = {12},

pages = {8788--8799},

publisher = {Springer Science and Business Media LLC},

abstract = {Abstract

                Objectives

                To test if tumour changes measured using combination of diffusion-weighted imaging (DWI) MRI and FDG-PET/CT performed serially during radiotherapy (RT) in mucosal head and neck carcinoma can predict treatment response.

              

                Methods

                Fifty-five patients from two prospective imaging biomarker studies were analysed. FDG-PET/CT was performed at baseline, during RT (week 3), and post RT (3 months). DWI was performed at baseline, during RT (weeks 2, 3, 5, 6), and post RT (1 and 3 months). The ADCmean from DWI and FDG-PET parameters SUVmax, SUVmean, metabolic tumour volume (MTV), and total lesion glycolysis (TLG) were measured. Absolute and relative change (%∆) in DWI and PET parameters were correlated to 1-year local recurrence. Patients were categorised into favourable, mixed, and unfavourable imaging response using optimal cut-off (OC) values of DWI and FDG-PET parameters and correlated to local control.

              

                Results

                The 1-year local, regional, and distant recurrence rates were 18.2% (10/55), 7.3% (4/55), and 12.7% (7/55), respectively. ∆Week 3 ADCmean (AUC 0.825, p = 0.003; OC ∆ > 24.4%) and ∆MTV (AUC 0.833, p = 0.001; OC ∆ > 50.4%) were the best predictors of local recurrence. Week 3 was the optimal time point for assessing DWI imaging response. Using a combination of ∆ADCmean and ∆MTV improved the strength of correlation to local recurrence (p ≤ 0.001). In patients who underwent both week 3 MRI and FDG-PET/CT, significant differences in local recurrence rates were seen between patients with favourable (0%), mixed (17%), and unfavourable (78%) combined imaging response.

              

                Conclusions

                Changes in mid-treatment DWI and FDG-PET/CT imaging can predict treatment response and could be utilised in the design of future adaptive clinical trials.

              

                Clinical relevance statement

                Our study shows the complementary information provided by two functional imaging modalities for mid-treatment response prediction in patients with head and neck cancer.

              

                Key Points

                •FDG-PET/CT and DWI MRI changes in tumour during radiotherapy in head and neck cancer can predict treatment response.

                •Combination of FDG-PET/CT and DWI parameters improved correlation to clinical outcome.

                •Week 3 was the optimal time point for DWI MRI imaging response assessment.

              },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Dillon2023,

title = {X-ray source arrays for volumetric imaging during radiotherapy treatment},

author = {Owen Dillon and Tess Reynolds and Ricky T. O’Brien},

doi = {10.1038/s41598-023-36708-x},

issn = {2045-2322},

year  = {2023},

date = {2023-12-00},

journal = {Sci Rep},

volume = {13},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractThis work presents a novel hardware configuration for radiotherapy systems to enable fast 3D X-ray imaging before and during treatment delivery. Standard external beam radiotherapy linear accelerators (linacs) have a single X-ray source and detector located at ± 90° from the treatment beam respectively. The entire system can be rotated around the patient acquiring multiple 2D X-ray images to create a 3D cone-beam Computed Tomography (CBCT) image before treatment delivery to ensure the tumour and surrounding organs align with the treatment plan. Scanning with a single source is slow relative to patient respiration or breath holds and cannot be performed during treatment delivery, limiting treatment delivery accuracy in the presence of patient motion and excluding some patients from concentrated treatment plans that would be otherwise expected to have improved outcomes. This simulation study investigated whether recent advances in carbon nanotube (CNT) field emission source arrays, high frame rate (60 Hz) flat panel detectors and compressed sensing reconstruction algorithms could circumvent imaging limitations of current linacs. We investigated a novel hardware configuration incorporating source arrays and high frame rate detectors into an otherwise standard linac. We investigated four potential pre-treatment scan protocols that could be achieved in a 17 s breath hold or 2–10 1 s breath holds. Finally, we demonstrated for the first time volumetric X-ray imaging during treatment delivery by using source arrays, high frame rate detectors and compressed sensing. Image quality was assessed quantitatively over the CBCT geometric field of view as well as across each axis through the tumour centroid. Our results demonstrate that source array imaging enables larger volumes to be imaged with acquisitions as short as 1 s albeit with reduced image quality arising from lower photon flux and shorter imaging arcs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Debrot2023,

title = {Nano X Image Guidance in radiation therapy: feasibility study protocol for cone beam computed tomography imaging with gravity-induced motion},

author = {Emily Debrot and Paul Liu and Mark Gardner and Soo Min Heng and Chin Hwa Chan and Stephanie Corde and Simon Downes and Michael Jackson and Paul Keall},

doi = {10.1186/s40814-023-01340-z},

issn = {2055-5784},

year  = {2023},

date = {2023-12-00},

journal = {Pilot Feasibility Stud},

volume = {9},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {Abstract

                Background

                This paper describes the protocol for the Nano X Image Guidance (Nano X IG) trial, a single-institution, clinical imaging study. The Nano X is a prototype fixed-beam radiotherapy system developed to investigate the feasibility of a low-cost, compact radiotherapy system to increase global access to radiation therapy. This study aims to assess the feasibility of volumetric image guidance with cone beam computed tomography (CBCT) acquired during horizontal patient rotation on the Nano X radiotherapy system.

              

                Methods

                In the Nano X IG study, we will determine whether radiotherapy image guidance can be performed with the Nano X radiotherapy system where the patient is horizontally rotated while scan projections are acquired. We will acquire both conventional CBCT scans and Nano X CBCT scans for 30 patients aged 18 and above and receiving radiotherapy for head/neck or upper abdomen cancers. For each patient, a panel of experts will assess the image quality of Nano X CBCT scans against conventional CBCT scans. Each patient will receive two Nano X CBCT scans to determine the image quality reproducibility, the extent and reproducibility of patient motion and assess patient tolerance.

              

                Discussion

                Fixed-beam radiotherapy systems have the potential to help ease the current shortfall and increase global access to radiotherapy treatment. Advances in image guidance could facilitate fixed-beam radiotherapy using horizontal patient rotation. The efficacy of this radiotherapy approach is dependent on our ability to image and adapt to motion due to rotation and for patients to tolerate rotation during treatment.

              

                Trial registration

                ClinicalTrials.gov, NCT04488224. Registered on 27 July 2020.



              },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Byrne2023,

title = {BRAVEHeart: a randomised trial comparing the accuracy of Breathe Well and RPM for deep inspiration breath hold breast cancer radiotherapy},

author = {Hilary L. Byrne and Elisabeth Steiner and Jeremy Booth and Gillian Lamoury and Marita Morgia and Kylie Richardson and Leigh Ambrose and Kuldeep Makhija and Cameron Stanton and Benjamin Zwan and Regina Bromley and John Atyeo and Shona Silvester and Natalie Plant and Paul Keall},

doi = {10.1186/s13063-023-07072-y},

issn = {1745-6215},

year  = {2023},

date = {2023-12-00},

journal = {Trials},

volume = {24},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {Abstract

                Background

                Deep inspiration breath hold (DIBH) reduces radiotherapy cardiac dose for left-sided breast cancer patients. The primary aim of the BRAVEHeart (Breast Radiotherapy Audio Visual Enhancement for sparing the Heart) trial is to assess the accuracy and usability of a novel device, Breathe Well, for DIBH guidance for left-sided breast cancer patients. Breathe Well will be compared to an adapted widely available monitoring system, the Real-time Position Management system (RPM).

              

                Methods

                BRAVEHeart is a single institution prospective randomised trial of two DIBH devices. BRAVEHeart will assess the DIBH accuracy for Breathe Well and RPM during left-sided breast cancer radiotherapy. After informed consent has been obtained, 40 patients will be randomised into two equal groups, the experimental arm (Breathe Well) and the control arm (RPM with in-house modification of an added patient screen). The primary hypothesis of BRAVEHeart is that the accuracy of Breathe Well in maintaining the position of the chest during DIBH is superior to the RPM system. Accuracy will be measured by comparing chest wall motion extracted from images acquired of the treatment field during breast radiotherapy for patients treated using the Breathe Well system and those using the RPM system.

              

                Discussion

                The Breathe Well device uses a depth camera to monitor the chest surface while the RPM system monitors a block on the patient’s abdomen. The hypothesis of this trial is that the chest surface is a better surrogate for the internal chest wall motion used as a measure of treatment accuracy. The Breathe Well device aims to deliver an easy-to-use implementation of surface monitoring. The findings from the study will help inform the technology choice for other centres performing DIBH.

              

                Trial registration

                ClinicalTrials.govNCT02881203. Registered on 26 August 2016.

              },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lombardo2023,

title = {Experimental comparison of linear regression and LSTM motion prediction models for MLC‐tracking on an MRI‐linac},

author = {Elia Lombardo and Paul Z. Y. Liu and David E. J. Waddington and James Grover and Brendan Whelan and Esther Wong and Michael Reiner and Stefanie Corradini and Claus Belka and Marco Riboldi and Christopher Kurz and Guillaume Landry and Paul J. Keall},

doi = {10.1002/mp.16770},

issn = {2473-4209},

year  = {2023},

date = {2023-11-00},

journal = {Medical Physics},

volume = {50},

number = {11},

pages = {7083--7092},

publisher = {Wiley},

abstract = {AbstractBackgroundMagnetic resonance imaging (MRI)‐guided radiotherapy with multileaf collimator (MLC)‐tracking is a promising technique for intra‐fractional motion management, achieving high dose conformality without prolonging treatment times. To improve beam‐target alignment, the geometric error due to system latency should be reduced by using temporal prediction.PurposeTo experimentally compare linear regression (LR) and long‐short‐term memory (LSTM) motion prediction models for MLC‐tracking on an MRI‐linac using multiple patient‐derived traces with different complexities.MethodsExperiments were performed on a prototype 1.0 T MRI‐linac capable of MLC‐tracking. A motion phantom was programmed to move a target in superior‐inferior (SI) direction according to eight lung cancer patient respiratory motion traces. Target centroid positions were localized from sagittal 2D cine MRIs acquired at 4 Hz using a template matching algorithm. The centroid positions were input to one of four motion prediction models. We used (1) a LSTM network which had been optimized in a previous study on patient data from another cohort (offline LSTM). We also used (2) the same LSTM model as a starting point for continuous re‐optimization of its weights during the experiment based on recent motion (offline+online LSTM). Furthermore, we implemented (3) a continuously updated LR model, which was solely based on recent motion (online LR). Finally, we used (4) the last available target centroid without any changes as a baseline (no‐predictor). The predictions of the models were used to shift the MLC aperture in real‐time. An electronic portal imaging device (EPID) was used to visualize the target and MLC aperture during the experiments. Based on the EPID frames, the root‐mean‐square error (RMSE) between the target and the MLC aperture positions was used to assess the performance of the different motion predictors. Each combination of motion trace and prediction model was repeated twice to test stability, for a total of 64 experiments.ResultsThe end‐to‐end latency of the system was measured to be (389 ± 15) ms and was successfully mitigated by both LR and LSTM models. The offline+online LSTM was found to outperform the other models for all investigated motion traces. It obtained a median RMSE over all traces of (2.8 ± 1.3) mm, compared to the (3.2 ± 1.9) mm of the offline LSTM, the (3.3 ± 1.4) mm of the online LR and the (4.4 ± 2.4) mm when using the no‐predictor. According to statistical tests, differences were significant (p‐value <0.05) among all models in a pair‐wise comparison, but for the offline LSTM and online LR pair. The offline+online LSTM was found to be more reproducible than the offline LSTM and the online LR with a maximum deviation in RMSE between two measurements of 10%.ConclusionsThis study represents the first experimental comparison of different prediction models for MRI‐guided MLC‐tracking using several patient‐derived respiratory motion traces. We have shown that among the investigated models, continuously re‐optimized LSTM networks are the most promising to account for the end‐to‐end system latency in MRI‐guided radiotherapy with MLC‐tracking.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Brighi2023,

title = {Comparison between [68Ga]Ga-PSMA-617 and [18F]FET PET as Imaging Biomarkers in Adult Recurrent Glioblastoma},

author = {Caterina Brighi and Simon Puttick and Amanda Woods and Paul Keall and Paul A. Tooney and David E. J. Waddington and Vicki Sproule and Stephen Rose and Michael Fay},

doi = {10.3390/ijms242216208},

issn = {1422-0067},

year  = {2023},

date = {2023-11-00},

journal = {IJMS},

volume = {24},

number = {22},

publisher = {MDPI AG},

abstract = {The aim of this prospective clinical study was to evaluate the potential of the prostate specific membrane antigen (PSMA) targeting ligand, [68Ga]-PSMA–Glu–NH–CO–NH–Lys-2-naphthyl-L-Ala-cyclohexane-DOTA ([68Ga]Ga-PSMA-617) as a positron emission tomography (PET) imaging biomarker in recurrent glioblastoma patients. Patients underwent [68Ga]Ga-PSMA-617 and O-(2-[18F]-fluoroethyl)-L-tyrosine ([18F]FET) PET scans on two separate days. [68Ga]Ga-PSMA-617 tumour selectivity was assessed by comparing tumour volume delineation and by assessing the intra-patient correlation between tumour uptake on [68Ga]Ga-PSMA-617 and [18F]FET PET images. [68Ga]Ga-PSMA-617 tumour specificity was evaluated by comparing its tumour-to-brain ratio (TBR) with [18F]FET TBR and its tumour volume with the magnetic resonance imaging (MRI) contrast-enhancing (CE) tumour volume. Ten patients were recruited in this study. [68Ga]Ga-PSMA-617-avid tumour volume was larger than the [18F]FET tumour volume (p = 0.063). There was a positive intra-patient correlation (median Pearson r = 0.51; p < 0.0001) between [68Ga]Ga-PSMA-617 and [18F]FET in the tumour volume. [68Ga]Ga-PSMA-617 had significantly higher TBR (p = 0.002) than [18F]FET. The [68Ga]Ga-PSMA-617-avid tumour volume was larger than the CE tumour volume (p = 0.0039). Overall, accumulation of [68Ga]-Ga-PSMA-617 beyond [18F]FET-avid tumour regions suggests the presence of neoangiogenesis in tumour regions that are not overly metabolically active yet. Higher tumour specificity suggests that [68Ga]-Ga-PSMA-617 could be a better imaging biomarker for recurrent tumour delineation and secondary treatment planning than [18F]FET and CE MRI.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Whelan2023,

title = {ParticlePhaseSpace: A python package for streamlined

import, analysis, and export of particle phase space data},

author = {Brendan Whelan and Leo Esnault},

doi = {10.21105/joss.05375},

issn = {2475-9066},

year  = {2023},

date = {2023-09-00},

journal = {JOSS},

volume = {8},

number = {89},

publisher = {The Open Journal},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Trada2023b,

title = {Mid-treatment 18F-FDG PET imaging changes in parotid gland correlates to radiation-induced xerostomia},

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 = {10.1016/j.radonc.2023.109745},

issn = {0167-8140},

year  = {2023},

date = {2023-09-00},

journal = {Radiotherapy and Oncology},

volume = {186},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Shan2023,

title = {Distortion‐corrected image reconstruction with deep learning on an MRI‐Linac},

author = {Shanshan Shan and Yang Gao and Paul Z. Y. Liu and Brendan Whelan and Hongfu Sun and Bin Dong and Feng Liu and David E. J. Waddington},

doi = {10.1002/mrm.29684},

issn = {1522-2594},

year  = {2023},

date = {2023-09-00},

journal = {Magnetic Resonance in Med},

volume = {90},

number = {3},

pages = {963--977},

publisher = {Wiley},

abstract = {PurposeMRI is increasingly utilized for image‐guided radiotherapy due to its outstanding soft‐tissue contrast and lack of ionizing radiation. However, geometric distortions caused by gradient nonlinearities (GNLs) limit anatomical accuracy, potentially compromising the quality of tumor treatments. In addition, slow MR acquisition and reconstruction limit the potential for effective image guidance. Here, we demonstrate a deep learning‐based method that rapidly reconstructs distortion‐corrected images from raw k‐space data for MR‐guided radiotherapy applications.MethodsWe leverage recent advances in interpretable unrolling networks to develop a Distortion‐Corrected Reconstruction Network (DCReconNet) that applies convolutional neural networks (CNNs) to learn effective regularizations and nonuniform fast Fourier transforms for GNL‐encoding. DCReconNet was trained on a public MR brain dataset from 11 healthy volunteers for fully sampled and accelerated techniques, including parallel imaging (PI) and compressed sensing (CS). The performance of DCReconNet was tested on phantom, brain, pelvis, and lung images acquired on a 1.0T MRI‐Linac. The DCReconNet, CS‐, PI‐and UNet‐based reconstructed image quality was measured by structural similarity (SSIM) and RMS error (RMSE) for numerical comparisons. The computation time and residual distortion for each method were also reported.ResultsImaging results demonstrated that DCReconNet better preserves image structures compared to CS‐ and PI‐based reconstruction methods. DCReconNet resulted in the highest SSIM (0.95 median value) and lowest RMSE (<0.04) on simulated brain images with four times acceleration. DCReconNet is over 10‐times faster than iterative, regularized reconstruction methods.ConclusionsDCReconNet provides fast and geometrically accurate image reconstruction and has the potential for MRI‐guided radiotherapy applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Richardson2023,

title = {Genitourinary Quality-of-Life Comparison Between Urethral Sparing Prostate Stereotactic Body Radiation Therapy Monotherapy and Virtual High-Dose-Rate Brachytherapy Boost},

author = {Matthew Richardson and Mark Sidhom and Paul Keall and Lucy Leigh and Helen Ball and Joseph Bucci and Sarah Gallagher and Peter Greer and Amy J. Hayden and Andrew Kneebone and David Pryor and Shankar Siva and Jarad Martin},

doi = {10.1016/j.ijrobp.2023.02.049},

issn = {0360-3016},

year  = {2023},

date = {2023-08-00},

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

volume = {116},

number = {5},

pages = {1069--1078},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}