Publications

@article{Hindley2023,

title = {A patient-specific deep learning framework for 3D motion estimation and volumetric imaging during lung cancer radiotherapy},

author = {Nicholas Hindley and Chun-Chien Shieh and Paul Keall},

doi = {10.1088/1361-6560/ace1d0},

issn = {1361-6560},

year  = {2023},

date = {2023-07-21},

journal = {Phys. Med. Biol.},

volume = {68},

number = {14},

publisher = {IOP Publishing},

abstract = {Abstract

               

                  Objective. Respiration introduces a constant source of irregular motion that poses a significant challenge for the precise irradiation of thoracic and abdominal cancers. Current real-time motion management strategies require dedicated systems that are not available in most radiotherapy centers. We sought to develop a system that estimates and visualises the impact of respiratory motion in 3D given the 2D images acquired on a standard linear accelerator. Approach. In this paper we introduce Voxelmap, a patient-specific deep learning framework that achieves 3D motion estimation and volumetric imaging using the data and resources available in standard clinical settings. Here we perform a simulation study of this framework using imaging data from two lung cancer patients. Main results. Using 2D images as input and 3D–3D Elastix registrations as ground-truth, Voxelmap was able to continuously predict 3D tumor motion with mean errors of 0.1 ± 0.5, −0.6 ± 0.8, and 0.0 ± 0.2 mm along the left–right, superior–inferior, and anterior–posterior axes respectively. Voxelmap also predicted 3D thoracoabdominal motion with mean errors of −0.1 ± 0.3, −0.1 ± 0.6, and −0.2 ± 0.2 mm respectively. Moreover, volumetric imaging was achieved with mean average error 0.0003, root-mean-squared error 0.0007, structural similarity 1.0 and peak-signal-to-noise ratio 65.8. Significance. The results of this study demonstrate the possibility of achieving 3D motion estimation and volumetric imaging during lung cancer treatments on a standard linear accelerator.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gardner2023,

title = {Realistic CT data augmentation for accurate deep‐learning based segmentation of head and neck tumors in kV images acquired during radiation therapy},

author = {Mark Gardner and Youssef Ben Bouchta and Adam Mylonas and Marco Mueller and Chen Cheng and Phillip Chlap and Robert Finnegan and Jonathan Sykes and Paul J Keall and Doan Trang Nguyen},

doi = {10.1002/mp.16388},

issn = {2473-4209},

year  = {2023},

date = {2023-07-00},

journal = {Medical Physics},

volume = {50},

number = {7},

pages = {4206--4219},

publisher = {Wiley},

abstract = {AbstractBackgroundUsing radiation therapy (RT) to treat head and neck (H&N) cancers requires precise targeting of the tumor to avoid damaging the surrounding healthy organs. Immobilisation masks and planning target volume margins are used to attempt to mitigate patient motion during treatment, however patient motion can still occur. Patient motion during RT can lead to decreased treatment effectiveness and a higher chance of treatment related side effects. Tracking tumor motion would enable motion compensation during RT, leading to more accurate dose delivery.PurposeThe purpose of this paper is to develop a method to detect and segment the tumor in kV images acquired during RT. Unlike previous tumor segmentation methods for kV images, in this paper, a process for generating realistic and synthetic CT deformations was developed to augment the training data and make the segmentation method robust to patient motion. Detecting the tumor in 2D kV images is a necessary step toward 3D tracking of the tumor position during treatment.MethodIn this paper, a conditional generative adversarial network (cGAN) is presented that can detect and segment the gross tumor volume (GTV) in kV images acquired during H&N RT. Retrospective data from 15 H&N cancer patients obtained from the Cancer Imaging Archive were used to train and test patient‐specific cGANs. The training data consisted of digitally reconstructed radiographs (DRRs) generated from each patient's planning CT and contoured GTV. Training data was augmented by using synthetically deformed CTs to generate additional DRRs (in total 39 600 DRRs per patient or 25 200 DRRs for nasopharyngeal patients) containing realistic patient motion. The method for deforming the CTs was a novel deformation method based on simulating head rotation and internal tumor motion. The testing dataset consisted of 1080 DRRs for each patient, obtained by deforming the planning CT and GTV at different magnitudes to the training data.The accuracy of the generated segmentations was evaluated by measuring the segmentation centroid error, Dice similarity coefficient (DSC) and mean surface distance (MSD). This paper evaluated the hypothesis that when patient motion occurs, using a cGAN to segment the GTV would create a more accurate segmentation than no‐tracking segmentations from the original contoured GTV, the current standard‐of‐care. This hypothesis was tested using the 1‐tailed Mann‐Whitney U‐test.ResultsThe magnitude of our cGAN segmentation centroid error was (mean ± standard deviation) 1.1 ± 0.8 mm and the DSC and MSD values were 0.90 ± 0.03 and 1.6 ± 0.5 mm, respectively. Our cGAN segmentation method reduced the segmentation centroid error (p < 0.001), and MSD (p = 0.031) when compared to the no‐tracking segmentation, but did not significantly increase the DSC (p = 0.294).ConclusionsThe accuracy of our cGAN segmentation method demonstrates the feasibility of this method for H&N cancer patients during RT. Accurate tumor segmentation of H&N tumors would allow for intrafraction monitoring methods to compensate for tumor motion during treatment, ensuring more accurate dose delivery and enabling better H&N cancer patient outcomes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chrystall2023,

title = {Deep learning enables MV-based real-time image guided radiation therapy for prostate cancer patients},

author = {Danielle Chrystall and Adam Mylonas and Emily Hewson and Jarad Martin and Paul Keall and Jeremy Booth and Doan Trang Nguyen},

doi = {10.1088/1361-6560/acc77c},

issn = {1361-6560},

year  = {2023},

date = {2023-05-07},

journal = {Phys. Med. Biol.},

volume = {68},

number = {9},

publisher = {IOP Publishing},

abstract = {Abstract

               

                  Objective. Using MV images for real-time image guided radiation therapy (IGRT) is ideal as it does not require additional imaging equipment, adds no additional imaging dose and provides motion data in the treatment beam frame of reference. However, accurate tracking using MV images is challenging due to low contrast and modulated fields. Here, a novel real-time marker tracking system based on a convolutional neural network (CNN) classifier was developed and evaluated on retrospectively acquired patient data for MV-based IGRT for prostate cancer patients. Approach. MV images, acquired from 29 volumetric modulated arc therapy (VMAT) prostate cancer patients treated in a multi-institutional clinical trial, were used to train and evaluate a CNN-based marker tracking system. The CNN was trained using labelled MV images from 9 prostate cancer patients (35 fractions) with implanted markers. CNN performance was evaluated on an independent cohort of unseen MV images from 20 patients (78 fractions), using a Precision–Recall curve (PRC), area under the PRC plot (AUC) and sensitivity and specificity. The accuracy of the tracking system was evaluated on the same unseen dataset and quantified by calculating mean absolute (±1 SD) and [1st, 99th] percentiles of the geometric tracking error in treatment beam co-ordinates using manual identification as the ground truth. Main results. The CNN had an AUC of 0.99, sensitivity of 98.31% and specificity of 99.87%. The mean absolute geometric tracking error was 0.30 ± 0.27 and 0.35 ± 0.31 mm in the lateral and superior–inferior directions of the MV images, respectively. The [1st, 99th] percentiles of the error were [−1.03, 0.90] and [−1.12, 1.12] mm in the lateral and SI directions, respectively. Significance. The high classification performance on unseen MV images demonstrates the CNN can successfully identify implanted prostate markers. Furthermore, the sub-millimetre accuracy and precision of the marker tracking system demonstrates potential for adaptation to real-time applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ahmed2023,

title = {Evaluation of deep learning based implanted fiducial markers tracking in pancreatic cancer patients},

author = {Abdella M Ahmed and Maegan Gargett and Levi Madden and Adam Mylonas and Danielle Chrystall and Ryan Brown and Adam Briggs and Trang Nguyen and Paul Keall and Andrew Kneebone and George Hruby and Jeremy Booth},

doi = {10.1088/2057-1976/acb550},

issn = {2057-1976},

year  = {2023},

date = {2023-05-01},

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

volume = {9},

number = {3},

publisher = {IOP Publishing},

abstract = {Abstract

               Real-time target position verification during pancreas stereotactic body radiation therapy (SBRT) is important for the detection of unplanned tumour motions. Fast and accurate fiducial marker segmentation is a Requirement of real-time marker-based verification. Deep learning (DL) segmentation techniques are ideal because they don’t require additional learning imaging or prior marker information (e.g., shape, orientation). In this study, we evaluated three DL frameworks for marker tracking applied to pancreatic cancer patient data. The DL frameworks evaluated were (1) a convolutional neural network (CNN) classifier with sliding window, (2) a pretrained you-only-look-once (YOLO) version-4 architecture, and (3) a hybrid CNN-YOLO. Intrafraction kV images collected during pancreas SBRT treatments were used as training data (44 fractions, 2017 frames). All patients had 1-4 implanted fiducial markers. Each model was evaluated on unseen kV images (42 fractions, 2517 frames). The ground truth was calculated from manual segmentation and triangulation of markers in orthogonal paired kV/MV images. The sensitivity, specificity, and area under the precision-recall curve (AUC) were calculated. In addition, the mean-absolute-error (MAE), root-mean-square-error (RMSE) and standard-error-of-mean (SEM) were calculated for the centroid of the markers predicted by the models, relative to the ground truth. The sensitivity and specificity of the CNN model were 99.41% and 99.69%, respectively. The AUC was 0.9998. The average precision of the YOLO model for different values of recall was 96.49%. The MAE of the three models in the left-right, superior-inferior, and anterior-posterior directions were under 0.88 ± 0.11 mm, and the RMSE were under 1.09 ± 0.12 mm. The detection times per frame on a GPU were 48.3, 22.9, and 17.1 milliseconds for the CNN, YOLO, and CNN-YOLO, respectively. The results demonstrate submillimeter accuracy of marker position predicted by DL models compared to the ground truth. The marker detection time was fast enough to meet the requirements for real-time application.},

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pubstate = {published},

tppubtype = {article}

}

@article{Trada2023c,

title = {Impact of tumour region of interest delineation method for mid-treatment FDG-PET response prediction in head and neck squamous cell carcinoma undergoing radiotherapy},

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

doi = {10.21037/qims-22-798},

issn = {2223-4306},

year  = {2023},

date = {2023-05-00},

journal = {Quant Imaging Med Surg},

volume = {13},

number = {5},

pages = {2822--2836},

publisher = {AME Publishing Company},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Reynolds2023,

title = {Technical note: Extended longitudinal and lateral 3D imaging with a continuous dual‐isocenter CBCT scan},

author = {Tess Reynolds and Sepideh Hatamikia and Yiqun Q. Ma and Owen Dillon and Grace Gang and J. Webster Stayman and Ricky O'Brien},

doi = {10.1002/mp.16234},

issn = {2473-4209},

year  = {2023},

date = {2023-04-00},

journal = {Medical Physics},

volume = {50},

number = {4},

pages = {2372--2379},

publisher = {Wiley},

abstract = {AbstractBackgroundThe clinical benefits of intraoperative cone beam CT (CBCT) during orthopedic procedures include (1) improved accuracy for procedures involving the placement of hardware and (2) providing immediate surgical verification.PurposeOrthopedic interventions often involve long and wide anatomical sites (e.g., lower extremities). Therefore, in order to ensure that the clinical benefits are available to all orthopedic procedures, we investigate the feasibility of a novel imaging trajectory to simultaneously expand the CBCT field‐of‐view longitudinally and laterally.MethodsA continuous dual‐isocenter imaging trajectory was implemented on a clinical robotic CBCT system using additional real‐time control hardware. The trajectory consisted of 200° circular arcs separated by alternating lateral and longitudinal table translations. Due to hardware constraints, the direction of rotation (clockwise/anticlockwise) and lateral table translation (left/right) was reversed every 400°. X‐ray projections were continuously acquired at 15 frames/s throughout all movements. A whole‐body phantom was used to verify the trajectory. As comparator, a series of conventional large volume acquisitions were stitched together. Image quality was quantified using Root Mean Square Deviation (RMSD), Mean Absolute Percentage Deviation (MAPD), Structural Similarity Index Metric (SSIM) and Contrast‐to‐Noise Ratio (CNR).ResultsThe imaging volume produced by the continuous dual‐isocenter trajectory had dimensions of L = 95 cm × W = 45 cm × H = 45 cm. This enabled the hips to the feet of the whole‐body phantom to be captured in approximately half the imaging dose and acquisition time of the 11 stitched conventional acquisitions required to match the longitudinal and lateral imaging dimensions. Compared to the stitched conventional images, the continuous dual‐isocenter acquisition had RMSD of 4.84, MAPD of 6.58% and SSIM of 0.99. The CNR of the continuous dual‐isocenter and stitched conventional acquisitions were 1.998 and 1.999, respectively.ConclusionExtended longitudinal and lateral intraoperative volumetric imaging is feasible on clinical robotic CBCT systems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Waddington2023,

title = {Real‐time radial reconstruction with domain transform manifold learning for MRI‐guided radiotherapy},

author = {David E. J. Waddington and Nicholas Hindley and Neha Koonjoo and Christopher Chiu and Tess Reynolds and Paul Z. Y. Liu and Bo Zhu and Danyal Bhutto and Chiara Paganelli and Paul J. Keall and Matthew S. Rosen},

doi = {10.1002/mp.16224},

issn = {2473-4209},

year  = {2023},

date = {2023-04-00},

journal = {Medical Physics},

volume = {50},

number = {4},

pages = {1962--1974},

publisher = {Wiley},

abstract = {AbstractBackgroundMRI‐guidance techniques that dynamically adapt radiation beams to follow tumor motion in real time will lead to more accurate cancer treatments and reduced collateral healthy tissue damage. The gold‐standard for reconstruction of undersampled MR data is compressed sensing (CS) which is computationally slow and limits the rate that images can be available for real‐time adaptation.PurposeOnce trained, neural networks can be used to accurately reconstruct raw MRI data with minimal latency. Here, we test the suitability of deep‐learning‐based image reconstruction for real‐time tracking applications on MRI‐Linacs.MethodsWe use automated transform by manifold approximation (AUTOMAP), a generalized framework that maps raw MR signal to the target image domain, to rapidly reconstruct images from undersampled radial k‐space data. The AUTOMAP neural network was trained to reconstruct images from a golden‐angle radial acquisition, a benchmark for motion‐sensitive imaging, on lung cancer patient data and generic images from ImageNet. Model training was subsequently augmented with motion‐encoded k‐space data derived from videos in the YouTube‐8M dataset to encourage motion robust reconstruction.ResultsAUTOMAP models fine‐tuned on retrospectively acquired lung cancer patient data reconstructed radial k‐space with equivalent accuracy to CS but with much shorter processing times. Validation of motion‐trained models with a virtual dynamic lung tumor phantom showed that the generalized motion properties learned from YouTube lead to improved target tracking accuracy.ConclusionAUTOMAP can achieve real‐time, accurate reconstruction of radial data. These findings imply that neural‐network‐based reconstruction is potentially superior to alternative approaches for real‐time image guidance applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yamamoto2023,

title = {Four-Dimensional Computed Tomography Ventilation Image-Guided Lung Functional Avoidance Radiation Therapy: A Single-Arm Prospective Pilot Clinical Trial},

author = {Tokihiro Yamamoto and Sven Kabus and Matthieu Bal and Paul J. Keall and Angel Moran and Cari Wright and Stanley H. Benedict and Devin Holland and Nichole Mahaffey and Lihong Qi and Megan E. Daly},

doi = {10.1016/j.ijrobp.2022.11.026},

issn = {0360-3016},

year  = {2023},

date = {2023-04-00},

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

volume = {115},

number = {5},

pages = {1144--1154},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yamamoto2023b,

title = {Four-Dimensional Computed Tomography Ventilation Image-Guided Lung Functional Avoidance Radiation Therapy: A Single-Arm Prospective Pilot Clinical Trial},

author = {Tokihiro Yamamoto and Sven Kabus and Matthieu Bal and Paul J. Keall and Angel Moran and Cari Wright and Stanley H. Benedict and Devin Holland and Nichole Mahaffey and Lihong Qi and Megan E. Daly},

doi = {10.1016/j.ijrobp.2022.11.026},

issn = {0360-3016},

year  = {2023},

date = {2023-04-00},

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

volume = {115},

number = {5},

pages = {1144--1154},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Blake2023,

title = {Thoracic motion‐compensated cone‐beam computed tomography in under 20 seconds on a fast‐rotating linac: A simulation study},

author = {Samuel J. Blake and Owen Dillon and Hilary L. Byrne and Ricky T. O'Brien},

doi = {10.1002/acm2.13909},

issn = {1526-9914},

year  = {2023},

date = {2023-03-00},

journal = {J Applied Clin Med Phys},

volume = {24},

number = {3},

publisher = {Wiley},

abstract = {AbstractBackgroundRapid kV cone‐beam computed tomography (CBCT) scans are achievable in under 20 s on select linear accelerator systems to generate volumetric images in three dimensions (3D). Daily pre‐treatment four‐dimensional CBCT (4DCBCT) is recommended in image‐guided lung radiotherapy to mitigate the detrimental effects of respiratory motion on treatment quality.PurposeTo demonstrate the potential for thoracic 4DCBCT reconstruction using projection data that was simulated using a clinical rapid 3DCBCT acquisition protocol.MethodsWe simulated conventional (1320 projections over 4 min) and rapid (491 projections over 16.6 s) CBCT acquisitions using 4D computed tomography (CT) volumes of 14 lung cancer patients. Conventional acquisition data were reconstructed using the 4D Feldkamp‐Davis‐Kress (FDK) algorithm. Rapid acquisition data were reconstructed using 3DFDK, 4DFDK, and Motion‐Compensated FDK (MCFDK). Image quality was evaluated using Contrast‐to‐Noise Ratio (CNR), Tissue Interface Width (TIW), Root‐Mean‐Square Error (RMSE), and Structural SIMilarity (SSIM).ResultsThe conventional acquisition 4DFDK reconstructions had median phase averaged CNR, TIW, RMSE, and SSIM of 2.96, 8.02 mm, 83.5, and 0.54, respectively. The rapid acquisition 3DFDK reconstructions had median CNR, TIW, RMSE, and SSIM of 2.99, 13.6 mm, 112, and 0.44 respectively. The rapid acquisition MCFDK reconstructions had median phase averaged CNR, TIW, RMSE, and SSIM of 2.98, 10.2 mm, 103, and 0.46, respectively. Rapid acquisition 4DFDK reconstruction quality was insufficient for any practical use due to sparse angular projection sampling.ConclusionsResults suggest that 4D motion‐compensated reconstruction of rapid acquisition thoracic CBCT data are feasible with image quality approaching conventional acquisition CBCT data reconstructed using standard 4DFDK.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Whelan2022,

title = {TopasOpt: An open‐source library for optimization with Topas Monte Carlo},

author = {Brendan Whelan and Billy W. Loo and Jinghui Wang and Paul Keall},

doi = {10.1002/mp.16126},

issn = {2473-4209},

year  = {2023},

date = {2023-02-00},

journal = {Medical Physics},

volume = {50},

number = {2},

pages = {1121--1131},

publisher = {Wiley},

abstract = {AbstractPurposeTo describe and test TopasOpt: a free, open‐source and extensible library for performing mathematical optimization of Monte Carlo simulations in Topas.MethodsTopasOpt enables any Topas model to be transformed into an optimization problem, and any parameter within the model to be treated as an optimization variable. Three case studies are presented. The starting model consists of a 10 MeV electron beam striking a tungsten target. The resulting bremsstrahlung X‐ray spectrum is collimated by a primary and secondary collimator before being scored in a water tank. In the first case study (electron phase space optimization), five parameters describing the electron beam were treated as optimization variables and assigned a random starting value. An objective function was defined based on differences of depth‐dose and profiles in water between the original (ground truth) model and a given model generated by TopasOpt. The problem was solved using Bayesian Optimization and the Nelder‐Mead method. One hundred iterations were run in each case. In the second case study, (collimator geometry optimization), this process was repeated, but three geometric parameters defining the secondary collimator were treated as optimization variables and assigned random starting values, and forty iterations were run. In the third case study, the optimization was repeated with different number of primary particles to study the effect of noise on convergence.ResultsFor case 1 (phase space optimization), both optimization algorithms successfully minimized the objective function, with absolute mean differences in profile dose of 0.4% (Bayesian) and 0.3% (Nelder‐Mead) and 0.2% in depth‐dose for both algorithms. The beam energy was recovered to within 1%, however some parameters had relative errors of up to 171% – a result consistent with the known X‐ray dose is insensitivity to many electron beam parameters. For case 2 (geometry optimization), absolute mean differences in profile dose were 0.6% (Bayesian) and 0.9% (Nelder‐Mead), and 0.5% and 0.9% in depth‐dose. The maximum percentage error in any parameter was 9% with Bayesian Optimization and 28% with Nelder‐Mead. Finally, the Bayesian Optimization algorithm was demonstrated to be robust to moderate levels of noise; when the standard deviation of the objective function was 16% of the mean, the maximum error in any parameter value was 16%, and the absolute mean difference in dose was 0.9% (profile) and 0.8% (depth‐dose).ConclusionsAn open‐source library for optimization with Topas Monte Carlo has been developed, tested, and released. This tool will improve accuracy and efficiency in any situation in which the optimal value of a parameter in a Monte Carlo simulation is unknown. Applications for this tool include (1) The design of new components (2) Reverse engineering of models based on limited experimental or published data, and (3) Tuning of Monte Carlo “hyper parameters” such as variance reduction, physics settings, or scoring parameters.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Whelan2022b,

title = {TopasOpt: An open‐source library for optimization with Topas Monte Carlo},

author = {Brendan Whelan and Billy W. Loo and Jinghui Wang and Paul Keall},

doi = {10.1002/mp.16126},

issn = {2473-4209},

year  = {2023},

date = {2023-02-00},

journal = {Medical Physics},

volume = {50},

number = {2},

pages = {1121--1131},

publisher = {Wiley},

abstract = {AbstractPurposeTo describe and test TopasOpt: a free, open‐source and extensible library for performing mathematical optimization of Monte Carlo simulations in Topas.MethodsTopasOpt enables any Topas model to be transformed into an optimization problem, and any parameter within the model to be treated as an optimization variable. Three case studies are presented. The starting model consists of a 10 MeV electron beam striking a tungsten target. The resulting bremsstrahlung X‐ray spectrum is collimated by a primary and secondary collimator before being scored in a water tank. In the first case study (electron phase space optimization), five parameters describing the electron beam were treated as optimization variables and assigned a random starting value. An objective function was defined based on differences of depth‐dose and profiles in water between the original (ground truth) model and a given model generated by TopasOpt. The problem was solved using Bayesian Optimization and the Nelder‐Mead method. One hundred iterations were run in each case. In the second case study, (collimator geometry optimization), this process was repeated, but three geometric parameters defining the secondary collimator were treated as optimization variables and assigned random starting values, and forty iterations were run. In the third case study, the optimization was repeated with different number of primary particles to study the effect of noise on convergence.ResultsFor case 1 (phase space optimization), both optimization algorithms successfully minimized the objective function, with absolute mean differences in profile dose of 0.4% (Bayesian) and 0.3% (Nelder‐Mead) and 0.2% in depth‐dose for both algorithms. The beam energy was recovered to within 1%, however some parameters had relative errors of up to 171% – a result consistent with the known X‐ray dose is insensitivity to many electron beam parameters. For case 2 (geometry optimization), absolute mean differences in profile dose were 0.6% (Bayesian) and 0.9% (Nelder‐Mead), and 0.5% and 0.9% in depth‐dose. The maximum percentage error in any parameter was 9% with Bayesian Optimization and 28% with Nelder‐Mead. Finally, the Bayesian Optimization algorithm was demonstrated to be robust to moderate levels of noise; when the standard deviation of the objective function was 16% of the mean, the maximum error in any parameter value was 16%, and the absolute mean difference in dose was 0.9% (profile) and 0.8% (depth‐dose).ConclusionsAn open‐source library for optimization with Topas Monte Carlo has been developed, tested, and released. This tool will improve accuracy and efficiency in any situation in which the optimal value of a parameter in a Monte Carlo simulation is unknown. Applications for this tool include (1) The design of new components (2) Reverse engineering of models based on limited experimental or published data, and (3) Tuning of Monte Carlo “hyper parameters” such as variance reduction, physics settings, or scoring parameters.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Carr2023,

title = {A method for beam’s eye view breath-hold monitoring during breast volumetric modulated arc therapy},

author = {Michael A. Carr and Maegan Gargett and Cameron Stanton and Benjamin Zwan and Hilary L. Byrne and Jeremy T. Booth},

doi = {10.1016/j.phro.2023.100419},

issn = {2405-6316},

year  = {2023},

date = {2023-01-00},

journal = {Physics and Imaging in Radiation Oncology},

volume = {25},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Liu2023,

title = {Rapid distortion correction enables accurate magnetic resonance imaging-guided real-time adaptive radiotherapy},

author = {Paul Z. Y Liu and Shanshan Shan and David Waddington and Brendan Whelan and Bin Dong and Gary Liney and Paul Keall},

doi = {10.1016/j.phro.2023.100414},

issn = {2405-6316},

year  = {2023},

date = {2023-01-00},

journal = {Physics and Imaging in Radiation Oncology},

volume = {25},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sengupta2022,

title = {The dosimetric error due to uncorrected tumor rotation during real‐time adaptive prostate stereotactic body radiation therapy},

author = {Chandrima Sengupta and Simon Skouboe and Thomas Ravkilde and Per Rugaard Poulsen and Doan Trang Nguyen and Peter B. Greer and Trevor Moodie and Nicholas Hardcastle and Amy J. Hayden and Sandra Turner and Shankar Siva and Keen‐Hun Tai and Jarad Martin and Jeremy T. Booth and Ricky O'Brien and Paul J. Keall},

doi = {10.1002/mp.16094},

issn = {2473-4209},

year  = {2023},

date = {2023-01-00},

journal = {Medical Physics},

volume = {50},

number = {1},

pages = {20--29},

publisher = {Wiley},

abstract = {AbstractBackgroundDuring prostate stereotactic body radiation therapy (SBRT), prostate tumor translational motion may deteriorate the planned dose distribution. Most of the major advances in motion management to date have focused on correcting this one aspect of the tumor motion, translation. However, large prostate rotation up to 30° has been measured. As the technological innovation evolves toward delivering increasingly precise radiotherapy, it is important to quantify the clinical benefit of translational and rotational motion correction over translational motion correction alone.PurposeThe purpose of this work was to quantify the dosimetric impact of intrafractional dynamic rotation of the prostate measured with a six degrees‐of‐freedom tumor motion monitoring technology.MethodsThe delivered dose was reconstructed including (a) translational and rotational motion and (b) only translational motion of the tumor for 32 prostate cancer patients recruited on a 5‐fraction prostate SBRT clinical trial. Patients on the trial received 7.25 Gy in a treatment fraction. A 5 mm clinical target volume (CTV) to planning target volume (PTV) margin was applied in all directions except the posterior direction where a 3 mm expansion was used. Prostate intrafractional translational motion was managed using a gating strategy, and any translation above the gating threshold was corrected by applying an equivalent couch shift. The residual translational motion is denoted as . Prostate intrafractional rotational motion  was recorded but not corrected. The dose differences from the planned dose due to  + , ΔD( + ) and due to  alone, ΔD(), were then determined for CTV D98, PTV D95, bladder V6Gy, and rectum V6Gy. The residual dose error due to uncorrected rotation,  was then quantified:  = ΔD( + ) ‐ ΔD().ResultsFractional data analysis shows that the dose differences from the plan (both ΔD( + ) and ΔD()) for CTV D98 was less than 5% in all treatment fractions. ΔD( + ) was larger than 5% in one fraction for PTV D95, in one fraction for bladder V6Gy, and in five fractions for rectum V6Gy. Uncorrected rotation,  induced residual dose error, , resulted in less dose to CTV and PTV in 43% and 59% treatment fractions, respectively, and more dose to bladder and rectum in 51% and 53% treatment fractions, respectively. The cumulative dose over five fractions, ∑D( + ) and ∑D(), was always within 5% of the planned dose for all four structures for every patient.ConclusionsThe dosimetric impact of tumor rotation on a large prostate cancer patient cohort was quantified in this study. These results suggest that the standard 3–5 mm CTV‐PTV margin was sufficient to account for the intrafraction prostate rotation observed for this cohort of patients, provided an appropriate gating threshold was applied to correct for translational motion. Residual dose errors due to uncorrected prostate rotation were small in magnitude, which may be corrected using different treatment adaptation strategies to further improve the dosimetric accuracy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sengupta2022b,

title = {The dosimetric error due to uncorrected tumor rotation during real‐time adaptive prostate stereotactic body radiation therapy},

author = {Chandrima Sengupta and Simon Skouboe and Thomas Ravkilde and Per Rugaard Poulsen and Doan Trang Nguyen and Peter B. Greer and Trevor Moodie and Nicholas Hardcastle and Amy J. Hayden and Sandra Turner and Shankar Siva and Keen‐Hun Tai and Jarad Martin and Jeremy T. Booth and Ricky O'Brien and Paul J. Keall},

doi = {10.1002/mp.16094},

issn = {2473-4209},

year  = {2023},

date = {2023-01-00},

journal = {Medical Physics},

volume = {50},

number = {1},

pages = {20--29},

publisher = {Wiley},

abstract = {AbstractBackgroundDuring prostate stereotactic body radiation therapy (SBRT), prostate tumor translational motion may deteriorate the planned dose distribution. Most of the major advances in motion management to date have focused on correcting this one aspect of the tumor motion, translation. However, large prostate rotation up to 30° has been measured. As the technological innovation evolves toward delivering increasingly precise radiotherapy, it is important to quantify the clinical benefit of translational and rotational motion correction over translational motion correction alone.PurposeThe purpose of this work was to quantify the dosimetric impact of intrafractional dynamic rotation of the prostate measured with a six degrees‐of‐freedom tumor motion monitoring technology.MethodsThe delivered dose was reconstructed including (a) translational and rotational motion and (b) only translational motion of the tumor for 32 prostate cancer patients recruited on a 5‐fraction prostate SBRT clinical trial. Patients on the trial received 7.25 Gy in a treatment fraction. A 5 mm clinical target volume (CTV) to planning target volume (PTV) margin was applied in all directions except the posterior direction where a 3 mm expansion was used. Prostate intrafractional translational motion was managed using a gating strategy, and any translation above the gating threshold was corrected by applying an equivalent couch shift. The residual translational motion is denoted as . Prostate intrafractional rotational motion  was recorded but not corrected. The dose differences from the planned dose due to  + , ΔD( + ) and due to  alone, ΔD(), were then determined for CTV D98, PTV D95, bladder V6Gy, and rectum V6Gy. The residual dose error due to uncorrected rotation,  was then quantified:  = ΔD( + ) ‐ ΔD().ResultsFractional data analysis shows that the dose differences from the plan (both ΔD( + ) and ΔD()) for CTV D98 was less than 5% in all treatment fractions. ΔD( + ) was larger than 5% in one fraction for PTV D95, in one fraction for bladder V6Gy, and in five fractions for rectum V6Gy. Uncorrected rotation,  induced residual dose error, , resulted in less dose to CTV and PTV in 43% and 59% treatment fractions, respectively, and more dose to bladder and rectum in 51% and 53% treatment fractions, respectively. The cumulative dose over five fractions, ∑D( + ) and ∑D(), was always within 5% of the planned dose for all four structures for every patient.ConclusionsThe dosimetric impact of tumor rotation on a large prostate cancer patient cohort was quantified in this study. These results suggest that the standard 3–5 mm CTV‐PTV margin was sufficient to account for the intrafraction prostate rotation observed for this cohort of patients, provided an appropriate gating threshold was applied to correct for translational motion. Residual dose errors due to uncorrected prostate rotation were small in magnitude, which may be corrected using different treatment adaptation strategies to further improve the dosimetric accuracy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Brighi2022b,

title = {A novel semiautomated method for background activity and biological tumour volume definition to improve standardisation of 18F-FET PET imaging in glioblastoma},

author = {Caterina Brighi and Simon Puttick and Shenpeng Li and Paul Keall and Katherine Neville and David Waddington and Pierrick Bourgeat and Ashley Gillman and Michael Fay},

doi = {10.1186/s40658-022-00438-2},

issn = {2197-7364},

year  = {2022},

date = {2022-12-00},

journal = {EJNMMI Phys},

volume = {9},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {Abstract

                Background

                Multicentre clinical trials evaluating the role of 18F-Fluoroethyl-l-tyrosine (18F-FET) PET as a diagnostic biomarker in glioma management have highlighted a need for standardised methods of data analysis. 18F-FET uptake normalised against background in the contralateral brain is a standard imaging technique to delineate the biological tumour volume (BTV). Quantitative analysis of 18F-FET PET images requires a consistent and robust background activity. Currently, defining background activity involves the manual selection of an arbitrary region of interest, a process that is subject to large variability. This study aims to eliminate methodological errors in background activity definition through the introduction of a semiautomated method for region of interest selection. A new method for background activity definition, involving the semiautomated generation of mirror-image (MI) reference regions, was compared with the current state-of-the-art method, involving manually drawing crescent-shape (gCS) reference regions. The MI and gCS methods were tested by measuring values of background activity and resulting BTV of 18F-FET PET scans of ten patients with recurrent glioblastoma multiforme generated from inputs provided by seven readers. To assess intra-reader variability, each scan was evaluated six times by each reader. Intra- and inter-reader variability in background activity and BTV definition was assessed by means of coefficient of variation.

              

                Results

                Compared to the gCS method, the MI method showed significantly lower intra- and inter-reader variability both in background activity and in BTV definition.

              

                Conclusions

                The proposed semiautomated MI method minimises intra- and inter-reader variability, providing a valuable approach for standardisation of 18F-FET PET quantitative parameters.

                Trial registration ANZCTR, ACTRN12618001346268. Registered 9 August 2018, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374253

              },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Goodburn2022,

title = {The future of MRI in radiation therapy: Challenges and opportunities for the MR community},

author = {Rosie J. Goodburn and Marielle E. P. Philippens and Thierry L. Lefebvre and Aly Khalifa and Tom Bruijnen and Joshua N. Freedman and David E. J. Waddington and Eyesha Younus and Eric Aliotta and Gabriele Meliadò and Teo Stanescu and Wajiha Bano and Ali Fatemi‐Ardekani and Andreas Wetscherek and Uwe Oelfke and Nico van den Berg and Ralph P. Mason and Petra J. van Houdt and James M. Balter and Oliver J. Gurney‐Champion},

doi = {10.1002/mrm.29450},

issn = {1522-2594},

year  = {2022},

date = {2022-12-00},

journal = {Magnetic Resonance in Med},

volume = {88},

number = {6},

pages = {2592--2608},

publisher = {Wiley},

abstract = {AbstractRadiation therapy is a major component of cancer treatment pathways worldwide. The main aim of this treatment is to achieve tumor control through the delivery of ionizing radiation while preserving healthy tissues for minimal radiation toxicity. Because radiation therapy relies on accurate localization of the target and surrounding tissues, imaging plays a crucial role throughout the treatment chain. In the treatment planning phase, radiological images are essential for defining target volumes and organs‐at‐risk, as well as providing elemental composition (e.g., electron density) information for radiation dose calculations. At treatment, onboard imaging informs patient setup and could be used to guide radiation dose placement for sites affected by motion. Imaging is also an important tool for treatment response assessment and treatment plan adaptation. MRI, with its excellent soft tissue contrast and capacity to probe functional tissue properties, holds great untapped potential for transforming treatment paradigms in radiation therapy. The MR in Radiation Therapy ISMRM Study Group was established to provide a forum within the MR community to discuss the unmet needs and fuel opportunities for further advancement of MRI for radiation therapy applications. During the summer of 2021, the study group organized its first virtual workshop, attended by a diverse international group of clinicians, scientists, and clinical physicists, to explore our predictions for the future of MRI in radiation therapy for the next 25 years. This article reviews the main findings from the event and considers the opportunities and challenges of reaching our vision for the future in this expanding field.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Keall2022b,

title = {ICRU REPORT 97: MRI-Guided Radiation Therapy Using MRI-Linear Accelerators},

author = {Paul J. Keall and Carri K. Glide-Hurst and Minsong Cao and Percy Lee and Brad Murray and Bas W. Raaymakers and Alison Tree and Uulke A. van der Heide},

doi = {10.1177/14736691221141950},

issn = {1742-3422},

year  = {2022},

date = {2022-12-00},

journal = {J�ICRU},

volume = {22},

number = {1},

pages = {1--100},

publisher = {SAGE Publications},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Morton2022,

title = {CArdiac and REspiratory adaptive Computed Tomography (CARE-CT): a proof-of-concept digital phantom study},

author = {Natasha Morton and Paul Keall and Ricky O’Brien and Tess Reynolds},

doi = {10.1007/s13246-022-01193-5},

issn = {2662-4737},

year  = {2022},

date = {2022-12-00},

journal = {Phys Eng Sci Med},

volume = {45},

number = {4},

pages = {1257--1271},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractCurrent respiratory 4DCT imaging for high-dose rate thoracic radiotherapy treatments are negatively affected by the complex interaction of cardiac and respiratory motion. We propose an imaging method to reduce artifacts caused by thoracic motion, CArdiac and REspiratory adaptive CT (CARE-CT), that monitors respiratory motion and ECG signals in real-time, triggering CT acquisition during combined cardiac and respiratory bins. Using a digital phantom, conventional 4DCT and CARE-CT acquisitions for nineteen patient-measured physiological traces were simulated. Ten respiratory bins were acquired for conventional 4DCT scans and ten respiratory bins during cardiac diastole were acquired for CARE-CT scans. Image artifacts were quantified for 10 common thoracic organs at risk (OAR) substructures using the differential normalized cross correlation between axial slices (ΔNCC), mean squared error (MSE) and sensitivity. For all images, on average, CARE-CT improved the ΔNCC for 18/19 and the MSE and sensitivity for all patient traces. The ΔNCC was reduced for all cardiac OARs (mean reduction 21%). The MSE was reduced for all OARs (mean reduction 36%). In the digital phantom study, the average scan time was increased from 1.8 ± 0.4 min to 7.5 ± 2.2 min with a reduction in average beam on time from 98 ± 28 s to 45 s using CARE-CT compared to conventional 4DCT. The proof-of-concept study indicates the potential for CARE-CT to image the thorax in real-time during the cardiac and respiratory cycle simultaneously, to reduce image artifacts for common thoracic OARs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Smith2022,

title = {Nanoparticles for MRI-guided radiation therapy: a review},

author = {Leon Smith and Hilary L. Byrne and David Waddington and Zdenka Kuncic},

doi = {10.1186/s12645-022-00145-8},

issn = {1868-6966},

year  = {2022},

date = {2022-12-00},

journal = {Cancer Nano},

volume = {13},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractThe development of nanoparticle agents for MRI-guided radiotherapy is growing at an increasing pace, with clinical trials now underway and many pre-clinical evaluation studies ongoing. Gadolinium and iron-oxide-based nanoparticles remain the most clinically advanced nanoparticles to date, although several promising candidates are currently under varying stages of development. Goals of current and future generation nanoparticle-based contrast agents for MRI-guided radiotherapy include achieving positive signal contrast on T1-weighted MRI scans, local radiation enhancement at clinically relevant concentrations and, where applicable, avoidance of uptake by the reticuloendothelial system. Exploiting the enhanced permeability and retention effect or the use of active targeting ligands on nanoparticle surfaces is utilised to promote tumour uptake. This review outlines the current status of promising nanoparticle agents for MRI-guided radiation therapy, including several platforms currently undergoing clinical evaluation or at various stages of the pre-clinical development process. Challenges facing nanoparticle agents and possible avenues for current and future development are discussed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Whelan2022c,

title = {Bayesian optimization to design a novel x‐ray shaping device},

author = {Brendan Whelan and Stefania Trovati and Jinghui Wang and Rebecca Fahrig and Peter G Maxim and Adi Hanuka and Muhammad Shumail and Sami Tantawi and Julian Merrick and Joseph Perl and Paul Keall and Billy W Loo Jr},

doi = {10.1002/mp.15887},

issn = {2473-4209},

year  = {2022},

date = {2022-12-00},

journal = {Medical Physics},

volume = {49},

number = {12},

pages = {7623--7637},

publisher = {Wiley},

abstract = {AbstractPurposeIn radiation therapy, x‐ray dose must be precisely sculpted to the tumor, while simultaneously avoiding surrounding organs at risk. This requires modulation of x‐ray intensity in space and/or time. Typically, this is achieved using a multi leaf collimator (MLC)—a complex mechatronic device comprising over one hundred individually powered tungsten ‘leaves’ that move in or out of the radiation field as required. Here, an all‐electronic x‐ray collimation concept with no moving parts is presented, termed “SPHINX”: Scanning Pencil‐beam High‐speed Intensity‐modulated X‐ray source. SPHINX utilizes a spatially distributed bremsstrahlung target and collimator array in conjunction with magnetic scanning of a high energy electron beam to generate a plurality of small x‐ray “beamlets.”MethodsA simulation framework was developed in Topas Monte Carlo incorporating a phase space electron source, transport through user defined magnetic fields, bremsstrahlung x‐ray production, transport through a SPHINX collimator, and dose in water. This framework was completely parametric, meaning a simulation could be built and run for any supplied geometric parameters. This functionality was coupled with Bayesian optimization to find the best parameter set based on an objective function which included terms to maximize dose rate for a user defined beamlet width while constraining inter‐channel cross talk and electron contamination. Designs for beamlet widths of 5, 7, and 10 mm2 were generated. Each optimization was run for 300 iterations and took approximately 40 h on a 24‐core computer. For the optimized 7‐mm model, a simulation of all beamlets in water was carried out including a linear scanning magnet calibration simulation. Finally, a back‐of‐envelope dose rate formalism was developed and used to estimate dose rate under various conditions.ResultsThe optimized 5–, 7–, and 10‐mm models had beamlet widths of 5.1 , 7.2 , and 10.1 mm2 and dose rates of 3574, 6351, and 10 015 Gy/C, respectively. The reduction in dose rate for smaller beamlet widths is a result of both increased collimation and source occlusion. For the simulation of all beamlets in water, the scanning magnet calibration reduced the offset between the collimator channels and beam centroids from 2.9 ±1.9 mm to 0.01 ±0.03 mm. A slight reduction in dose rate of approximately 2% per degree of scanning angle was observed. Based on a back‐of‐envelope dose rate formalism, SPHINX in conjunction with next‐generation linear accelerators has the potential to achieve substantially higher dose rates than conventional MLC‐based delivery, with delivery of an intensity modulated 100 × 100 mm2 field achievable in 0.9 to 10.6 s depending on the beamlet widths used.ConclusionsBayesian optimization was coupled with Monte Carlo modeling to generate SPHINX geometries for various beamlet widths. A complete Monte Carlo simulation for one of these designs was developed, including electron beam transport of all beamlets through scanning magnets, x‐ray production and collimation, and dose in water. These results demonstrate that SPHINX is a promising candidate for sculpting radiation dose with no moving parts, and has the potential to vastly improve both the speed and robustness of radiotherapy delivery. A multi‐beam SPHINX system may be a candidate for delivering magavoltage FLASH RT in humans.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Morton2022b,

title = {System requirements to improve adaptive 4-dimensional computed tomography (4D CT) imaging},

author = {Natasha Morton and Ricky O’Brien and Paul Keall and Tess Reynolds},

doi = {10.1088/2057-1976/ac9849},

issn = {2057-1976},

year  = {2022},

date = {2022-11-01},

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

volume = {8},

number = {6},

publisher = {IOP Publishing},

abstract = {Abstract

               Four-Dimensional Computed Tomography (4D CT) is of increasing importance in stereotactic body radiotherapy (SBRT) treatments affected by respiratory motion. However, 4D CT images are commonly impacted by irregular breathing, causing image artifacts that can propagate through to treatment, negatively effecting local control. REspiratory Adaptive CT (REACT) is a real-time gating method demonstrated to reduce motion artifacts by avoiding imaging during irregular respiration. Despite artifact reduction seen through in silico and clinical phantom-based studies, REACT has not been able to remove all artifacts. Here, we explore several hardware and software latencies (gantry rotation time, couch shifts, acquisition delays and phase calculation method) inherently linked to REACT and 4D CT in general and investigate their contribution to artifacts beyond those caused by irregular breathing. Imaging was simulated using the digital extended cardiac-torso (XCAT) phantom for fifty patient-measured respiratory traces. Imaging protocols included conventional cine 4D CT and five REACT scans with systematically varied parameters to test the effect of different latencies on artifacts. Artifacts were quantified by comparing the image normalized cross correlation across couch transition points and determining the volume error compared to a static phantom ground truth both as a total error and individually across pixel rows in the main plane of motion. Artifacts were determined for each lung, the whole heart and lung tumour and were compared back to conventional 4D CT and REACT with standard clinical scanning parameters. The gantry rotation time and acquisition delay were found to have the largest impact on reducing image artifacts and should be the focus of future development. The phase calculation method was also found to influence motion artifacts and should potentially be assessed on a patient-to-patient basis. Finally, the correlation between an increase in artifacts and baseline drift suggests that longer scan times allowing drift to occur may impact image quality.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ball2022,

title = {Results from the AAPM Task Group 324 respiratory motion management in radiation oncology survey},

author = {Helen J. Ball and Lakshmi Santanam and Suresh Senan and James A. Tanyi and Marcel van Herk and Paul J. Keall},

doi = {10.1002/acm2.13810},

issn = {1526-9914},

year  = {2022},

date = {2022-11-00},

journal = {J Applied Clin Med Phys},

volume = {23},

number = {11},

publisher = {Wiley},

abstract = {AbstractPurposeTo quantify the clinical practice of respiratory motion management in radiation oncology.MethodsA respiratory motion management survey was designed and conducted based on clinician survey guidelines. The survey was administered to American Association of Physicists in Medicine (AAPM) members on 17 August 2020 and closed on 13 September 2020.ResultsA total of 527 respondents completed the entire survey and 651 respondents completed part of the survey, with the partially completed surveys included in the analysis. Overall, 84% of survey respondents used deep inspiration breath hold for left‐sided breast cancer. Overall, 83% of respondents perceived respiratory motion management for thoracic and abdominal cancer radiotherapy patients to be either very important or required. Overall, 95% of respondents used respiratory motion management for thoracic and abdominal sites, with 36% of respondents using respiratory motion management for at least 90% of thoracic and abdominal patients. The majority (60%) of respondents used the internal target volume method to treat thoracic and abdominal cancer patients, with 25% using breath hold or abdominal compression and 13% using gating or tracking.ConclusionsA respiratory motion management survey has been completed by AAPM members. Respiratory motion management is generally considered very important or required and is widely used for breast, thoracic, and abdominal cancer treatments.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hewson2022,

title = {Optimising multi-target multileaf collimator tracking using real-time dose for locally advanced prostate cancer patients},

author = {Emily A Hewson and Doan Trang Nguyen and Andrew Le and Jeremy T Booth and Paul J Keall and Lars Mejnertsen},

doi = {10.1088/1361-6560/ac8967},

issn = {1361-6560},

year  = {2022},

date = {2022-09-21},

journal = {Phys. Med. Biol.},

volume = {67},

number = {18},

publisher = {IOP Publishing},

abstract = {Abstract

               

                  Objective. The accuracy of radiotherapy for patients with locally advanced cancer is compromised by independent motion of multiple targets. To date, MLC tracking approaches have used 2D geometric optimisation where the MLC aperture shape is simply translated to correspond to the target’s motion, which results in sub-optimal delivered dose. To address this limitation, a dose-optimised multi-target MLC tracking method was developed and evaluated through simulated locally advanced prostate cancer treatments. Approach. A dose-optimised multi-target tracking algorithm that adapts the MLC aperture to minimise 3D dosimetric error was developed for moving prostate and static lymph node targets. A fast dose calculation algorithm accumulated the planned dose to the prostate and lymph node volumes during treatment in real time, and the MLC apertures were recalculated to minimise the difference between the delivered and planned dose with the included motion. Dose-optimised tracking was evaluated by simulating five locally advanced prostate plans and three prostate motion traces with a relative interfraction displacement. The same simulations were performed using geometric-optimised tracking and no tracking. The dose-optimised, geometric-optimised, and no tracking results were compared with the planned doses using a 2%/2 mm γ criterion. Main results. The mean dosimetric error was lowest for dose-optimised MLC tracking, with γ-failure rates of 12% ± 8.5% for the prostate and 2.2% ± 3.2% for the nodes. The γ-failure rates for geometric-optimised MLC tracking were 23% ± 12% for the prostate and 3.6% ± 2.5% for the nodes. When no tracking was used, the γ-failure rates were 37% ± 28% for the prostate and 24% ± 3.2% for the nodes. Significance. This study developed a dose-optimised multi-target MLC tracking method that minimises the difference between the planned and delivered doses in the presence of intrafraction motion. When applied to locally advanced prostate cancer, dose-optimised tracking showed smaller errors than geometric-optimised tracking and no tracking for both the prostate and nodes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lydiard2022,

title = {Atrial fibrillation cardiac radioablation target visibility on magnetic resonance imaging},

author = {Suzanne Lydiard and Beau Pontré and Boris S. Lowe and Paul Keall},

doi = {10.1007/s13246-022-01141-3},

issn = {2662-4737},

year  = {2022},

date = {2022-09-00},

journal = {Phys Eng Sci Med},

volume = {45},

number = {3},

pages = {757--767},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractMagnetic resonance imaging (MRI) guided cardiac radioablation (CR) for atrial fibrillation (AF) is a promising treatment concept. However, the visibility of AF CR targets on MRI acquisitions requires further exploration and MRI sequence and parameter optimization has not yet been performed for this application. This pilot study explores the feasibility of MRI-guided tracking of AF CR targets by evaluating AF CR target visualization on human participants using a selection of 3D and 2D MRI sequences.MRI datasets were acquired in healthy and AF participants using a range of MRI sequences and parameters. MRI acquisition categories included 3D free-breathing acquisitions (3Dacq), 2D breath-hold ECG-gated acquisitions (2DECG-gated), stacks of 2D breath-hold ECG-gated acquisitions which were retrospectively interpolated to 3D datasets (3Dinterp), and 2D breath-hold ungated acquisitions (2Dreal-time). The ease of target delineation and the presence of artifacts were qualitatively analyzed. Image quality was quantitatively analyzed using signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and non-uniformity. Confident 3D target delineation was achievable on all 3Dinterp datasets but was not possible on any of the 3Dacq datasets. Fewer artifacts and significantly better SNR, CNR and non-uniformity metrics were observed with 3Dinterp compared to 3Dacq. 2Dreal-time datasets had slightly lower SNR and CNR than 2DECG-gated and 3Dinterp n datasets. AF CR target visualization on MRI was qualitatively and quantitatively evaluated. The study findings indicate that AF CR target visualization is achievable despite the imaging challenges associated with these targets, warranting further investigation into MRI-guided AF CR treatments.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hatamikia2022,

title = {Source-detector trajectory optimization in cone-beam computed tomography: a comprehensive review on today’s state-of-the-art},

author = {S Hatamikia and A Biguri and G Herl and G Kronreif and T Reynolds and J Kettenbach and T Russ and A Tersol and A Maier and M Figl and J H Siewerdsen and W Birkfellner},

doi = {10.1088/1361-6560/ac8590},

issn = {1361-6560},

year  = {2022},

date = {2022-08-21},

journal = {Phys. Med. Biol.},

volume = {67},

number = {16},

publisher = {IOP Publishing},

abstract = {AbstractCone-beam computed tomography (CBCT) imaging is becoming increasingly important for a wide range of applications such as image-guided surgery, image-guided radiation therapy as well as diagnostic imaging such as breast and orthopaedic imaging. The potential benefits of non-circular source-detector trajectories was recognized in early work to improve the completeness of CBCT sampling and extend the field of view (FOV). Another important feature of interventional imaging is that prior knowledge of patient anatomy such as a preoperative CBCT or prior CT is commonly available. This provides the opportunity to integrate such prior information into the image acquisition process by customized CBCT source-detector trajectories. Such customized trajectories can be designed in order to optimize task-specific imaging performance, providing intervention or patient-specific imaging settings. The recently developed robotic CBCT C-arms as well as novel multi-source CBCT imaging systems with additional degrees of freedom provide the possibility to largely expand the scanning geometries beyond the conventional circular source-detector trajectory. This recent development has inspired the research community to innovate enhanced image quality by modifying image geometry, as opposed to hardware or algorithms. The recently proposed techniques in this field facilitate image quality improvement, FOV extension, radiation dose reduction, metal artifact reduction as well as 3D imaging under kinematic constraints. Because of the great practical value and the increasing importance of CBCT imaging in image-guided therapy for clinical and preclinical applications as well as in industry, this paper focuses on the review and discussion of the available literature in the CBCT trajectory optimization field. To the best of our knowledge, this paper is the first study that provides an exhaustive literature review regarding customized CBCT algorithms and tries to update the community with the clarification of in-depth information on the current progress and future trends.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Grover2022,

title = {Investigating the use of machine learning to generate ventilation images from CT scans},

author = {James Grover and Hilary L. Byrne and Yu Sun and John Kipritidis and Paul Keall},

doi = {10.1002/mp.15688},

issn = {2473-4209},

year  = {2022},

date = {2022-08-00},

journal = {Medical Physics},

volume = {49},

number = {8},

pages = {5258--5267},

publisher = {Wiley},

abstract = {AbstractBackgroundRadiotherapy treatment planning incorporating ventilation imaging can reduce the incidence of radiation‐induced lung injury. The gold‐standard of ventilation imaging, using nuclear medicine, has limitations with respect to availability and cost.PurposeAn alternative type of ventilation imaging to nuclear medicine uses 4DCT (or breath‐hold CT [BHCT] pair) with deformable image registration (DIR) and a ventilation metric to produce a CT ventilation image (CTVI). The purpose of this study is to investigate the application of machine learning as an alternative to DIR‐based methods when producing CTVIs.MethodsA patient dataset of 15 inhale and exhale BHCTs and Galligas PET ventilation images were used to train and test a 2D U‐Net style convolutional neural network. The neural network established relationships between axial input BHCT image pairs and axial labeled Galligas PET images and was evaluated using eightfold cross‐validation. Once trained, the neural network could produce a CTVI from an input BHCT image pair. The CTVIs produced by the neural network were qualitatively assessed visually and quantitatively compared to a Galligas PET ventilation image using a Spearman correlation and Dice similarity coefficient (DSC). The DSC measured the spatial overlap between three segmented equal lung volumes by ventilation (high, medium, and low functioning lung [LFL]).ResultsThe mean Spearman correlation between the CTVIs and the Galligas PET ventilation images was 0.58 ± 0.14. The mean DSC over high, medium, and LFL between the CTVIs and Galligas PET ventilation images was 0.55 ± 0.06. Visually, a systematic overprediction of ventilation within the lung was observed in the CTVIs with respect to the Galligas PET ventilation images, with jagged regions of ventilation in the sagittal and coronal planes.ConclusionsA convolutional neural network was developed that could produce a CTVI from a BHCT image pair, which was then compared with a Galligas PET ventilation image. The performance of this machine learning method was comparable to previous benchmark studies investigating a DIR‐based CTVI, warranting future development, and investigation of applying machine learning to a CTVI.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Martin2022,

title = {Challenges in Glioblastoma Radiomics and the Path to Clinical Implementation},

author = {Philip Martin and Lois Holloway and Peter Metcalfe and Eng-Siew Koh and Caterina Brighi},

doi = {10.3390/cancers14163897},

issn = {2072-6694},

year  = {2022},

date = {2022-08-00},

journal = {Cancers},

volume = {14},

number = {16},

publisher = {MDPI AG},

abstract = {Radiomics is a field of medical imaging analysis that focuses on the extraction of many quantitative imaging features related to shape, intensity and texture. These features are incorporated into models designed to predict important clinical or biological endpoints for patients. Attention for radiomics research has recently grown dramatically due to the increased use of imaging and the availability of large, publicly available imaging datasets. Glioblastoma multiforme (GBM) patients stand to benefit from this emerging research field as radiomics has the potential to assess the biological heterogeneity of the tumour, which contributes significantly to the inefficacy of current standard of care therapy. Radiomics models still require further development before they are implemented clinically in GBM patient management. Challenges relating to the standardisation of the radiomics process and the validation of radiomic models impede the progress of research towards clinical implementation. In this manuscript, we review the current state of radiomics in GBM, and we highlight the barriers to clinical implementation and discuss future validation studies needed to advance radiomics models towards clinical application.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Brighi2022d,

title = {Repeatability of radiotherapy dose-painting prescriptions derived from a multiparametric magnetic resonance imaging model of glioblastoma infiltration},

author = {Caterina Brighi and Niels Verburg and Eng-Siew Koh and Amy Walker and Cathy Chen and Sugendran Pillay and Philip C. de Witt Hamer and Farhannah Aly and Lois C. Holloway and Paul J. Keall and David E.J. Waddington},

doi = {10.1016/j.phro.2022.06.004},

issn = {2405-6316},

year  = {2022},

date = {2022-07-00},

journal = {Physics and Imaging in Radiation Oncology},

volume = {23},

pages = {8--15},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kanawati2022,

title = {Generating patient‐matched 3D‐printed pedicle screw and laminectomy drill guides from Cone Beam CT images: Studies in ovine and porcine cadavers},

author = {Andrew Kanawati and Alex Constantinidis and Zoe Williams and Ricky O'Brien and Tess Reynolds},

doi = {10.1002/mp.15681},

issn = {2473-4209},

year  = {2022},

date = {2022-07-00},

journal = {Medical Physics},

volume = {49},

number = {7},

pages = {4642--4652},

publisher = {Wiley},

abstract = {AbstractBackgroundThe emergence of robotic Cone Beam Computed Tomography (CBCT) imaging systems in trauma departments has enabled 3D anatomical assessment of musculoskeletal injuries, supplementing conventional 2D fluoroscopic imaging for examination, diagnosis, and treatment planning. To date, the primary focus has been on trauma sites in the extremities.PurposeTo determine if CBCT images can be used during the treatment planning process in spinal instrumentation and laminectomy procedures, allowing accurate 3D‐printed pedicle screw and laminectomy drill guides to be generated for the cervical and thoracic spine.MethodsThe accuracy of drill guides generated from CBCT images was assessed using animal cadavers (ovine and porcine). Preoperative scans were acquired using a robotic CBCT C‐arm system, the Siemens ARTIS pheno (Siemens Healthcare, GmbH, Germany). The CBCT images were imported into 3D‐Slicer version 4.10.2 (www.slicer.org) where vertebral models and specific guides were developed and subsequently 3D‐printed. In the ovine cadaver, 11 pedicle screw guides from the T1–T5 and T7–T12 vertebra and six laminectomy guides from the C2–C7 vertebra were planned and printed. In the porcine cadaver, nine pedicle screw guides from the C3–T4 vertebra were planned and printed. For the pedicle screw guides, accuracy was assessed by three observers according to pedicle breach via the Gertzbein–Robbins grading system as well as measured mean axial and sagittal screw error via postoperative CBCT and CT scans. For the laminectomies, the guides were designed to leave 1 mm of lamina. The average thickness of the lamina at the mid‐point was used to assess the accuracy of the guides, measured via postoperative CBCT and CT scans from three observers. For all measurements, the intraclass correlation coefficient (ICC) was calculated to determine observer reliability.ResultsCompared with the planned screw angles for both the ovine and porcine procedures (n = 32), the mean axial and sagittal screw error measured on the postoperative CBCT scans from three observers were 3.9 ± 1.9° and 1.8 ± 0.8°, respectively. The ICC among the observes was 0.855 and 0.849 for the axial and sagittal measurements, respectively, indicating good reliability. In the ovine cadaver, directly comparing the measured axial and sagittal screw angle of the visible screws (n = 14) in the postoperative CBCT and conventional CT scans from three observers revealed an average difference 1.9 ± 1.0° in axial angle and 1.8 ± 1.0° in the sagittal angle. The average thickness of the lamina at the middle of each vertebra, as measured on‐screen in the postoperative CBCT scans by three observes was 1.6 ± 0.2 mm. The ICC among observers was 0.693, indicating moderate reliability. No lamina breaches were observed in the postoperative images.ConclusionHere, CBCT images have been used to generate accurate 3D‐printed pedicle screw and laminectomy drill guides for use in the cervical and thoracic spine. The results demonstrate sufficient precision compared with those previously reported, generated from standard preoperative CT and MRI scans, potentially expanding the treatment planning capabilities of robotic CBCT imaging systems in trauma departments and operating rooms.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Keall2022,

title = {Integrated MRI-guided radiotherapy — opportunities and challenges},

author = {Paul J. Keall and Caterina Brighi and Carri Glide-Hurst and Gary Liney and Paul Z. Y. Liu and Suzanne Lydiard and Chiara Paganelli and Trang Pham and Shanshan Shan and Alison C. Tree and Uulke A. van der Heide and David E. J. Waddington and Brendan Whelan},

doi = {10.1038/s41571-022-00631-3},

issn = {1759-4782},

year  = {2022},

date = {2022-07-00},

journal = {Nat Rev Clin Oncol},

volume = {19},

number = {7},

pages = {458--470},

publisher = {Springer Science and Business Media LLC},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Keall2022c,

title = {A systematic review of assessment approaches to predict opioid misuse in people with cancer},

author = {Robyn Keall and Paul Keall and Carly Kiani and Tim Luckett and Richard McNeill and Melanie Lovell},

doi = {10.1007/s00520-022-06895-w},

issn = {1433-7339},

year  = {2022},

date = {2022-07-00},

journal = {Support Care Cancer},

volume = {30},

number = {7},

pages = {5645--5658},

publisher = {Springer Science and Business Media LLC},

abstract = {Abstract

                Context

                Cancer prevalence is increasing, with many patients requiring opioid analgesia. Clinicians need to ensure patients receive adequate pain relief. However, opioid misuse is widespread, and cancer patients are at risk.

              

                Objectives

                This study aims (1) to identify screening approaches that have been used to assess and monitor risk of opioid misuse in patients with cancer; (2) to compare the prevalence of risk estimated by each of these screening approaches; and (3) to compare risk factors among demographic and clinical variables associated with a positive screen on each of the approaches.

              

                Methods

                Medline, Cochrane Controlled Trial Register, PubMed, PsycINFO, and Embase databases were searched for articles reporting opioid misuse screening in cancer patients, along with handsearching the reference list of included articles. Bias was assessed using tools from the Joanna Briggs Suite.

              

                Results

                Eighteen studies met the eligibility criteria, evaluating seven approaches: Urine Drug Test (UDT) (n = 8); the Screener and Opioid Assessment for Patients with Pain (SOAPP) and two variants, Revised and Short Form (n = 6); the Cut-down, Annoyed, Guilty, Eye-opener (CAGE) tool and one variant, Adapted to Include Drugs (n = 6); the Opioid Risk Tool (ORT) (n = 4); Prescription Monitoring Program (PMP) (n = 3); the Screen for Opioid-Associated Aberrant Behavior Risk (SOABR) (n = 1); and structured/specialist interviews (n = 1). Eight studies compared two or more approaches. The rates of risk of opioid misuse in the studied populations ranged from 6 to 65%, acknowledging that estimates are likely to have varied partly because of how specific to opioids the screening approaches were and whether a single or multi-step approach was used. UDT prompted by an intervention or observation of aberrant opioid behaviors (AOB) were conclusive of actual opioid misuse found to be 6.5–24%. Younger age, found in 8/10 studies; personal or family history of anxiety or other mental ill health, found in 6/8 studies; and history of illicit drug use, found in 4/6 studies, showed an increased risk of misuse.

              

                Conclusions

                Younger age, personal or familial mental health history, and history of illicit drug use consistently showed an increased risk of opioid misuse. Clinical suspicion of opioid misuse may be raised by data from PMP or any of the standardized list of AOBs. Clinicians may use SOAPP-R, CAGE-AID, or ORT to screen for increased risk and may use UDT to confirm suspicion of opioid misuse or monitor adherence. More research into this important area is required.

              

                Significance of results

                This systematic review summarized the literature on the use of opioid misuse risk approaches in people with cancer. The rates of reported risk range from 6 to 65%; however, true rate may be closer to 6.5–24%. Younger age, personal or familial mental health history, and history of illicit drug use consistently showed an increased risk of opioid misuse. Clinicians may choose from several approaches. Limited data are available on feasibility and patient experience.

                PROSPERO registration number.

                CRD42020163385.

              },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Begg2022,

title = {Experimental characterisation of the magnetic field correction factor, 

	    





	    kB⃗,

	    

	   for Roos chambers in a parallel MRI-linac},

author = {Jarrad Begg and Urszula Jelen and Paul Keall and Gary Liney and Lois Holloway},

doi = {10.1088/1361-6560/ac66b8},

issn = {1361-6560},

year  = {2022},

date = {2022-05-07},

journal = {Phys. Med. Biol.},

volume = {67},

number = {9},

publisher = {IOP Publishing},

abstract = {Abstract

               

                  Objective. Reference dosimetry on an MRI-linac requires a chamber specific magnetic field correction factor, 

                     





                     

                        

                           

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                        .

                     

                     

                   This work aims to measure the correction factor for a parallel plate chamber on a parallel MRI-linac. Approach. 

                  

                     





                     

                        

                           

                              k

                           

                           

                              

                                 B

                                 ⃗

                              

                           

                        

                     

                     

                   is defined as the ratio of the absorbed dose to water calibration coefficient in the presence of the magnetic field, 

                     





                     

                        

                           

                              N

                           

                           

                              D

                              ,

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                  relative to that under 0 T conditions, 

                     





                     

                        

                           

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                              w

                           

                           

                              0

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                        .

                     

                     

                   

                  

                     





                     

                        

                           

                              k

                           

                           

                              

                                 B

                                 ⃗

                              

                           

                        

                     

                     

                   was measured via a 

                     





                     

                        

                           

                              N

                           

                           

                              D

                              ,

                              w

                           

                        

                     

                     

                   transfer to a field chamber at each magnetic field strength from a chamber with known 

                     





                     

                        

                           

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                              ,

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                   and 

                     





                     

                        

                           

                              k

                           

                           

                              

                                 B

                                 ⃗

                              

                           

                        

                        .

                     

                     

                   This was achieved on the parallel MRI-linac by moving the measurement set-up between a high magnetic field strength region at the MRI-isocentre and a low magnetic field strength region at the end of the bore whilst maintaining consistent set-up and scatter conditions. Three PTW 34001 Roos chambers were investigated as well as a PTW 30013 Farmer used to validate methodology. Main Results. The beam quality used for the measurements of 

                     





                     

                        

                           

                              k

                           

                           

                              

                                 B

                                 ⃗

                              

                           

                        

                     

                     

                   was TPR

                  20/10 = 0.632. The 

                     





                     

                        

                           

                              k

                           

                           

                              

                                 B

                                 ⃗

                              

                           

                        

                     

                     

                   for the PTW Farmer chamber at 1 T on a parallel MRI-linac was 0.993 ± 0.013 (k = 1). The average 

                     





                     

                        

                           

                              k

                           

                           

                              

                                 B

                                 ⃗

                              

                           

                        

                     

                     

                   factor measured for the three Roos chambers on a 1 T parallel MRI-linac was 0.999 ± 0.014 (k = 1). Significance. The results presented are the first measurements of 

                     





                     

                        

                           

                              k

                           

                           

                              

                                 B

                                 ⃗

                              

                           

                        

                     

                     

                   for a Roos chamber on a parallel MRI-linac. The Roos chamber results demonstrate the potential for the chamber as a reference dosimeter in parallel MRI-linacs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Brighi2022c,

title = {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.2022.03.035},

issn = {0168-3659},

year  = {2022},

date = {2022-05-00},

journal = {Journal of Controlled Release},

volume = {345},

pages = {443--463},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pham2022,

title = {Magnetic resonance imaging (MRI) guided proton therapy: A review of the clinical challenges, potential benefits and pathway to implementation},

author = {Trang Thanh Pham and Brendan Whelan and Bradley M. Oborn and Geoff P. Delaney and Shalini Vinod and Caterina Brighi and Michael Barton and Paul Keall},

doi = {10.1016/j.radonc.2022.02.031},

issn = {0167-8140},

year  = {2022},

date = {2022-05-00},

journal = {Radiotherapy and Oncology},

volume = {170},

pages = {37--47},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lau2022,

title = {Reducing 4DCBCT imaging dose and time: exploring the limits of adaptive acquisition and motion compensated reconstruction},

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

doi = {10.1088/1361-6560/ac55a4},

issn = {1361-6560},

year  = {2022},

date = {2022-03-21},

journal = {Phys. Med. Biol.},

volume = {67},

number = {6},

publisher = {IOP Publishing},

abstract = {Abstract

               This study investigates the dose and time limits of adaptive 4DCBCT acquisitions (adaptive-acquisition) compared with current conventional 4DCBCT acquisition (conventional-acquisition). We investigate adaptive-acquisitions as low as 60 projections (∼25 s scan, 6 projections per respiratory phase) in conjunction with emerging image reconstruction methods. 4DCBCT images from 20 patients recruited into the adaptive CT acquisition for personalized thoracic imaging clinical study (NCT04070586) were resampled to simulate faster and lower imaging dose acquisitions. All acquisitions were reconstructed using Feldkamp–Davis–Kress (FDK), McKinnon–Bates (MKB), motion compensated FDK (MCFDK), motion compensated MKB (MCMKB) and simultaneous motion estimation and image reconstruction (SMEIR) algorithms. All reconstructions were compared against conventional-acquisition 4DFDK-reconstruction using Structural SIMilarity Index (SSIM), signal-to-noise ratio (SNR), contrast-to-noise-ratio (CNR), tissue interface sharpness diaphragm (TIS-D), tissue interface sharpness tumor (TIS-T) and center of mass trajectory (COMT) for difference in diaphragm and tumor motion. All reconstruction methods using 110-projection adaptive-acquisition (11 projections per respiratory phase) had a SSIM of greater than 0.92 relative to conventional-acquisition 4DFDK-reconstruction. Relative to conventional-acquisition 4DFDK-reconstruction, 110-projection adaptive-acquisition MCFDK-reconstructions images had 60% higher SNR, 10% higher CNR, 30% higher TIS-T and 45% higher TIS-D on average. The 110-projection adaptive-acquisition SMEIR-reconstruction images had 123% higher SNR, 90% higher CNR, 96% higher TIS-T and 60% higher TIS-D on average. The difference in diaphragm and tumor motion compared to conventional-acquisition 4DFDK-reconstruction was within submillimeter accuracy for all acquisition reconstruction methods. Adaptive-acquisitions resulted in faster scans with lower imaging dose and equivalent or improved image quality compared to conventional-acquisition. Adaptive-acquisition with motion compensated-reconstruction enabled scans with as low as 110 projections to deliver acceptable image quality. This translates into 92% lower imaging dose and 80% less scan time than conventional-acquisition.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Montinola2022,

title = {Investigation of Micron-Scale Radiotherapy Dose Deposition in the Lung: Effect of Magnetic Field and Nanoparticles—a Monte Carlo Simulation},

author = {Denxybel Montinola and Aimee L. McNamara and Zdenka Kuncic and Hilary L. Byrne},

doi = {10.3389/fphy.2022.835016},

issn = {2296-424X},

year  = {2022},

date = {2022-02-17},

journal = {Front. Phys.},

volume = {10},

publisher = {Frontiers Media SA},

abstract = {MRI-Linacs couple magnetic resonance imaging (MRI) with a linear accelerator (Linac) to enable MR-guided radiotherapy. The magnetic field is known to cause inhomogeneities in the pattern of dose deposition at centimeter-scale air-tissue interfaces such as pockets of digestive gas but has not been studied at the micrometer scale of lung alveoli. Nanoparticle radio-enhancement is a novel therapy enhancing the dose deposition pattern where nanoparticles are delivered to the radiation target, with proposed application to lung cancer treatment through inhalation of nebulized nanoparticles. This study reports the first investigation of the effect of a magnetic field on the pattern of dose deposition at the micrometer air-tissue interfaces of alveoli in the lung, and the impact of incorporating nanoparticles. Monte Carlo simulations investigated a single alveolus model irradiated with mono-energetic, uni-directional electrons and a multi-alveoli model irradiated with a realistic beam at depth. The magnetic field was found to produce field-strength dependent hot- and cold-spot dose inhomogeneities in the tissue surrounding a micrometer air cavity irradiated with low energy (100 keV) electrons. The most affected regions exhibited a dose increase of 37.30 ± 1.29% and a decrease of 31.58 ± 1.01% with the application of a 1.5 T magnetic field. The addition of nanoparticles to the interior surface layer of the alveolus air cavity increased energy deposit by a constant ratio dependent on the nanoparticle concentration regardless of magnetic field strength. A similar but less pronounced effect was observed for a multi-alveolus model irradiated at depth by a 6 MV photon beam. This result warrants further investigation into the biological impact of micrometer-scale dose inhomogeneity on tumor response and normal tissue complication probability.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mueller2021,

title = {The markerless lung target tracking AAPM Grand Challenge (MATCH) results},

author = {Marco Mueller and Per Poulsen and Rune Hansen and Wilko Verbakel and Ross Berbeco and Dianne Ferguson and Shinichiro Mori and Lei Ren and John C. Roeske and Lei Wang and Pengpeng Zhang and Paul Keall},

doi = {10.1002/mp.15418},

issn = {2473-4209},

year  = {2022},

date = {2022-02-00},

journal = {Medical Physics},

volume = {49},

number = {2},

pages = {1161--1180},

publisher = {Wiley},

abstract = {AbstractPurposeLung stereotactic ablative body radiotherapy (SABR) is a radiation therapy success story with level 1 evidence demonstrating its efficacy. To provide real‐time respiratory motion management for lung SABR, several commercial and preclinical markerless lung target tracking (MLTT) approaches have been developed. However, these approaches have yet to be benchmarked using a common measurement methodology. This knowledge gap motivated the MArkerless lung target Tracking CHallenge (MATCH). The aim was to localize lung targets accurately and precisely in a retrospective in silico study and a prospective experimental study.MethodsMATCH was an American Association of Physicists in Medicine sponsored Grand Challenge. Common materials for the in silico and experimental studies were the experiment setup including an anthropomorphic thorax phantom with two targets within the lungs, and a lung SABR planning protocol. The phantom was moved rigidly with patient‐measured lung target motion traces, which also acted as ground truth motion. In the retrospective in silico study a volumetric modulated arc therapy treatment was simulated and a dataset consisting of treatment planning data and intra‐treatment kilovoltage (kV) and megavoltage (MV) images for four blinded lung motion traces was provided to the participants. The participants used their MLTT approach to localize the moving target based on the dataset. In the experimental study, the participants received the phantom experiment setup and five patient‐measured lung motion traces. The participants used their MLTT approach to localize the moving target during an experimental SABR phantom treatment. The challenge was open to any participant, and participants could complete either one or both parts of the challenge. For both the in silico and experimental studies the MLTT results were analyzed and ranked using the prospectively defined metric of the percentage of the tracked target position being within 2 mm of the ground truth.ResultsA total of 30 institutions registered and 15 result submissions were received, four for the in silico study and 11 for the experimental study. The participating MLTT approaches were: Accuray CyberKnife (2), Accuray Radixact (2), BrainLab Vero, C‐RAD, and preclinical MLTT (5) on a conventional linear accelerator (Varian TrueBeam). For the in silico study the percentage of the 3D tracking error within 2 mm ranged from 50% to 92%. For the experimental study, the percentage of the 3D tracking error within 2 mm ranged from 39% to 96%.ConclusionsA common methodology for measuring the accuracy of MLTT approaches has been developed and used to benchmark preclinical and commercial approaches retrospectively and prospectively. Several MLTT approaches were able to track the target with sub‐millimeter accuracy and precision. The study outcome paves the way for broader clinical implementation of MLTT. MATCH is live, with datasets and analysis software being available online at https://www.aapm.org/GrandChallenge/MATCH/ to support future research.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mueller2021b,

title = {The markerless lung target tracking AAPM Grand Challenge (MATCH) results},

author = {Marco Mueller and Per Poulsen and Rune Hansen and Wilko Verbakel and Ross Berbeco and Dianne Ferguson and Shinichiro Mori and Lei Ren and John C. Roeske and Lei Wang and Pengpeng Zhang and Paul Keall},

doi = {10.1002/mp.15418},

issn = {2473-4209},

year  = {2022},

date = {2022-02-00},

journal = {Medical Physics},

volume = {49},

number = {2},

pages = {1161--1180},

publisher = {Wiley},

abstract = {AbstractPurposeLung stereotactic ablative body radiotherapy (SABR) is a radiation therapy success story with level 1 evidence demonstrating its efficacy. To provide real‐time respiratory motion management for lung SABR, several commercial and preclinical markerless lung target tracking (MLTT) approaches have been developed. However, these approaches have yet to be benchmarked using a common measurement methodology. This knowledge gap motivated the MArkerless lung target Tracking CHallenge (MATCH). The aim was to localize lung targets accurately and precisely in a retrospective in silico study and a prospective experimental study.MethodsMATCH was an American Association of Physicists in Medicine sponsored Grand Challenge. Common materials for the in silico and experimental studies were the experiment setup including an anthropomorphic thorax phantom with two targets within the lungs, and a lung SABR planning protocol. The phantom was moved rigidly with patient‐measured lung target motion traces, which also acted as ground truth motion. In the retrospective in silico study a volumetric modulated arc therapy treatment was simulated and a dataset consisting of treatment planning data and intra‐treatment kilovoltage (kV) and megavoltage (MV) images for four blinded lung motion traces was provided to the participants. The participants used their MLTT approach to localize the moving target based on the dataset. In the experimental study, the participants received the phantom experiment setup and five patient‐measured lung motion traces. The participants used their MLTT approach to localize the moving target during an experimental SABR phantom treatment. The challenge was open to any participant, and participants could complete either one or both parts of the challenge. For both the in silico and experimental studies the MLTT results were analyzed and ranked using the prospectively defined metric of the percentage of the tracked target position being within 2 mm of the ground truth.ResultsA total of 30 institutions registered and 15 result submissions were received, four for the in silico study and 11 for the experimental study. The participating MLTT approaches were: Accuray CyberKnife (2), Accuray Radixact (2), BrainLab Vero, C‐RAD, and preclinical MLTT (5) on a conventional linear accelerator (Varian TrueBeam). For the in silico study the percentage of the 3D tracking error within 2 mm ranged from 50% to 92%. For the experimental study, the percentage of the 3D tracking error within 2 mm ranged from 39% to 96%.ConclusionsA common methodology for measuring the accuracy of MLTT approaches has been developed and used to benchmark preclinical and commercial approaches retrospectively and prospectively. Several MLTT approaches were able to track the target with sub‐millimeter accuracy and precision. The study outcome paves the way for broader clinical implementation of MLTT. MATCH is live, with datasets and analysis software being available online at https://www.aapm.org/GrandChallenge/MATCH/ to support future research.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Brighi2022,

title = {An investigation of the conformity, feasibility, and expected clinical benefits of multiparametric MRI-guided dose painting radiotherapy in glioblastoma},

author = {Caterina Brighi and Paul J Keall and Lois C Holloway and Amy Walker and Brendan Whelan and Philip C de Witt Hamer and Niels Verburg and Farhannah Aly and Cathy Chen and Eng-Siew Koh and David E J Waddington},

doi = {10.1093/noajnl/vdac134},

issn = {2632-2498},

year  = {2022},

date = {2022-01-01},

volume = {4},

number = {1},

publisher = {Oxford University Press (OUP)},

abstract = {Abstract

               

                  Background

                  New technologies developed to improve survival outcomes for glioblastoma (GBM) continue to have limited success. Recently, image-guided dose painting (DP) radiotherapy has emerged as a promising strategy to increase local control rates. In this study, we evaluate the practical application of a multiparametric MRI model of glioma infiltration for DP radiotherapy in GBM by measuring its conformity, feasibility, and expected clinical benefits against standard of care treatment.

               

               

                  Methods

                  Maps of tumor probability were generated from perfusion/diffusion MRI data from 17 GBM patients via a previously developed model of GBM infiltration. Prescriptions for DP were linearly derived from tumor probability maps and used to develop dose optimized treatment plans. Conformity of DP plans to dose prescriptions was measured via a quality factor. Feasibility of DP plans was evaluated by dose metrics to target volumes and critical brain structures. Expected clinical benefit of DP plans was assessed by tumor control probability. The DP plans were compared to standard radiotherapy plans.

               

               

                  Results

                  The conformity of the DP plans was >90%. Compared to the standard plans, DP (1) did not affect dose delivered to organs at risk; (2) increased mean and maximum dose and improved minimum dose coverage for the target volumes; (3) reduced minimum dose within the radiotherapy treatment margins; (4) improved local tumor control probability within the target volumes for all patients.

               

               

                  Conclusions

                  A multiparametric MRI model of GBM infiltration can enable conformal, feasible, and potentially beneficial dose painting radiotherapy plans.

               },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mueller2022,

title = {MArkerless image Guidance using Intrafraction Kilovoltage x-ray imaging (MAGIK): study protocol for a phase I interventional study for lung cancer radiotherapy},

author = {Marco Mueller and Jeremy Booth and Adam Briggs and Dasantha Jayamanne and Vanessa Panettieri and Sashendra Senthi and Chun-Chien Shieh and Paul Keall},

doi = {10.1136/bmjopen-2021-057135},

issn = {2044-6055},

year  = {2022},

date = {2022-01-00},

journal = {BMJ Open},

volume = {12},

number = {1},

publisher = {BMJ},

abstract = {IntroductionIn radiotherapy, tumour tracking leads the radiation beam to accurately target the tumour while it moves in a complex and unpredictable way due to respiration. Several tumour tracking techniques require the implantation of fiducial markers around the tumour, a procedure that involves unnecessary risks and costs. Markerless tumour tracking (MTT) negates the need for implanted markers, potentially enabling accurate and optimal radiotherapy in a non-invasive way.Methods and analysisWe will perform a phase I interventional trial called MArkerless image Guidance using Intrafraction Kilovoltage x-ray imaging (MAGIK) to investigate the technical feasibility of the MTT technology developed at the University of Sydney (sponsor). 30 participants will undergo the current standard of care lung stereotactic ablative radiation therapy, with the exception that kilovoltage X-ray images will be acquired continuously during treatment delivery to enable MTT. If MTT indicates that the mean lung tumour position has shifted >3 mm, a warning message will be displayed to indicate the need for a treatment intervention. The radiation therapist will then pause the treatment, shift the treatment couch to account for the shift in tumour position and resume the treatment. Participants will be implanted with fiducial markers, which act as the ground truth for evaluating the accuracy of MTT. MTT is considered feasible if the tracking accuracy is <3 mm in each dimension for >80% of the treatment time.Ethics and disseminationThe MAGIK trial has received ethical approval from The Alfred Human Research Ethics Committee and has been registered with ClinicalTrials.gov with the Identifier: NCT04086082. Estimated time of first recruitment is early 2022. The study recruitment and data analysis phases will be performed concurrently. Treatment for all 30 participants is expected to be completed within 2 years and participant follow-up within a total duration of 7 years. Findings will be disseminated through peer-reviewed publications and conference presentations.Trial registration numberNCT04086082; Pre-result.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Reynolds2022,

title = {Extended Intraoperative Longitudinal 3-Dimensional Cone Beam Computed Tomography Imaging With a Continuous Multi-Turn Reverse Helical Scan},

author = {Tess Reynolds and Yiqun Q. Ma and Andrew J. Kanawati and Alex Constantinidis and Zoe Williams and Grace Gang and Owen Dillon and Tom Russ and Wenying Wang and Tina Ehtiati and Clifford R. Weiss and Nicholas Theodore and Jeffery H. Siewerdsen and Joseph W. Stayman and Ricky T. O'Brien},

doi = {10.1097/rli.0000000000000885},

issn = {1536-0210},

year  = {2022},

date = {2022-00-00},

journal = {Invest Radiol},

volume = {57},

number = {11},

pages = {764--772},

publisher = {Ovid Technologies (Wolters Kluwer Health)},

abstract = {

            Objectives

            Cone beam computed tomography (CBCT) imaging is becoming an indispensable intraoperative tool; however, the current field of view prevents visualization of long anatomical sites, limiting clinical utility. Here, we demonstrate the longitudinal extension of the intraoperative CBCT field of view using a multi-turn reverse helical scan and assess potential clinical utility in interventional procedures.

          

          

            Materials and Methods

            A fixed-room robotic CBCT imaging system, with additional real-time control, was used to implement a multi-turn reverse helical scan. The scan consists of C-arm rotation, through a series of clockwise and anticlockwise rotations, combined with simultaneous programmed table translation. The motion properties and geometric accuracy of the multi-turn reverse helical imaging trajectory were examined using a simple geometric phantom. To assess potential clinical utility, a pedicle screw posterior fixation procedure in the thoracic spine from T1 to T12 was performed on an ovine cadaver. The multi-turn reverse helical scan was used to provide postoperative assessment of the screw insertion via cortical breach grading and mean screw angle error measurements (axial and sagittal) from 2 observers. For all screw angle measurements, the intraclass correlation coefficient was calculated to determine observer reliability.

          

          

            Results

            The multi-turn reverse helical scans took 100 seconds to complete and increased the longitudinal coverage by 370% from 17 cm to 80 cm. Geometric accuracy was examined by comparing the measured to actual dimensions (0.2 ± 0.1 mm) and angles (0.2 ± 0.1 degrees) of a simple geometric phantom, indicating that the multi-turn reverse helical scan provided submillimeter and degree accuracy with no distortion. During the pedicle screw procedure in an ovine cadaver, the multi-turn reverse helical scan identified 4 cortical breaches, confirmed via the postoperative CT scan. Directly comparing the screw insertion angles (n = 22) measured in the postoperative multi-turn reverse helical and CT scans revealed an average difference of 3.3 ± 2.6 degrees in axial angle and 1.9 ± 1.5 degrees in the sagittal angle from 2 expert observers. The intraclass correlation coefficient was above 0.900 for all measurements (axial and sagittal) across all scan types (conventional CT, multi-turn reverse helical, and conventional CBCT), indicating excellent reliability between observers.

          

          

            Conclusions

            Extended longitudinal field-of-view intraoperative 3-dimensional imaging with a multi-turn reverse helical scan is feasible on a clinical robotic CBCT imaging system, enabling long anatomical sites to be visualized in a single image, including in the presence of metal hardware.

          },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lee2021,

title = {Study protocol of the LARK (TROG 17.03) clinical trial: a phase II trial investigating the dosimetric impact of Liver Ablative Radiotherapy using Kilovoltage intrafraction monitoring},

author = {Yoo Young Dominique Lee and Doan Trang Nguyen and Trevor Moodie and Ricky O’Brien and Anne McMaster and Andrew Hickey and Nicole Pritchard and Per Poulsen and Elizaveta Mitkina Tabaksblat and Britta Weber and Esben Worm and David Pryor and Julie Chu and Nicholas Hardcastle and Jeremy Booth and Val Gebski and Tim Wang and Paul Keall},

doi = {10.1186/s12885-021-08184-x},

issn = {1471-2407},

year  = {2021},

date = {2021-12-00},

journal = {BMC Cancer},

volume = {21},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {Abstract

                Background

                Stereotactic Ablative Body Radiotherapy (SABR) is a non-invasive treatment which allows delivery of an ablative radiation dose with high accuracy and precision. SABR is an established treatment for both primary and secondary liver malignancies, and technological advances have improved its efficacy and safety. Respiratory motion management to reduce tumour motion and image guidance to achieve targeting accuracy are crucial elements of liver SABR. This phase II multi-institutional TROG 17.03 study, Liver Ablative Radiotherapy using Kilovoltage intrafraction monitoring (LARK), aims to investigate and assess the dosimetric impact of the KIM real-time image guidance technology. KIM utilises standard linear accelerator equipment and therefore has the potential to be a widely available real-time image guidance technology for liver SABR.

              

                Methods

                Forty-six patients with either hepatocellular carcinoma or oligometastatic disease to the liver suitable for and treated with SABR using Kilovoltage Intrafraction Monitoring (KIM) guidance will be included in the study. The dosimetric impact will be assessed by quantifying accumulated patient dose distribution with or without the KIM intervention. The patient treatment outcomes of local control, toxicity and quality of life will be measured.

              

                Discussion

                Liver SABR is a highly effective treatment, but precise dose delivery is challenging due to organ motion. Currently, there is a lack of widely available options for performing real-time tumour localisation to assist with accurate delivery of liver SABR. This study will provide an assessment of the impact of KIM as a potential solution for real-time image guidance in liver SABR.

              

                Trial registration

                This trial was registered on December 7th 2016 on ClinicalTrials.gov under the trial-ID NCT02984566.

              },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Nguyen2021,

title = {A real-time IGRT method using a Kalman filter framework to extract 3D positions from 2D projections},

author = {Doan Trang Nguyen and Paul Keall and Jeremy Booth and Chun-Chien Shieh and Per Poulsen and Ricky O’Brien},

doi = {10.1088/1361-6560/ac06e3},

issn = {1361-6560},

year  = {2021},

date = {2021-11-07},

journal = {Phys. Med. Biol.},

volume = {66},

number = {21},

publisher = {IOP Publishing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}