Publications

@article{Steiner2019,

title = {Both four-dimensional computed tomography and four-dimensional cone beam computed tomography under-predict lung target motion during radiotherapy},

author = {Elisabeth Steiner and Chun-Chien Shieh and Vincent Caillet and Jeremy Booth and Ricky O'Brien and Adam Briggs and Nicholas Hardcastle and Dasantha Jayamanne and Kathryn Szymura and Thomas Eade and Paul Keall},

doi = {10.1016/j.radonc.2019.02.019},

issn = {0167-8140},

year  = {2019},

date = {2019-06-00},

journal = {Radiotherapy and Oncology},

volume = {135},

pages = {65--73},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mylonas2019,

title = {A deep learning framework for automatic detection of arbitrarily shaped fiducial markers in intrafraction fluoroscopic images},

author = {Adam Mylonas and Paul J. Keall and Jeremy T. Booth and Chun‐Chien Shieh and Thomas Eade and Per Rugaard Poulsen and Doan Trang Nguyen},

doi = {10.1002/mp.13519},

issn = {2473-4209},

year  = {2019},

date = {2019-05-00},

journal = {Medical Physics},

volume = {46},

number = {5},

pages = {2286--2297},

publisher = {Wiley},

abstract = {PurposeReal‐time image‐guided adaptive radiation therapy (IGART) requires accurate marker segmentation to resolve three‐dimensional (3D) motion based on two‐dimensional (2D) fluoroscopic images. Most common marker segmentation methods require prior knowledge of marker properties to construct a template. If marker properties are not known, an additional learning period is required to build the template which exposes the patient to an additional imaging dose. This work investigates a deep learning‐based fiducial marker classifier for use in real‐time IGART that requires no prior patient‐specific data or additional learning periods. The proposed tracking system uses convolutional neural network (CNN) models to segment cylindrical and arbitrarily shaped fiducial markers.MethodsThe tracking system uses a tracking window approach to perform sliding window classification of each implanted marker. Three cylindrical marker training datasets were generated from phantom kilovoltage (kV) and patient intrafraction images with increasing levels of megavoltage (MV) scatter. The cylindrical shaped marker CNNs were validated on unseen kV fluoroscopic images from 12 fractions of 10 prostate cancer patients with implanted gold fiducials. For the training and validation of the arbitrarily shaped marker CNNs, cone beam computed tomography (CBCT) projection images from ten fractions of seven lung cancer patients with implanted coiled markers were used. The arbitrarily shaped marker CNNs were trained using three patients and the other four unseen patients were used for validation. The effects of full training using a compact CNN (four layers with learnable weights) and transfer learning using a pretrained CNN (AlexNet, eight layers with learnable weights) were analyzed. Each CNN was evaluated using a Precision‐Recall curve (PRC), the area under the PRC plot (AUC), and by the calculation of sensitivity and specificity. The tracking system was assessed using the validation data and the accuracy was quantified by calculating the mean error, root‐mean‐square error (RMSE) and the 1st and 99th percentiles of the error.ResultsThe fully trained CNN on the dataset with moderate noise levels had a sensitivity of 99.00% and specificity of 98.92%. Transfer learning of AlexNet resulted in a sensitivity and specificity of 99.42% and 98.13%, respectively, for the same datasets. For the arbitrarily shaped marker CNNs, the sensitivity was 98.58% and specificity was 98.97% for the fully trained CNN. The transfer learning CNN had a sensitivity and specificity of 98.49% and 99.56%, respectively. The CNNs were successfully incorporated into a multiple object tracking system for both cylindrical and arbitrarily shaped markers. The cylindrical shaped marker tracking had a mean RMSE of 1.6 ± 0.2 pixels and 1.3 ± 0.4 pixels in the x‐ and y‐directions, respectively. The arbitrarily shaped marker tracking had a mean RMSE of 3.0 ± 0.5 pixels and 2.2 ± 0.4 pixels in the x‐ and y‐directions, respectively.ConclusionWith deep learning CNNs, high classification performances on unseen patient images were achieved for both cylindrical and arbitrarily shaped markers. Furthermore, the application of CNN models to intrafraction monitoring was demonstrated using a simple tracking system. The results demonstrate that CNN models can be used to track markers without prior knowledge of the marker properties or an additional learning period.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Cooper2019,

title = {Real-time respiratory triggered four dimensional cone-beam CT halves imaging dose compared to conventional 4D CBCT},

author = {Benjamin J Cooper and Ricky T O’Brien and Chun-Chien Shieh and Paul J Keall},

doi = {10.1088/1361-6560/ab065d},

issn = {1361-6560},

year  = {2019},

date = {2019-04-01},

journal = {Phys. Med. Biol.},

volume = {64},

number = {7},

publisher = {IOP Publishing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Caillet2019,

title = {Technical Note: \textit{In silico} and experimental evaluation of two leaf‐fitting algorithms for MLC tracking based on exposure error and plan complexity},

author = {Vincent Caillet and Ricky O'Brien and Douglas Moore and Per Poulsen and Tobias Pommer and Emma Colvill and Amit Sawant and Jeremy Booth and Paul Keall},

doi = {10.1002/mp.13425},

issn = {2473-4209},

year  = {2019},

date = {2019-04-00},

journal = {Medical Physics},

volume = {46},

number = {4},

pages = {1814--1820},

publisher = {Wiley},

abstract = {PurposeMultileaf collimator (MLC) tracking is being clinically pioneered to continuously compensate for thoracic and pelvic motion during radiotherapy. The purpose of this work was to characterize the performance of two MLC leaf‐fitting algorithms, direct optimization and piecewise optimization, for real‐time motion compensation with different plan complexity and tumor trajectories.MethodsTo test the algorithms, both in silico and phantom experiments were performed. The phantom experiments were performed on a Trilogy Varian linac and a HexaMotion programmable motion platform. High and low modulation VMAT plans for lung and prostate cancer cases were used along with eight patient‐measured organ‐specific trajectories. For both MLC leaf‐fitting algorithms, the plans were run with their corresponding patient trajectories. To compare algorithms, the average exposure errors, i.e., the difference in shape between ideal and fitted MLC leaves by the algorithm, plan complexity and system latency of each experiment were calculated.ResultsComparison of exposure errors for the in silico and phantom experiments showed minor differences between the two algorithms. The average exposure errors for in silico experiments with low/high plan complexity were 0.66/0.88 cm2 for direct optimization and 0.66/0.88 cm2 for piecewise optimization, respectively. The average exposure errors for the phantom experiments with low/high plan complexity were 0.73/1.02 cm2 for direct and 0.73/1.02 cm2 for piecewise optimization, respectively. The measured latency for the direct optimization was 226 ± 10 ms and for the piecewise algorithm was 228 ± 10 ms. In silico and phantom exposure errors quantified for each treatment plan demonstrated that the exposure errors from the high plan complexity (0.96 cm2 mean, 2.88 cm2 95% percentile) were all significantly different from the low plan complexity (0.70 cm2 mean, 2.18 cm2 95% percentile) (P < 0.001, two‐tailed, Mann–Whitney statistical test).ConclusionsThe comparison between the two leaf‐fitting algorithms demonstrated no significant differences in exposure errors, neither in silico nor with phantom experiments. This study revealed that plan complexity impacts the overall exposure errors significantly more than the difference between the algorithms.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Keall2019,

title = {In the future, emission‐guided radiation therapy will play a critical role in clinical radiation oncology},

author = {Paul Keall and Tomas Kron and Habib Zaidi},

doi = {10.1002/mp.13408},

issn = {2473-4209},

year  = {2019},

date = {2019-04-00},

journal = {Medical Physics},

volume = {46},

number = {4},

pages = {1519--1522},

publisher = {Wiley},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Reynolds2019,

title = {Towards patient connected imaging with ACROBEAT: Adaptive CaRdiac cOne BEAm computed Tomography},

author = {Tess Reynolds and Chun-Chien Shieh and Paul J Keall and Ricky T O’Brien},

doi = {10.1088/1361-6560/ab03f4},

issn = {1361-6560},

year  = {2019},

date = {2019-03-01},

journal = {Phys. Med. Biol.},

volume = {64},

number = {6},

publisher = {IOP Publishing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kipritidis2019,

title = {The VAMPIRE challenge: A multi‐institutional validation study of CT ventilation imaging},

author = {John Kipritidis and Bilal A. Tahir and Guillaume Cazoulat and Michael S. Hofman and Shankar Siva and Jason Callahan and Nicholas Hardcastle and Tokihiro Yamamoto and Gary E. Christensen and Joseph M. Reinhardt and Noriyuki Kadoya and Taylor J. Patton and Sarah E. Gerard and Isabella Duarte and Ben Archibald‐Heeren and Mikel Byrne and Rick Sims and Scott Ramsay and Jeremy T. Booth and Enid Eslick and Fiona Hegi‐Johnson and Henry C. Woodruff and Rob H. Ireland and Jim M. Wild and Jing Cai and John E. Bayouth and Kristy Brock and Paul J. Keall},

doi = {10.1002/mp.13346},

issn = {2473-4209},

year  = {2019},

date = {2019-03-00},

journal = {Medical Physics},

volume = {46},

number = {3},

pages = {1198--1217},

publisher = {Wiley},

abstract = {PurposeCT ventilation imaging (CTVI) is being used to achieve functional avoidance lung cancer radiation therapy in three clinical trials (NCT02528942, NCT02308709, NCT02843568). To address the need for common CTVI validation tools, we have built the Ventilation And Medical Pulmonary Image Registration Evaluation (VAMPIRE) Dataset, and present the results of the first VAMPIRE Challenge to compare relative ventilation distributions between different CTVI algorithms and other established ventilation imaging modalities.MethodsThe VAMPIRE Dataset includes 50 pairs of 4DCT scans and corresponding clinical or experimental ventilation scans, referred to as reference ventilation images (RefVIs). The dataset includes 25 humans imaged with Galligas 4DPET/CT, 21 humans imaged with DTPA‐SPECT, and 4 sheep imaged with Xenon‐CT. For the VAMPIRE Challenge, 16 subjects were allocated to a training group (with RefVI provided) and 34 subjects were allocated to a validation group (with RefVI blinded). Seven research groups downloaded the Challenge dataset and uploaded CTVIs based on deformable image registration (DIR) between the 4DCT inhale/exhale phases. Participants used DIR methods broadly classified into B‐splines, Free‐form, Diffeomorphisms, or Biomechanical modeling, with CT ventilation metrics based on the DIR evaluation of volume change, Hounsfield Unit change, or various hybrid approaches. All CTVIs were evaluated against the corresponding RefVI using the voxel‐wise Spearman coefficient , and Dice similarity coefficients evaluated for low function lung () and high function lung ().ResultsA total of 37 unique combinations of DIR method and CT ventilation metric were either submitted by participants directly or derived from participant‐submitted DIR motion fields using the in‐house software, VESPIR. The  and DSC results reveal a high degree of inter‐algorithm and intersubject variability among the validation subjects, with algorithm rankings changing by up to ten positions depending on the choice of evaluation metric. The algorithm with the highest overall cross‐modality correlations used a biomechanical model‐based DIR with a hybrid ventilation metric, achieving a median (range) of 0.49 (0.27–0.73) for , 0.52 (0.36–0.67) for , and 0.45 (0.28–0.62) for . All other algorithms exhibited at least one negative  value, and/or one DSC value less than 0.5.ConclusionsThe VAMPIRE Challenge results demonstrate that the cross‐modality correlation between CTVIs and the RefVIs varies not only with the choice of CTVI algorithm but also with the choice of RefVI modality, imaging subject, and the evaluation metric used to compare relative ventilation distributions. This variability may arise from the fact that each of the different CTVI algorithms and RefVI modalities provides a distinct physiologic measurement. Ultimately this variability, coupled with the lack of a “gold standard,” highlights the ongoing importance of further validation studies before CTVI can be widely translated from academic centers to the clinic. It is hoped that the information gleaned from the VAMPIRE Challenge can help inform future validation efforts.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Liu2019,

title = {Development and commissioning of a full‐size prototype fixed‐beam radiotherapy system with horizontal patient rotation},

author = {Paul Zhi Yuan Liu and Ricky O'Brien and Soo‐Min Heng and Matthew Newall and Simon Downes and Chun‐Chen Shieh and Stephanie Corde and Michael Jackson and Paul Keall},

doi = {10.1002/mp.13356},

issn = {2473-4209},

year  = {2019},

date = {2019-03-00},

journal = {Medical Physics},

volume = {46},

number = {3},

pages = {1331--1340},

publisher = {Wiley},

abstract = {PurposeCompared to conventional linacs with rotating gantries, a fixed‐beam radiotherapy system could be smaller, more robust and more cost‐effective. In this work, we developed and commissioned a prototype x‐ray radiotherapy system utilizing a fixed vertical radiation beam and horizontal patient rotation.MethodsThe prototype system consists of an Elekta Synergy linac with gantry fixed at 0° and a custom‐built patient rotation system (PRS). The PRS was designed to immobilize patients and safely rotate them about the horizontal axis. The interlocks and emergency stops of the linac and PRS were connected. Custom software was developed to monitor the system status, control the motion of the PRS and modify treatment plans for the fixed‐beam configuration. Following installation, the prototype system was commissioned for three‐dimensional (3D) conformal therapy based on guidelines specified in AAPM TG‐45 and TG‐142, with modifications for the fixed‐beam geometry made where necessary.ResultsThe system and control software was tested in a variety of machine states and executed motion, stop and beam gating commands as expected. Interlocks and emergency stops of the linac and PRS were found to correctly stop PRS motion and both kV and MV radiation beams when triggered. For 3D conformal treatments, the prototype system met all AAPM TG‐45 and TG‐142 specifications for geometric and dosimetric accuracy. Motion of the PRS was within 0.6 ± 0.3 mm and 0.10° ± 0.07° of input values for translation and rotation respectively. The axis of rotation of the PRS was coincident with the radiation beam axis to less than 1 mm. End‐to‐end treatment verification for 6 MV conformal treatments showed less than 2% difference between planned and delivered dose for all fields.ConclusionIn this work, we have developed and commissioned a radiotherapy system that utilizes a fixed vertical radiation beam and horizontal patient rotation. This system is a proof‐of‐concept prototype for a fixed‐beam treatment system without a rotating gantry. Fixed‐beam systems that are smaller and more cost‐effective could help in improving global access to radiotherapy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kipritidis2019b,

title = {The VAMPIRE challenge: A multi‐institutional validation study of CT ventilation imaging},

author = {John Kipritidis and Bilal A. Tahir and Guillaume Cazoulat and Michael S. Hofman and Shankar Siva and Jason Callahan and Nicholas Hardcastle and Tokihiro Yamamoto and Gary E. Christensen and Joseph M. Reinhardt and Noriyuki Kadoya and Taylor J. Patton and Sarah E. Gerard and Isabella Duarte and Ben Archibald‐Heeren and Mikel Byrne and Rick Sims and Scott Ramsay and Jeremy T. Booth and Enid Eslick and Fiona Hegi‐Johnson and Henry C. Woodruff and Rob H. Ireland and Jim M. Wild and Jing Cai and John E. Bayouth and Kristy Brock and Paul J. Keall},

doi = {10.1002/mp.13346},

issn = {2473-4209},

year  = {2019},

date = {2019-03-00},

journal = {Medical Physics},

volume = {46},

number = {3},

pages = {1198--1217},

publisher = {Wiley},

abstract = {PurposeCT ventilation imaging (CTVI) is being used to achieve functional avoidance lung cancer radiation therapy in three clinical trials (NCT02528942, NCT02308709, NCT02843568). To address the need for common CTVI validation tools, we have built the Ventilation And Medical Pulmonary Image Registration Evaluation (VAMPIRE) Dataset, and present the results of the first VAMPIRE Challenge to compare relative ventilation distributions between different CTVI algorithms and other established ventilation imaging modalities.MethodsThe VAMPIRE Dataset includes 50 pairs of 4DCT scans and corresponding clinical or experimental ventilation scans, referred to as reference ventilation images (RefVIs). The dataset includes 25 humans imaged with Galligas 4DPET/CT, 21 humans imaged with DTPA‐SPECT, and 4 sheep imaged with Xenon‐CT. For the VAMPIRE Challenge, 16 subjects were allocated to a training group (with RefVI provided) and 34 subjects were allocated to a validation group (with RefVI blinded). Seven research groups downloaded the Challenge dataset and uploaded CTVIs based on deformable image registration (DIR) between the 4DCT inhale/exhale phases. Participants used DIR methods broadly classified into B‐splines, Free‐form, Diffeomorphisms, or Biomechanical modeling, with CT ventilation metrics based on the DIR evaluation of volume change, Hounsfield Unit change, or various hybrid approaches. All CTVIs were evaluated against the corresponding RefVI using the voxel‐wise Spearman coefficient , and Dice similarity coefficients evaluated for low function lung () and high function lung ().ResultsA total of 37 unique combinations of DIR method and CT ventilation metric were either submitted by participants directly or derived from participant‐submitted DIR motion fields using the in‐house software, VESPIR. The  and DSC results reveal a high degree of inter‐algorithm and intersubject variability among the validation subjects, with algorithm rankings changing by up to ten positions depending on the choice of evaluation metric. The algorithm with the highest overall cross‐modality correlations used a biomechanical model‐based DIR with a hybrid ventilation metric, achieving a median (range) of 0.49 (0.27–0.73) for , 0.52 (0.36–0.67) for , and 0.45 (0.28–0.62) for . All other algorithms exhibited at least one negative  value, and/or one DSC value less than 0.5.ConclusionsThe VAMPIRE Challenge results demonstrate that the cross‐modality correlation between CTVIs and the RefVIs varies not only with the choice of CTVI algorithm but also with the choice of RefVI modality, imaging subject, and the evaluation metric used to compare relative ventilation distributions. This variability may arise from the fact that each of the different CTVI algorithms and RefVI modalities provides a distinct physiologic measurement. Ultimately this variability, coupled with the lack of a “gold standard,” highlights the ongoing importance of further validation studies before CTVI can be widely translated from academic centers to the clinic. It is hoped that the information gleaned from the VAMPIRE Challenge can help inform future validation efforts.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Garau2019,

title = {A ROI-based global motion model established on 4DCT and 2D cine-MRI data for MRI-guidance in radiation therapy},

author = {Noemi Garau and Riccardo Via and Giorgia Meschini and Danny Lee and Paul Keall and Marco Riboldi and Guido Baroni and Chiara Paganelli},

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

issn = {1361-6560},

year  = {2019},

date = {2019-02-01},

journal = {Phys. Med. Biol.},

volume = {64},

number = {4},

publisher = {IOP Publishing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Keall2019c,

title = {In Reply to Dahele and Verbakel},

author = {Paul J. Keall and D. Trang Nguyen and Ricky O'Brien and Pengpeng Zhang and Jenny Bertholet and Per R. Poulsen},

doi = {10.1016/j.ijrobp.2018.08.063},

issn = {0360-3016},

year  = {2019},

date = {2019-01-00},

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

volume = {103},

number = {1},

pages = {283--284},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pinkham2019,

title = {A Feasibility Study of Single-inhalation, Single-energy Xenon-enhanced CT for High-resolution Imaging of Regional Lung Ventilation in Humans},

author = {Daniel W. Pinkham and Mohammadreza Negahdar and Tokihiro Yamamoto and Erik Mittra and Maximilian Diehn and Viswam S. Nair and Paul J. Keall and Peter G. Maxim and Billy W. Loo},

doi = {10.1016/j.acra.2018.03.006},

issn = {1076-6332},

year  = {2019},

date = {2019-01-00},

journal = {Academic Radiology},

volume = {26},

number = {1},

pages = {38--49},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Dahele2019,

title = {In Regard to Keall et al},

author = {Max Dahele and Wilko Verbakel},

doi = {10.1016/j.ijrobp.2018.08.065},

issn = {0360-3016},

year  = {2019},

date = {2019-01-00},

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

volume = {103},

number = {1},

pages = {282--283},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pinkham2019b,

title = {A Feasibility Study of Single-inhalation, Single-energy Xenon-enhanced CT for High-resolution Imaging of Regional Lung Ventilation in Humans},

author = {Daniel W. Pinkham and Mohammadreza Negahdar and Tokihiro Yamamoto and Erik Mittra and Maximilian Diehn and Viswam S. Nair and Paul J. Keall and Peter G. Maxim and Billy W. Loo},

doi = {10.1016/j.acra.2018.03.006},

issn = {1076-6332},

year  = {2019},

date = {2019-01-00},

journal = {Academic Radiology},

volume = {26},

number = {1},

pages = {38--49},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Paganelli2018,

title = {MRI-guidance for motion management in external beam radiotherapy: current status and future challenges},

author = {C Paganelli and B Whelan and M Peroni and P Summers and M Fast and T van de Lindt and J McClelland and B Eiben and P Keall and T Lomax and M Riboldi and G Baroni},

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

issn = {1361-6560},

year  = {2018},

date = {2018-11-01},

journal = {Phys. Med. Biol.},

volume = {63},

number = {22},

publisher = {IOP Publishing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Steiner2018,

title = {4-Dimensional Cone Beam Computed Tomography–Measured Target Motion Underrepresents Actual Motion},

author = {Elisabeth Steiner and Chun-Chien Shieh and Vincent Caillet and Jeremy Booth and Nicholas Hardcastle and Adam Briggs and Dasantha Jayamanne and Carol Haddad and Thomas Eade and Paul Keall},

doi = {10.1016/j.ijrobp.2018.04.056},

issn = {0360-3016},

year  = {2018},

date = {2018-11-00},

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

volume = {102},

number = {4},

pages = {932--940},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Liney2018,

title = {MRI-Linear Accelerator Radiotherapy Systems},

author = {G.P. Liney and B. Whelan and B. Oborn and M. Barton and P. Keall},

doi = {10.1016/j.clon.2018.08.003},

issn = {0936-6555},

year  = {2018},

date = {2018-11-00},

journal = {Clinical Oncology},

volume = {30},

number = {11},

pages = {686--691},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yamamoto2018,

title = {Changes in Regional Ventilation During Treatment and Dosimetric Advantages of CT Ventilation Image Guided Radiation Therapy for Locally Advanced Lung Cancer},

author = {Tokihiro Yamamoto and Sven Kabus and Matthieu Bal and Karl Bzdusek and Paul J. Keall and Cari Wright and Stanley H. Benedict and Megan E. Daly},

doi = {10.1016/j.ijrobp.2018.04.063},

issn = {0360-3016},

year  = {2018},

date = {2018-11-00},

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

volume = {102},

number = {4},

pages = {1366--1373},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lydiard2018,

title = {Investigating multi-leaf collimator tracking in stereotactic arrhythmic radioablation (STAR) treatments for atrial fibrillation},

author = {Suzanne Lydiard and Vincent Caillet and Svenja Ipsen and Ricky O’Brien and Oliver Blanck and Per Rugaard Poulsen and Jeremy Booth and Paul Keall},

doi = {10.1088/1361-6560/aadf7c},

issn = {1361-6560},

year  = {2018},

date = {2018-10-01},

journal = {Phys. Med. Biol.},

volume = {63},

number = {19},

publisher = {IOP Publishing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Shieh2018,

title = {Cone-beam CT reconstruction with gravity-induced motion},

author = {Chun-Chien Shieh and Jeffrey Barber and William Counter and Jonathan Sykes and Peter Bennett and Soo-Min Heng and Paul White and Stéphanie Corde and Michael Jackson and Verity Ahern and Ilana Feain and Ricky O’Brien and Paul J Keall},

doi = {10.1088/1361-6560/aae1bb},

issn = {1361-6560},

year  = {2018},

date = {2018-10-01},

journal = {Phys. Med. Biol.},

volume = {63},

number = {20},

publisher = {IOP Publishing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Nguyen2018,

title = {An augmented correlation framework for the estimation of tumour translational and rotational motion during external beam radiotherapy treatments using intermittent monoscopic x-ray imaging and an external respiratory signal},

author = {Doan Trang Nguyen and Jeremy T Booth and Vincent Caillet and Nicholas Hardcastle and Adam Briggs and Carol Haddad and Thomas Eade and Ricky O’Brien and Paul J Keall},

doi = {10.1088/1361-6560/aadf2c},

issn = {1361-6560},

year  = {2018},

date = {2018-10-01},

journal = {Phys. Med. Biol.},

volume = {63},

number = {20},

publisher = {IOP Publishing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Liu2018,

title = {Technical Note: Real‐time image‐guided adaptive radiotherapy of a rigid target for a prototype fixed beam radiotherapy system},

author = {P. Z. Y. Liu and D. T. Nguyen and I. Feain and R. O'Brien and P. J. Keall and J. T. Booth},

doi = {10.1002/mp.13143},

issn = {2473-4209},

year  = {2018},

date = {2018-10-00},

journal = {Medical Physics},

volume = {45},

number = {10},

pages = {4660--4666},

publisher = {Wiley},

abstract = {PurposeFixed beam radiotherapy systems utilize couch movement and rotation instead of gantry rotation in order to simplify linear accelerator design. We investigate the ability to deliver fixed beam treatments with the same level of clinical accuracy as conventional (rotating beam) treatments using real‐time image guidance to maintain this accuracy in the presence of rigid target motion.MethodsA prototype fixed beam radiotherapy system was built using a standard linac with the beam fixed in the vertical position and a computer controlled rotation stage that rotated a rigid phantom about the superior–inferior axis. Kilovoltage Intrafraction Monitoring (KIM) and real‐time beam adaptation with MLC tracking was applied to a five‐field IMRT treatment plan with motion introduced to the phantom. The same IMRT treatment was also delivered with real‐time adaptation using the conventional rotating beam geometry. Film dosimetry was used to measure the dose delivered with a fixed beam compared to a rotating beam, as well as to compare treatments delivered with and without real‐time adaptation.ResultsThe dose distributions were found to be equivalent between the fixed beam and rotating beam geometry for real‐time adaptive radiotherapy using KIM and MLC tracking beam adaptation. Gamma analysis on the films showed agreement >98% using a 2%/2 mm criteria with adaptation for static shifts and periodic motion.ConclusionsFixed beam treatments with real‐time beam adaptation are dosimetrically equivalent to conventional treatments with a rotating beam, even in the presence of rigid target motion. This suggests that, for a rigid target, the high clinical accuracy of real‐time adaptive radiotherapy can be achieved with simpler beam geometry.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Martin2018,

title = {An \textit{in silico} performance characterization of respiratory motion guided 4DCT for high-quality low-dose lung cancer imaging},

author = {Spencer Martin and Ricky O’ Brien and Christian Hofmann and Paul Keall and John Kipriditis},

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

issn = {1361-6560},

year  = {2018},

date = {2018-08-01},

journal = {Phys. Med. Biol.},

volume = {63},

number = {15},

publisher = {IOP Publishing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Martin2018b,

title = {An \textit{in silico} performance characterization of respiratory motion guided 4DCT for high-quality low-dose lung cancer imaging},

author = {Spencer Martin and Ricky O’ Brien and Christian Hofmann and Paul Keall and John Kipriditis},

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

issn = {1361-6560},

year  = {2018},

date = {2018-08-01},

journal = {Phys. Med. Biol.},

volume = {63},

number = {15},

publisher = {IOP Publishing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Liney2018b,

title = {Imaging performance of a dedicated radiation transparent RF coil on a 1.0 Tesla inline MRI-linac},

author = {G P Liney and B Dong and E Weber and R Rai and A Destruel and R Garcia-Alvarez and D J Manton and U Jelen and K Zhang and M Barton and P Keall and S Crozier},

doi = {10.1088/1361-6560/aac813},

issn = {1361-6560},

year  = {2018},

date = {2018-07-01},

journal = {Phys. Med. Biol.},

volume = {63},

number = {13},

publisher = {IOP Publishing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Paganelli2018b,

title = {Image‐based retrospective 4D MRI in external beam radiotherapy: A comparative study with a digital phantom},

author = {Chiara Paganelli and John Kipritidis and Danny Lee and Guido Baroni and Paul Keall and Marco Riboldi},

doi = {10.1002/mp.12965},

issn = {2473-4209},

year  = {2018},

date = {2018-07-00},

journal = {Medical Physics},

volume = {45},

number = {7},

pages = {3161--3172},

publisher = {Wiley},

abstract = {PurposeSeveral image‐based retrospective sorting methods of 4D magnetic resonance imaging (4D MRI) have been proposed for respiratory motion reconstruction in external beam radiotherapy. However, the optimal strategy for providing accurate and artifact‐free 4D MRI, ideally corresponding to an average breathing cycle, is not yet defined. This study presents a proactive comparison of three published image‐based sorting methods, to define a groundwork for benchmarking in 4D MRI.MethodsThree published 4D MRI methods were selected for image retrospective sorting: body area, mutual information, and navigator slice. The three image‐based methods were compared against a conventional retrospective sorting method based on an external surrogate. Comparisons were performed by means of an MRI digital phantom, derived from the XCAT CT phantom generated with different patient‐derived signals, for a total of 12 cases. Specific multislice MRI acquisitions were simulated for slice sorting and sagittal, coronal, and axial orientations were tested. An average 4D cycle was generated as ground truth.ResultsIndividual and grouped patient analyses showed better performance of the navigator slice and mutual information in amplitude binning with respect to the body area strategy. Binning artifacts were reduced on the diaphragm with the slice navigator method due to the acquired internal information. Tumor motion description accurately matched the ground truth in the mutual information strategy with amplitude binning. The body area method followed the performance of the external surrogate and presented larger errors, since was not correlated with the internal anatomy. Sagittal and coronal orientations reported lower errors than axial slicing. Individual analysis showed the need of a patient‐specific evaluation for the selection of the best method.ConclusionsA comparison between three different image‐based retrospective sorting methods for 4D MRI is proposed, providing guidelines for benchmark definition in MRI‐guided radiotherapy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Duncan2018,

title = {Real‐time high spatial resolution dose verification in stereotactic motion adaptive arc radiotherapy},

author = {Mitchell Duncan and Matthew K. Newall and Vincent Caillet and Jeremy T. Booth and Paul J. Keall and Michael Lerch and Vladimir Perevertaylo and Anatoly B. Rosenfeld and Marco Petasecca},

doi = {10.1002/acm2.12364},

issn = {1526-9914},

year  = {2018},

date = {2018-07-00},

journal = {J Applied Clin Med Phys},

volume = {19},

number = {4},

pages = {173--184},

publisher = {Wiley},

abstract = {AbstractPurposeRadiation treatments delivered with real‐time multileaf collimator (MLC) tracking currently lack fast pretreatment or real‐time quality assurance. The purpose of this study is to test a 2D silicon detector, MagicPlate‐512 (MP512), in a complex clinical environment involving real‐time reconfiguration of the MLC leaves during target tracking.MethodsMP512 was placed in the center of a solid water phantom and mounted on a motion platform used to simulate three different patient motions. Electromagnetic target tracking was implemented using the Calypso system (Varian Medical Systems, Palo Alto, CA, USA) and an MLC tracking software. A two‐arc VMAT plan was delivered and 2D dose distributions were reconstructed by MP512, EBT3 film, and the Eclipse treatment planning system (TPS). Dose maps were compared using gamma analysis with 2%/2 mm and 3%/3 mm acceptance criteria. Dose profiles were generated in sup‐inf and lateral directions to show the agreement of MP512 to EBT3 and to highlight the efficacy of the MLC tracking system in mitigating the effect of the simulated patient motion.ResultsUsing a 3%/3 mm acceptance criterion for 2D gamma analysis, MP512 to EBT3 film agreement was 99% and MP512 to TPS agreement was 100%. For a 2%/2 mm criterion, the agreement was 95% and 98%, respectively. Full width at half maximum and 80%/20% penumbral width of the MP512 and EBT3 dose profiles agreed within 1 mm and 0.5 mm, respectively. Patient motion increased the measured dose profile penumbral width by nearly 2 mm (with respect to the no‐motion case); however, the MLC tracking strategy was able to mitigate 80% of this effect.ConclusionsMP512 is capable of high spatial resolution 2D dose reconstruction during adaptive MLC tracking, including arc deliveries. It shows potential as an effective tool for 2D small field dosimetry and pretreatment quality assurance for MLC tracking modalities. These results provide confidence that detector‐based pretreatment dosimetry is clinically feasible despite fast real‐time MLC reconfigurations.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Whelan2018,

title = {An update of NIH research funding of AAPM members from 1985 to 2017},

author = {Brendan Whelan},

doi = {10.1002/mp.12993},

issn = {2473-4209},

year  = {2018},

date = {2018-07-00},

journal = {Medical Physics},

volume = {45},

number = {7},

pages = {3475--3476},

publisher = {Wiley},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chapman2018,

title = {Influence of respiratory motion management technique on radiation pneumonitis risk with robotic stereotactic body radiation therapy},

author = {Christopher H. Chapman and Christopher McGuinness and Alexander R. Gottschalk and Sue S. Yom and Adam A. Garsa and Mekhail Anwar and Steve E. Braunstein and Atchar Sudhyadhom and Paul Keall and Martina Descovich},

doi = {10.1002/acm2.12338},

issn = {1526-9914},

year  = {2018},

date = {2018-07-00},

journal = {J Applied Clin Med Phys},

volume = {19},

number = {4},

pages = {48--57},

publisher = {Wiley},

abstract = {AbstractPurpose/ObjectivesFor lung stereotactic body radiation therapy (SBRT), real‐time tumor tracking (RTT) allows for less radiation to normal lung compared to the internal target volume (ITV) method of respiratory motion management. To quantify the advantage of RTT, we examined the difference in radiation pneumonitis risk between these two techniques using a normal tissue complication probability (NTCP) model.Materials/Method20 lung SBRT treatment plans using RTT were replanned with the ITV method using respiratory motion information from a 4D‐CT image acquired at the original simulation. Risk of symptomatic radiation pneumonitis was calculated for both plans using a previously derived NTCP model. Features available before treatment planning that identified significant increase in NTCP with ITV versus RTT plans were identified.ResultsPrescription dose to the planning target volume (PTV) ranged from 22 to 60 Gy in 1–5 fractions. The median tumor diameter was 3.5 cm (range 2.1–5.5 cm) with a median volume of 14.5 mL (range 3.6–59.9 mL). The median increase in PTV volume from RTT to ITV plans was 17.1 mL (range 3.5–72.4 mL), and the median increase in PTV/lung volume ratio was 0.46% (range 0.13–1.98%). Mean lung dose and percentage dose–volumes were significantly higher in ITV plans at all levels tested. The median NTCP was 5.1% for RTT plans and 8.9% for ITV plans, with a median difference of 1.9% (range 0.4–25.5%, pairwise P < 0.001). Increases in NTCP between plans were best predicted by increases in PTV volume and PTV/lung volume ratio.ConclusionsThe use of RTT decreased the risk of radiation pneumonitis in all plans. However, for most patients the risk reduction was minimal. Differences in plan PTV volume and PTV/lung volume ratio may identify patients who would benefit from RTT technique before completing treatment planning.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Martin2018c,

title = {Moderately hypofractionated prostate external-beam radiotherapy: an emerging standard},

author = {Jarad M Martin and Stephane Supiot and Paul J Keall and Charles N Catton},

doi = {10.1259/bjr.20170807},

issn = {1748-880X},

year  = {2018},

date = {2018-06-01},

volume = {91},

number = {1086},

publisher = {Oxford University Press (OUP)},

abstract = {Research over recent years has demonstrated that curative external-beam radiotherapy can be safely and efficaciously delivered with roughly half the number of treatments which was previously considered standard. We review the data supporting this change in practice, methods for implementation, as well as emerging future directions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hazelaar2018,

title = {Markerless positional verification using template matching and triangulation of kV images acquired during irradiation for lung tumors treated in breath-hold},

author = {Colien Hazelaar and Max Dahele and Hassan Mostafavi and Lineke van der Weide and Ben Slotman and Wilko Verbakel},

doi = {10.1088/1361-6560/aac1a9},

issn = {1361-6560},

year  = {2018},

date = {2018-06-01},

journal = {Phys. Med. Biol.},

volume = {63},

number = {11},

publisher = {IOP Publishing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Paganelli2018c,

title = {Feasibility study on 3D image reconstruction from 2D orthogonal cine‐MRI for MRI‐guided radiotherapy},

author = {Chiara Paganelli and Danny Lee and John Kipritidis and Brendan Whelan and Peter B Greer and Guido Baroni and Marco Riboldi and Paul Keall},

doi = {10.1111/1754-9485.12713},

issn = {1754-9485},

year  = {2018},

date = {2018-06-00},

journal = {J Med Imag Rad Onc},

volume = {62},

number = {3},

pages = {389--400},

publisher = {Wiley},

abstract = {AbstractIntroductionIn‐room MRI is a promising image guidance strategy in external beam radiotherapy to acquire volumetric information for moving targets. However, limitations in spatio‐temporal resolution led several authors to use 2D orthogonal images for guidance. The aim of this work is to present a method to concurrently compensate for non‐rigid tumour motion and provide an approach for 3D reconstruction from 2D orthogonal cine‐MRI slices for MRI‐guided treatments.MethodsFree‐breathing sagittal/coronal interleaved 2D cine‐MRI were acquired in addition to a pre‐treatment 3D volume in two patients. We performed deformable image registration (DIR) between cine‐MRI slices and corresponding slices in the pre‐treatment 3D volume. Based on an extrapolation of the interleaved 2D motion fields, the 3D motion field was estimated and used to warp the pre‐treatment volume. Due to the lack of a ground truth for patients, the method was validated on a digital 4D lung phantom.ResultsOn the phantom, the 3D reconstruction method was able to compensate for tumour motion and compared favourably to the results of previously adopted strategies. The difference in the 3D motion fields between the phantom and the extrapolated motion was 0.4 ± 0.3 mm for tumour and 0.8 ± 1.5 mm for whole anatomy, demonstrating feasibility of performing a 3D volumetric reconstruction directly from 2D orthogonal cine‐MRI slices. Application of the method to patient data confirmed the feasibility of utilizing this method in real world scenarios.ConclusionPreliminary results on phantom and patient cases confirm the feasibility of the proposed approach in an MRI‐guided scenario, especially for non‐rigid tumour motion compensation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Keall2018,

title = {Electromagnetic-Guided MLC Tracking Radiation Therapy for Prostate Cancer Patients: Prospective Clinical Trial Results},

author = {Paul J. Keall and Emma Colvill and Ricky O'Brien and Vincent Caillet and Thomas Eade and Andrew Kneebone and George Hruby and Per R. Poulsen and Benjamin Zwan and Peter B. Greer and Jeremy Booth},

doi = {10.1016/j.ijrobp.2018.01.098},

issn = {0360-3016},

year  = {2018},

date = {2018-06-00},

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

volume = {101},

number = {2},

pages = {387--395},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Barber2018,

title = {A CBCT study of the gravity-induced movement in rotating rabbits},

author = {Jeffrey Barber and Chun-Chien Shieh and William Counter and Jonathan Sykes and Peter Bennett and Verity Ahern and Stéphanie Corde and Soo-Min Heng and Paul White and Michael Jackson and Paul Liu and Paul J Keall and Ilana Feain},

doi = {10.1088/1361-6560/aabf12},

issn = {1361-6560},

year  = {2018},

date = {2018-05-01},

journal = {Phys. Med. Biol.},

volume = {63},

number = {10},

publisher = {IOP Publishing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Toftegaard2018,

title = {Potential improvements of lung and prostate MLC tracking investigated by treatment simulations},

author = {Jakob Toftegaard and Paul J. Keall and Ricky O'Brien and Dan Ruan and Floris Ernst and Noriyasu Homma and Kei Ichiji and Per Rugaard Poulsen},

doi = {10.1002/mp.12868},

issn = {2473-4209},

year  = {2018},

date = {2018-05-00},

journal = {Medical Physics},

volume = {45},

number = {5},

pages = {2218--2229},

publisher = {Wiley},

abstract = {Purpose/objectivesIntrafraction tumor motion during external radiotherapy is a challenge for the treatment accuracy. A novel technique to mitigate the impact of tumor motion is real‐time adaptation of the multileaf collimator (MLC) aperture to the motion, also known as MLC tracking. Although MLC tracking improves the dosimetric accuracy, there are still residual errors. Here, we investigate and rank the performance of five prediction algorithms and seven improvements of an MLC tracking system by extensive tracking treatment simulations.Materials and MethodsAn in‐house–developed MLC tracking simulator that has been experimentally validated against an electromagnetic‐guided MLC tracking system was used to test the prediction algorithms and tracking system improvements. The simulator requires a Dicom treatment plan and a motion trajectory as input and outputs all motion of the accelerator during MLC tracking treatment delivery. For lung tumors, MLC tracking treatments were simulated with a low and a high modulation VMAT plan using 99 patient‐measured lung tumor trajectories. For prostate, tracking was also simulated with a low and a high modulation VMAT plan, but with 695 prostate trajectories. For each simulated treatment, the tracking error was quantified as the mean MLC exposure error, which is the sum of the overexposed area (irradiated area that should have been shielded according to the treatment plan) and the underexposed area (shielded area that should have been irradiated).First, MLC tracking was simulated with the current MLC tracking system without prediction, with perfect prediction (Perfect), and with the following five prediction algorithms: linear Kalman filter (Kalman), kernel density estimation (KDE), linear adaptive filtering (LAF), wavelet‐based multiscale autoregression (wLMS), and time variant seasonal autoregression (TVSAR). Next, MLC tracking was simulated using the best prediction algorithm and seven different tracking system improvements: no localization signal latency (a), doubled maximum MLC leaf speed (b), halved MLC leaf width (c), use of Y backup jaws to track motion perpendicular to the MLC leaves (d), dynamic collimator rotation for alignment of the MLC leaves with the dominant target motion direction (e), improvements 4 and 5 combined (f), and all improvements combined (g).ResultsAll results are presented as the mean residual MLC exposure error compared to no tracking. In the prediction study, the residual MLC exposure error was 47.0% (no prediction), 45.1% (Kalman), 43.8% (KDE), 43.7% (LAF), 42.1% (wLMS), 40.1% (TVSAR), and 36.5% (Perfect) for lung MLC tracking. For prostate MLC tracking, it was 66.0% (no prediction), 66.9% (Kalman), and 63.4% (Perfect). For lung with TVSAR prediction, the residual MLC exposure error for the seven tracking system improvements was 37.2%(1), 38.3%(2), 37.4%(3), 34.2%(4), 30.6%(5), 27.7%(6), and 20.7%(7). For prostate with no prediction, the residual MLC exposure error was 61.7%(1), 61.4%(2), 55.4%(3), 57.2%(4), 47.5%(5), 43.7%(6), and 38.7%(7).ConclusionFor prostate, MLC tracking was slightly better without prediction than with linear Kalman filter prediction. For lung, the TVSAR prediction algorithm performed best. Dynamic alignment of the collimator with the dominant motion axis was the most efficient MLC tracking improvement except for lung tracking with the low modulation VMAT plan, where jaw tracking was slightly better.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Whelan2018b,

title = {Passive magnetic shielding in MRI-Linac systems},

author = {Brendan Whelan and Stefan Kolling and Brad M Oborn and Paul Keall},

doi = {10.1088/1361-6560/aab138},

issn = {1361-6560},

year  = {2018},

date = {2018-04-01},

journal = {Phys. Med. Biol.},

volume = {63},

number = {7},

publisher = {IOP Publishing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Keall2018b,

title = {The first clinical implementation of real-time image-guided adaptive radiotherapy using a standard linear accelerator},

author = {Paul J. Keall and Doan Trang Nguyen and Ricky O'Brien and Vincent Caillet and Emily Hewson and Per Rugaard Poulsen and Regina Bromley and Linda Bell and Thomas Eade and Andrew Kneebone and Jarad Martin and Jeremy T. Booth},

doi = {10.1016/j.radonc.2018.01.001},

issn = {0167-8140},

year  = {2018},

date = {2018-04-00},

journal = {Radiotherapy and Oncology},

volume = {127},

number = {1},

pages = {6--11},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lee2018,

title = {Audiovisual biofeedback improves the correlation between internal/external surrogate motion and lung tumor motion},

author = {Danny Lee and Peter B. Greer and Chiara Paganelli and Joanna Jane Ludbrook and Taeho Kim and Paul Keall},

doi = {10.1002/mp.12758},

issn = {2473-4209},

year  = {2018},

date = {2018-03-00},

journal = {Medical Physics},

volume = {45},

number = {3},

pages = {1009--1017},

publisher = {Wiley},

abstract = {PurposeBreathing management can reduce breath‐to‐breath (intrafraction) and day‐by‐day (interfraction) variability in breathing motion while utilizing the respiratory motion of internal and external surrogates for respiratory guidance. Audiovisual (AV) biofeedback, an interactive personalized breathing motion management system, has been developed to improve reproducibility of intra‐ and interfraction breathing motion. However, the assumption of the correlation of respiratory motion between surrogates and tumors is not always verified during medical imaging and radiation treatment. Therefore, the aim of the study was to test the hypothesis that the correlation of respiratory motion between surrogates and tumors is the same under free breathing without guidance (FB) and with AV biofeedback guidance for voluntary motion management.MethodsFor 13 lung cancer patients receiving radiotherapy, 2D coronal and sagittal cine‐MR images were acquired across two MRI sessions (pre‐ and mid‐treatment) with two breathing conditions: (a) FB and (b) AV biofeedback, totaling 88 patient measurements. Simultaneously, the external respiratory motion of the abdomen was measured. The internal respiratory motion of the diaphragm and lung tumor was retrospectively measured from 2D coronal and sagittal cine‐MR images. The correlation of respiratory motion between surrogates and tumors was calculated using Pearson's correlation coefficient for: (a) abdomen to tumor (abdomen‐tumor) and (b) diaphragm to tumor (diaphragm‐tumor). The correlations were compared between FB and AV biofeedback using several metrics: abdomen‐tumor and diaphragm‐tumor correlations with/without ≥5 mm tumor motion range and with/without adjusting for phase shifts between the signals.ResultsCompared to FB, AV biofeedback improved abdomen‐tumor correlation by 11% (p = 0.12) from 0.53 to 0.59 and diaphragm‐tumor correlation by 13% (p = 0.02) from 0.55 to 0.62. Compared to FB, AV biofeedback improved abdomen‐tumor correlation by 17% (p = 0.01) and diaphragm‐tumor correlation by 15% (p < 0.01) while correcting 0.3 s (p = 0.54) and 0.2 s (p = 0.19) phase shifts, respectively. In addition, AV biofeedback with ≥5 mm tumor motion range, compared to FB improved abdomen‐tumor correlation by 14% (p = 0.18) and diaphragm‐tumor correlation by 17% (p = 0.01). The highest abdomen‐tumor and diaphragm‐tumor correlations were found using ≥5 mm tumor motion range and phase shifts, resulting in a 12% improvement in AV biofeedback.ConclusionsOur results demonstrated that AV biofeedback improves the correlation of respiratory motion between surrogates and the tumor. This suggests a need for AV biofeedback for respiratory guidance utilizing respiratory surrogates during image‐guided and MRI‐guided radiotherapy in thoracic regions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Montanaro2018,

title = {A comparison of gantry‐mounted x‐ray‐based real‐time target tracking methods},

author = {Tim Montanaro and Doan Trang Nguyen and Paul J. Keall and Jeremy Booth and Vincent Caillet and Thomas Eade and Carol Haddad and Chun‐Chien Shieh},

doi = {10.1002/mp.12765},

issn = {2473-4209},

year  = {2018},

date = {2018-03-00},

journal = {Medical Physics},

volume = {45},

number = {3},

pages = {1222--1232},

publisher = {Wiley},

abstract = {PurposeMost modern radiotherapy machines are built with a 2D kV imaging system. Combining this imaging system with a 2D‐3D inference method would allow for a ready‐made option for real‐time 3D tumor tracking. This work investigates and compares the accuracy of four existing 2D‐3D inference methods using both motion traces inferred from external surrogates and measured internally from implanted beacons.MethodTumor motion data from 160 fractions (46 thoracic/abdominal patients) of Synchrony traces (inferred traces), and 28 fractions (7 lung patients) of Calypso traces (internal traces) from the LIGHT SABR trial (NCT02514512) were used in this study. The motion traces were used as the ground truth. The ground truth trajectories were used in silico to generate 2D positions projected on the kV detector. These 2D traces were then passed to the 2D‐3D inference methods: interdimensional correlation, Gaussian probability density function (PDF), arbitrary‐shape PDF, and the Kalman filter. The inferred 3D positions were compared with the ground truth to determine tracking errors. The relationships between tracking error and motion magnitude, interdimensional correlation, and breathing periodicity index (BPI) were also investigated.ResultsLarger tracking errors were observed from the Calypso traces, with RMS and 95th percentile 3D errors of 0.84–1.25 mm and 1.72–2.64 mm, compared to 0.45–0.68 mm and 0.74–1.13 mm from the Synchrony traces. The Gaussian PDF method was found to be the most accurate, followed by the Kalman filter, the interdimensional correlation method, and the arbitrary‐shape PDF method. Tracking error was found to strongly and positively correlate with motion magnitude for both the Synchrony and Calypso traces and for all four methods. Interdimensional correlation and BPI were found to negatively correlate with tracking error only for the Synchrony traces. The Synchrony traces exhibited higher interdimensional correlation than the Calypso traces especially in the anterior‐posterior direction.ConclusionInferred traces often exhibit higher interdimensional correlation, which are not true representation of thoracic/abdominal motion and may underestimate kV‐based tracking errors. The use of internal traces acquired from systems such as Calypso is advised for future kV‐based tracking studies. The Gaussian PDF method is the most accurate 2D‐3D inference method for tracking thoracic/abdominal targets. Motion magnitude has significant impact on 2D‐3D inference error, and should be considered when estimating kV‐based tracking error.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kim2018,

title = {The accuracy and precision of Kilovoltage Intrafraction Monitoring (KIM) six degree-of-freedom prostate motion measurements during patient treatments},

author = {Jung-Ha Kim and Doan T. Nguyen and Jeremy T. Booth and Chen-Yu Huang and Todsaporn Fuangrod and Per Poulsen and Ricky O'Brien and Vincent Caillet and Thomas Eade and Andrew Kneebone and Paul Keall},

doi = {10.1016/j.radonc.2017.10.030},

issn = {0167-8140},

year  = {2018},

date = {2018-02-00},

journal = {Radiotherapy and Oncology},

volume = {126},

number = {2},

pages = {236--243},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pollock2018,

title = {Impact of audiovisual biofeedback on interfraction respiratory motion reproducibility in liver cancer stereotactic body radiotherapy},

author = {Sean Pollock and Regina Tse and Darren Martin and Lisa McLean and Melissa Pham and David Tait and Reuben Estoesta and Grant Whittington and Jessica Turley and Christopher Kearney and Gwi Cho and Robin Hill and Sheila Pickard and Paul Aston and Kuldeep Makhija and Ricky O'Brien and Paul Keall},

doi = {10.1111/1754-9485.12702},

issn = {1754-9485},

year  = {2018},

date = {2018-02-00},

journal = {J Med Imag Rad Onc},

volume = {62},

number = {1},

pages = {133--139},

publisher = {Wiley},

abstract = {AbstractIntroductionIrregular breathing motion exacerbates uncertainties throughout a course of radiation therapy. Breathing guidance has demonstrated to improve breathing motion consistency. This was the first clinical implementation of audiovisual biofeedback (AVB) breathing guidance over a course of liver stereotactic body radiotherapy (SBRT) investigating interfraction reproducibility.MethodsFive liver cancer patients underwent a screening procedure prior to CT sim during which patients underwent breathing conditions (i) AVB, or (ii) free breathing (FB). Whichever breathing condition was more regular was utilised for the patient's subsequent course of SBRT. Respiratory motion was obtained from the Varian respiratory position monitoring (RPM) system (Varian Medical Systems). Breathing motion reproducibility was assessed by the variance of displacement across 10 phase‐based respiratory bins over each patient's course of SBRT.ResultsThe screening procedure yielded the decision to utilise AVB for three patients and FB for two patients. Over the course of SBRT, AVB significantly improved the relative interfraction motion by 32%, from 22% displacement difference for FB patients to 15% difference for AVB patients. Further to this, AVB facilitated sub‐millimetre interfraction reproducibility for two AVB patients.ConclusionThere was significantly less interfraction motion with AVB than FB. These findings demonstrate that AVB is potentially a valuable tool in ensuring reproducible interfraction motion.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lowther2018,

title = {Investigation of the XCAT phantom as a validation tool in cardiac MRI tracking algorithms},

author = {Nicholas Lowther and Svenja Ipsen and Steven Marsh and Oliver Blanck and Paul Keall},

doi = {10.1016/j.ejmp.2017.12.003},

issn = {1120-1797},

year  = {2018},

date = {2018-01-00},

journal = {Physica Medica},

volume = {45},

pages = {44--51},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Huang2017,

title = {Performance assessment of a programmable five degrees-of-freedom motion platform for quality assurance of motion management techniques in radiotherapy},

author = {Chen-Yu Huang and Paul Keall and Adam Rice and Emma Colvill and Jin Aun Ng and Jeremy T. Booth},

doi = {10.1007/s13246-017-0572-0},

issn = {1879-5447},

year  = {2017},

date = {2017-09-00},

journal = {Australas Phys Eng Sci Med},

volume = {40},

number = {3},

pages = {643--649},

publisher = {Springer Science and Business Media LLC},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ekpo2017,

title = {Doctoral profile of the medical radiation sciences: a baseline for Australia and New Zealand},

author = {Ernest U. Ekpo and Beverly Snaith and Martine A. Harris and Mark F. McEntee},

doi = {10.1002/jmrs.231},

issn = {2051-3895},

year  = {2017},

date = {2017-09-00},

journal = {J Med Radiat Sci},

volume = {64},

number = {3},

pages = {195--202},

publisher = {Wiley},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Oborn2017,

title = {Future of medical physics: Real‐time MRI‐guided proton therapy},

author = {Bradley M. Oborn and Stephen Dowdell and Peter E. Metcalfe and Stuart Crozier and Radhe Mohan and Paul J. Keall},

doi = {10.1002/mp.12371},

issn = {2473-4209},

year  = {2017},

date = {2017-08-00},

journal = {Medical Physics},

volume = {44},

number = {8},

publisher = {Wiley},

abstract = {With the recent clinical implementation of real‐time MRI‐guided x‐ray beam therapy (MRXT), attention is turning to the concept of combining real‐time MRI guidance with proton beam therapy; MRI‐guided proton beam therapy (MRPT). MRI guidance for proton beam therapy is expected to offer a compelling improvement to the current treatment workflow which is warranted arguably more than for x‐ray beam therapy. This argument is born out of the fact that proton therapy toxicity outcomes are similar to that of the most advanced IMRT treatments, despite being a fundamentally superior particle for cancer treatment.In this Future of Medical Physics article, we describe the various software and hardware aspects of potential MRPT systems and the corresponding treatment workflow. Significant software developments, particularly focused around adaptive MRI‐based planning will be required. The magnetic interaction between the MRI and the proton beamline components will be a key area of focus. For example, the modeling and potential redesign of a magnetically compatible gantry to allow for beam delivery from multiple angles towards a patient located within the bore of an MRI scanner. Further to this, the accuracy of pencil beam scanning and beam monitoring in the presence of an MRI fringe field will require modeling, testing, and potential further development to ensure that the highly targeted radiotherapy is maintained.Looking forward we envisage a clear and accelerated path for hardware development, leveraging from lessons learnt from MRXT development. Within few years, simple prototype systems will likely exist, and in a decade, we could envisage coupled systems with integrated gantries. Such milestones will be key in the development of a more efficient, more accurate, and more successful form of proton beam therapy for many common cancer sites.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hegi‐Johnson2017,

title = {Evaluating the accuracy of 4D‐CT ventilation imaging: First comparison with Technegas SPECT ventilation},

author = {Fiona Hegi‐Johnson and Paul Keall and Jeff Barber and Chuong Bui and John Kipritidis},

doi = {10.1002/mp.12317},

issn = {2473-4209},

year  = {2017},

date = {2017-08-00},

journal = {Medical Physics},

volume = {44},

number = {8},

pages = {4045--4055},

publisher = {Wiley},

abstract = {PurposeComputed tomography ventilation imaging (CTVI) is a highly accessible functional lung imaging modality that can unlock the potential for functional avoidance in lung cancer radiation therapy. Previous attempts to validate CTVI against clinical ventilation single‐photon emission computed tomography (V‐SPECT) have been hindered by radioaerosol clumping artifacts. This work builds on those studies by performing the first comparison of CTVI with 99mTc‐carbon (‘Technegas’), a clinical V‐SPECT modality featuring smaller radioaerosol particles with less clumping.MethodsEleven lung cancer radiotherapy patients with early stage (T1/T2N0) disease received treatment planning four‐dimensional CT (4DCT) scans paired with Technegas V/Q‐SPECT/CT. For each patient, we applied three different CTVI methods. Two of these used deformable image registration (DIR) to quantify breathing‐induced lung density changes (CTVIDIR‐HU), or breathing‐induced lung volume changes (CTVIDIR‐Jac) between the 4DCT exhale/inhale phases. A third method calculated the regional product of air‐tissue densities (CTVIHU) and did not involve DIR. Corresponding CTVI and V‐SPECT scans were compared using the Dice similarity coefficient (DSC) for functional defect and nondefect regions, as well as the Spearman's correlation r computed over the whole lung. The DIR target registration error (TRE) was quantified using both manual and computer‐selected anatomic landmarks.ResultsInterestingly, the overall best performing method (CTVIHU) did not involve DIR. For nondefect regions, the CTVIHU, CTVIDIR‐HU, and CTVIDIR‐Jac methods achieved mean DSC values of 0.69, 0.68, and 0.54, respectively. For defect regions, the respective DSC values were moderate: 0.39, 0.33, and 0.44. The Spearman r‐values were generally weak: 0.26 for CTVIHU, 0.18 for CTVIDIR‐HU, and −0.02 for CTVIDIR‐Jac. The spatial accuracy of CTVI was not significantly correlated with TRE, however the DIR accuracy itself was poor with TRE > 3.6 mm on average, potentially indicative of poor quality 4DCT. Q‐SPECT scans achieved good correlations with V‐SPECT (mean r > 0.6), suggesting that the image quality of Technegas V‐SPECT was not a limiting factor in this study.ConclusionsWe performed a validation of CTVI using clinically available 4DCT and Technegas V/Q‐SPECT for 11 lung cancer patients. The results reinforce earlier findings that the spatial accuracy of CTVI exhibits significant interpatient and intermethod variability. We propose that the most likely factor affecting CTVI accuracy was poor image quality of clinical 4DCT.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lee2017,

title = {Audiovisual biofeedback guided breath-hold improves lung tumor position reproducibility and volume consistency},

author = {Danny Lee and Peter B. Greer and Carminia Lapuz and Joanna Ludbrook and Perry Hunter and Jameen Arm and Sean Pollock and Kuldeep Makhija and Ricky T. O'Brien and Taeho Kim and Paul Keall},

doi = {10.1016/j.adro.2017.03.002},

issn = {2452-1094},

year  = {2017},

date = {2017-07-00},

journal = {Advances in Radiation Oncology},

volume = {2},

number = {3},

pages = {354--362},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Feain2017,

title = {Technical Note: The design and function of a horizontal patient rotation system for the purposes of fixed-beam cancer radiotherapy},

author = {Ilana Feain and Lloyd Coleman and Hue Wallis and Richard Sokolov and Ricky O'Brien and Paul Keall},

doi = {10.1002/mp.12219},

issn = {0094-2405},

year  = {2017},

date = {2017-06-00},

journal = {Med. Phys.},

volume = {44},

number = {6},

pages = {2490--2502},

publisher = {Wiley},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Whelan2017b,

title = {Development and testing of a database of NIH research funding of AAPM members: A report from the AAPM Working Group for the Development of a Research Database (WGDRD)},

author = {Brendan Whelan and Eduardo G. Moros and Rebecca Fahrig and James Deye and Thomas Yi and Michael Woodward and Paul Keall and Jeff H. Siewerdsen},

doi = {10.1002/mp.12098},

issn = {0094-2405},

year  = {2017},

date = {2017-04-00},

journal = {Med. Phys.},

volume = {44},

number = {4},

pages = {1590--1601},

publisher = {Wiley},

keywords = {},

pubstate = {published},

tppubtype = {article}

}