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Jafarzadeh H, Antaki M, Mao X, Duclos M, Maleki F, Enger SA. Penalty weight tuning in high dose rate brachytherapy using multi-objective Bayesian optimization. Phys Med Biol 2024; 69:115024. [PMID: 38670145 DOI: 10.1088/1361-6560/ad4448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 04/26/2024] [Indexed: 04/28/2024]
Abstract
Objective.Treatment plan optimization in high dose rate brachytherapy often requires manual fine-tuning of penalty weights for each objective, which can be time-consuming and dependent on the planner's experience. To automate this process, this study used a multi-criteria approach called multi-objective Bayesian optimization with q-noisy expected hypervolume improvement as its acquisition function (MOBO-qNEHVI).Approach.The treatment plans of 13 prostate cancer patients were retrospectively imported to a research treatment planning system, RapidBrachyMTPS, where fast mixed integer optimization (FMIO) performs dwell time optimization given a set of penalty weights to deliver 15 Gy to the target volume. MOBO-qNEHVI was used to find patient-specific Pareto optimal penalty weight vectors that yield clinically acceptable dose volume histogram metrics. The relationship between the number of MOBO-qNEHVI iterations and the number of clinically acceptable plans per patient (acceptance rate) was investigated. The performance time was obtained for various parameter configurations.Main results.MOBO-qNEHVI found clinically acceptable treatment plans for all patients. With increasing the number of MOBO-qNEHVI iterations, the acceptance rate grew logarithmically while the performance time grew exponentially. Fixing the penalty weight of the tumour volume to maximum value, adding the target dose as a parameter, initiating MOBO-qNEHVI with 25 parallel sampling of FMIO, and running 6 MOBO-qNEHVI iterations found solutions that delivered 15 Gy to the hottest 95% of the clinical target volume while respecting the dose constraints to the organs at risk. The average acceptance rate for each patient was 89.74% ± 8.11%, and performance time was 66.6 ± 12.6 s. The initiation took 22.47 ± 7.57 s, and each iteration took 7.35 ± 2.45 s to find one Pareto solution.Significance.MOBO-qNEHVI combined with FMIO can automatically explore the trade-offs between treatment plan objectives in a patient specific manner within a minute. This approach can reduce the dependency of plan quality on planner's experience and reduce dose to the organs at risk.
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Affiliation(s)
- Hossein Jafarzadeh
- Medical Physics Unit, Department of Oncology, McGill University, Montreal, Quebec, Canada
| | - Majd Antaki
- Medical Physics Unit, Department of Oncology, McGill University, Montreal, Quebec, Canada
| | - Ximeng Mao
- mila-Quebec AI Institute, Montréal, Quebec, Canada
| | - Marie Duclos
- McGill University Health Center, Montreal, Canada
| | - Farhard Maleki
- Department of Computer Science, University of Calgary, Calgary, AB, Canada
| | - Shirin A Enger
- Medical Physics Unit, Department of Oncology, McGill University, Montreal, Quebec, Canada
- mila-Quebec AI Institute, Montréal, Quebec, Canada
- Lady Davis Institute for Medical Research, Montreal, Quebec, Canada
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Chatigny PY, Bélanger C, Poulin É, Beaulieu L. Catheters and dose optimization using a modified CVT algorithm and multi-criteria optimization in prostate HDR brachytherapy. Med Phys 2022; 49:6575-6587. [PMID: 35892205 DOI: 10.1002/mp.15878] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 04/08/2022] [Accepted: 06/24/2022] [Indexed: 11/07/2022] Open
Abstract
Currently, in HDR brachytherapy planning, the catheter's positions are often selected by the planner which involves the planner's experience. The catheters are then inserted using a template which helps to guide the catheters. For certain applications, it is of interest to choose the optimal location and number of catheters needed for dose coverage and potential decrease of the treatment's toxicity. Hence, it is of great importance to develop patient-specific algorithms for catheters and dose optimization. A modified Centroidal Voronoi tessellation (CVT) algorithm is implemented and merged with a GPU-based multi-criteria optimization algorithm (gMCO). The CVT algorithm optimizes the catheters' positions, and the gMCO algorithm optimizes the dwell times and dwell positions. The CVT algorithm can be used simultaneously for insertion with or without a template. Some improvements to the CVT algorithm are presented such as a new way of considering the area that needs to be covered. One hundred and eight previously treated prostates HDR cases using real-time ultrasound (US) are used to evaluate the different optimization procedures. The plan robustness is evaluated using two types of errors; deviations (random) in the insertion and deviation (systematic) in the reconstruction of the catheters. Using gMCO on clinically inserted catheter increases the acceptance rate by 37% for RTOG criteria. Our results show that all the patients respect RTOG criteria with 11 catheters using CVT+gMCO with a template of 5 mm. The number of catheters needed for all patients to respect RTOG criteria with the freehand technique is 10 catheters using CVT+gMCO. When deviations are introduced, using a template, the acceptance rate goes to 85% with 3 mm deviations using 11 catheters. This decrease is less significant when the number of catheters is higher, decreasing by less than 5% with a 3 mm deviation using 13 catheters or more. In conclusion, it is feasible to decrease the number of catheters needed to treat most patients. Some cases still need a high number of catheters to reach the plan's criteria. Using gMCO allows an increase in the plan quality while using CVT reduces the number of catheters. A higher number of catheters equates to plans that are more robust to deviations. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Philippe Y Chatigny
- Département de physique, de génie physique et d'optique, et Centre de recherche sur le cancer, Université Laval, Québec, Québec, Canada.,Service de physique médicale et de radioprotection, Centre intégré de cancérologie, CHU de Québec-Université Laval et Centre de recherche du CHU de Québec, Québec, Québec, Canada
| | - Cédric Bélanger
- Département de physique, de génie physique et d'optique, et Centre de recherche sur le cancer, Université Laval, Québec, Québec, Canada.,Service de physique médicale et de radioprotection, Centre intégré de cancérologie, CHU de Québec-Université Laval et Centre de recherche du CHU de Québec, Québec, Québec, Canada
| | - Éric Poulin
- Service de physique médicale et de radioprotection, Centre intégré de cancérologie, CHU de Québec-Université Laval et Centre de recherche du CHU de Québec, Québec, Québec, Canada
| | - Luc Beaulieu
- Département de physique, de génie physique et d'optique, et Centre de recherche sur le cancer, Université Laval, Québec, Québec, Canada.,Service de physique médicale et de radioprotection, Centre intégré de cancérologie, CHU de Québec-Université Laval et Centre de recherche du CHU de Québec, Québec, Québec, Canada
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Song WY, Robar JL, Morén B, Larsson T, Carlsson Tedgren Å, Jia X. Emerging technologies in brachytherapy. Phys Med Biol 2021; 66. [PMID: 34710856 DOI: 10.1088/1361-6560/ac344d] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 10/28/2021] [Indexed: 01/15/2023]
Abstract
Brachytherapy is a mature treatment modality. The literature is abundant in terms of review articles and comprehensive books on the latest established as well as evolving clinical practices. The intent of this article is to part ways and look beyond the current state-of-the-art and review emerging technologies that are noteworthy and perhaps may drive the future innovations in the field. There are plenty of candidate topics that deserve a deeper look, of course, but with practical limits in this communicative platform, we explore four topics that perhaps is worthwhile to review in detail at this time. First, intensity modulated brachytherapy (IMBT) is reviewed. The IMBT takes advantage ofanisotropicradiation profile generated through intelligent high-density shielding designs incorporated onto sources and applicators such to achieve high quality plans. Second, emerging applications of 3D printing (i.e. additive manufacturing) in brachytherapy are reviewed. With the advent of 3D printing, interest in this technology in brachytherapy has been immense and translation swift due to their potential to tailor applicators and treatments customizable to each individual patient. This is followed by, in third, innovations in treatment planning concerning catheter placement and dwell times where new modelling approaches, solution algorithms, and technological advances are reviewed. And, fourth and lastly, applications of a new machine learning technique, called deep learning, which has the potential to improve and automate all aspects of brachytherapy workflow, are reviewed. We do not expect that all ideas and innovations reviewed in this article will ultimately reach clinic but, nonetheless, this review provides a decent glimpse of what is to come. It would be exciting to monitor as IMBT, 3D printing, novel optimization algorithms, and deep learning technologies evolve over time and translate into pilot testing and sensibly phased clinical trials, and ultimately make a difference for cancer patients. Today's fancy is tomorrow's reality. The future is bright for brachytherapy.
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Affiliation(s)
- William Y Song
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia, United States of America
| | - James L Robar
- Department of Radiation Oncology, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Björn Morén
- Department of Mathematics, Linköping University, Linköping, Sweden
| | - Torbjörn Larsson
- Department of Mathematics, Linköping University, Linköping, Sweden
| | - Åsa Carlsson Tedgren
- Radiation Physics, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.,Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden.,Department of Oncology Pathology, Karolinska Institute, Stockholm, Sweden
| | - Xun Jia
- Innovative Technology Of Radiotherapy Computations and Hardware (iTORCH) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
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Morén B, Larsson T, Tedgren ÅC. Optimization in treatment planning of high dose-rate brachytherapy - Review and analysis of mathematical models. Med Phys 2021; 48:2057-2082. [PMID: 33576027 DOI: 10.1002/mp.14762] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 11/12/2020] [Accepted: 01/22/2021] [Indexed: 12/12/2022] Open
Abstract
Treatment planning in high dose-rate brachytherapy has traditionally been conducted with manual forward planning, but inverse planning is today increasingly used in clinical practice. There is a large variety of proposed optimization models and algorithms to model and solve the treatment planning problem. Two major parts of inverse treatment planning for which mathematical optimization can be used are the decisions about catheter placement and dwell time distributions. Both these problems as well as integrated approaches are included in this review. The proposed models include linear penalty models, dose-volume models, mean-tail dose models, quadratic penalty models, radiobiological models, and multiobjective models. The aim of this survey is twofold: (i) to give a broad overview over mathematical optimization models used for treatment planning of brachytherapy and (ii) to provide mathematical analyses and comparisons between models. New technologies for brachytherapy treatments and methods for treatment planning are also discussed. Of particular interest for future research is a thorough comparison between optimization models and algorithms on the same dataset, and clinical validation of proposed optimization approaches with respect to patient outcome.
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Affiliation(s)
- Björn Morén
- Department of Mathematics, Linköping University, Linköping, Sweden
| | - Torbjörn Larsson
- Department of Mathematics, Linköping University, Linköping, Sweden
| | - Åsa Carlsson Tedgren
- Radiation Physics, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.,Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden.,Department of Oncology Pathology, Karolinska Institute, Stockholm, Sweden
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Wang X, Wang P, Tang B, Kang S, Hou Q, Wu Z, Gou C, Li L, Orlandini L, Lang J, Li J. An Inverse Dose Optimization Algorithm for Three-Dimensional Brachytherapy. Front Oncol 2020; 10:564580. [PMID: 33194640 PMCID: PMC7606999 DOI: 10.3389/fonc.2020.564580] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 09/30/2020] [Indexed: 12/24/2022] Open
Abstract
PURPOSE To investigate an implementation method and the results of an inverse dose optimization algorithm, Gradient Based Planning Optimization (GBPO), for three-dimensional brachytherapy. METHODS The GBPO used a quadratic objective function, and a dwell time modulation item was added to the objective function to restrict the dwell time variance. We retrospectively studied 4 cervical cancer patients using different applicators and 15 cervical cancer patients using the Fletcher applicator. We assessed the plan quality of GBPO by isodose lines for the patients using different applicators. For the 15 patients using the Fletcher applicator, we utilized dose-volume histogram (DVH) parameters of HR-CTV (D100%, V150%) and organs at risk (OARs) (D0.1cc, D1cc, D2cc) to evaluate the difference between the GBPO plans and the IPSA (Inverse Planning Simulated Annealing) plans, as well as the GBPO plans and the Graphic plans. RESULTS For the 4 patients using different applicators, the dose distributions are conformable. For the 15 patients using the Fletcher applicator, when the dwell time modulation factor (DTMF) is less than 20, the dwell time deviation reduces quickly; however, after the DTMF increased to 100, the dwell time deviation has no remarkable change. The difference in dosimetric parameters between the GBPO plans and the IPSA plans is not statistically significant (P>0.05). The GBPO plans have a higher D100% (3.57 ± 0.36, 3.38 ± 0.34; P<0.01) and a lower V150% (55.73 ± 4.06, 57.75 ± 3.79; P<0.01) than those of the Graphic plans. The differences in other DVH parameters are negligible between the GBPO plans and the Graphic plans. CONCLUSIONS The GBPO plans have a comparable quality as the IPSA plans and the Graphic plans for the studied cervical cancer cases. The GBPO algorithm could be integrated into a three-dimensional brachytherapy treatment planning system after studying more sites.
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Affiliation(s)
- Xianliang Wang
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology Key Laboratory Of Sichuan Province, Chengdu, China
| | - Pei Wang
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology Key Laboratory Of Sichuan Province, Chengdu, China
| | - Bin Tang
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology Key Laboratory Of Sichuan Province, Chengdu, China
| | - Shengwei Kang
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology Key Laboratory Of Sichuan Province, Chengdu, China
| | - Qing Hou
- Key Laboratory of Radiation Physics and Technology, Institute of Nuclear Science and Technology, Sichuan University, Chengdu, China
| | - Zhangwen Wu
- Key Laboratory of Radiation Physics and Technology, Institute of Nuclear Science and Technology, Sichuan University, Chengdu, China
| | - Chengjun Gou
- Key Laboratory of Radiation Physics and Technology, Institute of Nuclear Science and Technology, Sichuan University, Chengdu, China
| | - Lintao Li
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology Key Laboratory Of Sichuan Province, Chengdu, China
| | - Lucia Orlandini
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology Key Laboratory Of Sichuan Province, Chengdu, China
| | - Jinyi Lang
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology Key Laboratory Of Sichuan Province, Chengdu, China
| | - Jie Li
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology Key Laboratory Of Sichuan Province, Chengdu, China
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van der Meer MC, Bosman PA, Niatsetski Y, Alderliesten T, van Wieringen N, Pieters BR, Bel A. Bi-objective optimization of catheter positions for high-dose-rate prostate brachytherapy. Med Phys 2020; 47:6077-6086. [PMID: 33000874 PMCID: PMC7821293 DOI: 10.1002/mp.14505] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 08/07/2020] [Accepted: 09/02/2020] [Indexed: 11/16/2022] Open
Abstract
Purpose Bi‐objective simultaneous optimization of catheter positions and dwell times for high‐dose‐rate (HDR) prostate brachytherapy, based directly on dose‐volume indices, has shown promising results. However, optimization with the state‐of‐the‐art evolutionary algorithm MO‐RV‐GOMEA so far required several hours of runtime, and resulting catheter positions were not always clinically feasible. The aim of this study is to extend the optimization model and apply GPU parallelization to achieve clinically acceptable computation times. The resulting optimization procedure is compared with a previously introduced method based solely on geometric criteria, the adapted Centroidal Voronoi Tessellations (CVT) algorithm. Methods Bi‐objective simultaneous optimization was performed with a GPU‐parallelized version of MO‐RV‐GOMEA. This optimization of catheter positions and dwell times was retrospectively applied to the data of 26 patients previously treated with HDR prostate brachytherapy for 8–16 catheters (steps of 2). Optimization of catheter positions using CVT was performed in seconds, after which optimization of only the dwell times using MO‐RV‐GOMEA was performed in 1 min. Results Simultaneous optimization of catheter positions and dwell times using MO‐RV‐GOMEA was performed in 5 min. For 16 down to 8 catheters (steps of 2), MO‐RV‐GOMEA found plans satisfying the planning‐aims for 20, 20, 18, 14, and 11 out of the 26 patients, respectively. CVT achieved this for 19, 17, 13, 9, and 2 patients, respectively. The P‐value for the difference between MO‐RV‐GOMEA and CVT was 0.023 for 16 catheters, 0.005 for 14 catheters, and <0.001 for 12, 10, and 8 catheters. Conclusions With bi‐objective simultaneous optimization on a GPU, high‐quality catheter positions can now be obtained within 5 min, which is clinically acceptable, but slower than CVT. For 16 catheters, the difference between MO‐RV‐GOMEA and CVT is clinically irrelevant. For 14 catheters and less, MO‐RV‐GOMEA outperforms CVT in finding plans satisfying all planning‐aims.
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Affiliation(s)
| | - Peter A.N. Bosman
- Life Sciences and Health research groupCentrum Wiskunde & InformaticaAmsterdam1098XGThe Netherlands
| | - Yury Niatsetski
- Physics and Advanced DevelopmentElektaVeenendaal3900AXThe Netherlands
| | - Tanja Alderliesten
- Department of Radiation OncologyLeiden University Medical CenterLeiden2300RCThe Netherlands
| | - Niek van Wieringen
- Department of Radiation OncologyAmsterdam UMCUniversity of AmsterdamAmsterdam1100DDThe Netherlands
| | - Bradley R. Pieters
- Department of Radiation OncologyAmsterdam UMCUniversity of AmsterdamAmsterdam1100DDThe Netherlands
| | - Arjan Bel
- Department of Radiation OncologyAmsterdam UMCUniversity of AmsterdamAmsterdam1100DDThe Netherlands
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Bélanger C, Poulin É, Cui S, Vigneault É, Martin AG, Foster W, Després P, Cunha JAM, Beaulieu L. Evaluating the impact of real-time multicriteria optimizers integrated with interactive plan navigation tools for HDR brachytherapy. Brachytherapy 2020; 19:607-617. [PMID: 32713779 DOI: 10.1016/j.brachy.2020.06.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 05/05/2020] [Accepted: 06/17/2020] [Indexed: 11/16/2022]
Abstract
PURPOSE Currently in high-dose-rate (HDR) brachytherapy planning, manual fine-tuning of an objective function is a common practice. Furthermore, automated planning approaches such as multicriteria optimization (MCO) are still limited to the automatic generation of a single treatment plan. This study aims to quantify planning efficiency gains when using a graphics processing unit-based MCO (gMCO) algorithm combined with a novel graphical user interface (gMCO-GUI) that integrates efficient automated and interactive plan navigation tools. METHODS AND MATERIALS The gMCO algorithm was used to generate 1000 Pareto optimal plans per case for 379 prostate cases. gMCO-GUI was developed to allow plan navigation through all plans. gMCO-GUI integrates interactive parameter selection tools directly with the optimization algorithm to allow plan navigation. The quality of each plan was evaluated based on the Radiation Treatment Oncology Group 0924 protocol and a more stringent institutional protocol (INSTp). gMCO-GUI allows real-time time display of the dose-volume histogram indices, the dose-volume histogram curves, and the isodose lines during the plan navigation. RESULTS Over the 379 cases, the fraction of Radiation Treatment Oncology Group 0924 protocol valid plans with target coverage greater than 95% was 90.8%, compared with 66.0% for clinical plans. The fraction of INSTp valid plans with target coverage greater than 95% was 81.8%, compared with 62.3% for clinical plans. The average time to compute 1000 deliverable plans with gMCO was 12.5 s, including the full computation of the 3D dose distributions. CONCLUSIONS Combining the gMCO algorithm with automated and interactive plan navigation tools resulted in simultaneous gains in both plan quality and planning efficiency.
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Affiliation(s)
- Cédric Bélanger
- Département de physique, de génie physique et d'optique et Centre de recherche sur le cancer de l'Université Laval, CHU de Québec, Québec, Canada; Département de radio-oncologie et Centre de recherche du CHU de Québec, CHU de Québec - Université Laval, Québec, Canada
| | - Éric Poulin
- Département de radio-oncologie et Centre de recherche du CHU de Québec, CHU de Québec - Université Laval, Québec, Canada
| | - Songye Cui
- Département de physique, de génie physique et d'optique et Centre de recherche sur le cancer de l'Université Laval, CHU de Québec, Québec, Canada; Département de radio-oncologie et Centre de recherche du CHU de Québec, CHU de Québec - Université Laval, Québec, Canada
| | - Éric Vigneault
- Département de radio-oncologie et Centre de recherche du CHU de Québec, CHU de Québec - Université Laval, Québec, Canada
| | - André-Guy Martin
- Département de radio-oncologie et Centre de recherche du CHU de Québec, CHU de Québec - Université Laval, Québec, Canada
| | - William Foster
- Département de radio-oncologie et Centre de recherche du CHU de Québec, CHU de Québec - Université Laval, Québec, Canada
| | - Philippe Després
- Département de physique, de génie physique et d'optique et Centre de recherche sur le cancer de l'Université Laval, CHU de Québec, Québec, Canada; Département de radio-oncologie et Centre de recherche du CHU de Québec, CHU de Québec - Université Laval, Québec, Canada
| | - J Adam M Cunha
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA
| | - Luc Beaulieu
- Département de physique, de génie physique et d'optique et Centre de recherche sur le cancer de l'Université Laval, CHU de Québec, Québec, Canada; Département de radio-oncologie et Centre de recherche du CHU de Québec, CHU de Québec - Université Laval, Québec, Canada.
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Deufel CL, Epelman MA, Pasupathy KS, Sir MY, Wu VW, Herman MG. PNaV: A tool for generating a high-dose-rate brachytherapy treatment plan by navigating the Pareto surface guided by the visualization of multidimensional trade-offs. Brachytherapy 2020; 19:518-531. [DOI: 10.1016/j.brachy.2020.02.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 02/16/2020] [Accepted: 02/29/2020] [Indexed: 10/24/2022]
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Bouter A, Alderliesten T, Pieters BR, Bel A, Niatsetski Y, Bosman PAN. GPU‐accelerated bi‐objective treatment planning for prostate high‐dose‐rate brachytherapy. Med Phys 2019; 46:3776-3787. [DOI: 10.1002/mp.13681] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 05/22/2019] [Accepted: 06/07/2019] [Indexed: 11/06/2022] Open
Affiliation(s)
- Anton Bouter
- Centrum Wiskunde & Informatica Science Park 123 1098 XG Amsterdam The Netherlands
| | - Tanja Alderliesten
- Department of Radiation Oncology Amsterdam UMC University of Amsterdam Meibergdreef 9 1105 AZ Amsterdam The Netherlands
| | - Bradley R. Pieters
- Department of Radiation Oncology Amsterdam UMC University of Amsterdam Meibergdreef 9 1105 AZ Amsterdam The Netherlands
| | - Arjan Bel
- Department of Radiation Oncology Amsterdam UMC University of Amsterdam Meibergdreef 9 1105 AZ Amsterdam The Netherlands
| | | | - Peter A. N. Bosman
- Centrum Wiskunde & Informatica Science Park 123 1098 XG Amsterdam The Netherlands
- Delft University of Technology Van Mourik Broekmanweg 6 2628 XE Delft The Netherlands
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