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Huijben EMC, Terpstra ML, Galapon AJ, Pai S, Thummerer A, Koopmans P, Afonso M, van Eijnatten M, Gurney-Champion O, Chen Z, Zhang Y, Zheng K, Li C, Pang H, Ye C, Wang R, Song T, Fan F, Qiu J, Huang Y, Ha J, Sung Park J, Alain-Beaudoin A, Bériault S, Yu P, Guo H, Huang Z, Li G, Zhang X, Fan Y, Liu H, Xin B, Nicolson A, Zhong L, Deng Z, Müller-Franzes G, Khader F, Li X, Zhang Y, Hémon C, Boussot V, Zhang Z, Wang L, Bai L, Wang S, Mus D, Kooiman B, Sargeant CAH, Henderson EGA, Kondo S, Kasai S, Karimzadeh R, Ibragimov B, Helfer T, Dafflon J, Chen Z, Wang E, Perko Z, Maspero M. Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report. Med Image Anal 2024; 97:103276. [PMID: 39068830 DOI: 10.1016/j.media.2024.103276] [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: 03/13/2024] [Revised: 06/02/2024] [Accepted: 07/11/2024] [Indexed: 07/30/2024]
Abstract
Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information, while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning. Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: (1) MRI-to-CT and (2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans. The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (≥0.87/0.90) and gamma pass rates for photon (≥98.1%/99.0%) and proton (≥97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT. SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy. It showcased the growing capacity of deep learning to produce high-quality sCT, reducing reliance on conventional CT for treatment planning.
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Affiliation(s)
- Evi M C Huijben
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Maarten L Terpstra
- Radiotherapy Department, University Medical Center Utrecht, Utrecht, The Netherlands; Computational Imaging Group for MR Diagnostics & Therapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Arthur Jr Galapon
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Suraj Pai
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Adrian Thummerer
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands; Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Peter Koopmans
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Manya Afonso
- Wageningen University & Research, Wageningen Plant Research, Wageningen, The Netherlands
| | - Maureen van Eijnatten
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Oliver Gurney-Champion
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location University of Amsterdam, Amsterdam, The Netherlands; Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Zeli Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Yiwen Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Kaiyi Zheng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Chuanpu Li
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Haowen Pang
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China
| | - Chuyang Ye
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China
| | - Runqi Wang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Tao Song
- Fudan University, Shanghai, China
| | - Fuxin Fan
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Jingna Qiu
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Yixing Huang
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | | | | | | | | | - Pengxin Yu
- Infervision Medical Technology Co., Ltd. Beijing, China
| | - Hongbin Guo
- Department of Biomedical Engineering, Shantou University, China
| | - Zhanyao Huang
- Department of Biomedical Engineering, Shantou University, China
| | | | | | - Yubo Fan
- Department of Computer Science, Vanderbilt University, Nashville, USA
| | - Han Liu
- Department of Computer Science, Vanderbilt University, Nashville, USA
| | - Bowen Xin
- Australian e-Health Research Centre, CSIRO, Herston, Queensland, Australia
| | - Aaron Nicolson
- Australian e-Health Research Centre, CSIRO, Herston, Queensland, Australia
| | - Lujia Zhong
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA, USA
| | - Zhiwei Deng
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA, USA
| | | | | | - Xia Li
- Center for Proton Therapy, Paul Scherrer Institut, Villigen, Switzerland; Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | - Ye Zhang
- Center for Proton Therapy, Paul Scherrer Institut, Villigen, Switzerland; Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | - Cédric Hémon
- University Rennes 1, CLCC Eugène Marquis, INSERM, LTSI, Rennes, France
| | - Valentin Boussot
- University Rennes 1, CLCC Eugène Marquis, INSERM, LTSI, Rennes, France
| | | | | | - Lu Bai
- MedMind Technology Co. Ltd., Beijing, China
| | | | - Derk Mus
- MRI Guidance BV, Utrecht, The Netherlands
| | | | | | | | | | - Satoshi Kasai
- Niigata University of Health and Welfare, Niigata, Japan
| | - Reza Karimzadeh
- Image Analysis, Computational Modelling and Geometry, University of Copenhagen, Denmark
| | - Bulat Ibragimov
- Image Analysis, Computational Modelling and Geometry, University of Copenhagen, Denmark
| | | | - Jessica Dafflon
- Data Science and Sharing Team, Functional Magnetic Resonance Imaging Facility, National Institute of Mental Health, Bethesda, USA; Machine Learning Team, Functional Magnetic Resonance Imaging Facility National Institute of Mental Health, Bethesda, USA
| | - Zijie Chen
- Shenying Medical Technology (Shenzhen) Co., Ltd., Shenzhen, Guangdong, China
| | - Enpei Wang
- Shenying Medical Technology (Shenzhen) Co., Ltd., Shenzhen, Guangdong, China
| | - Zoltan Perko
- Delft University of Technology, Faculty of Applied Sciences, Department of Radiation Science and Technology, Delft, The Netherlands
| | - Matteo Maspero
- Radiotherapy Department, University Medical Center Utrecht, Utrecht, The Netherlands; Computational Imaging Group for MR Diagnostics & Therapy, University Medical Center Utrecht, Utrecht, The Netherlands.
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Fridström KML, Winter RM, Hornik N, Almberg SS, Danielsen S, Redalen KR. Evaluation of magnetic resonance imaging derived synthetic computed tomography for proton therapy planning in prostate cancer. Phys Imaging Radiat Oncol 2024; 31:100625. [PMID: 39253731 PMCID: PMC11381754 DOI: 10.1016/j.phro.2024.100625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 08/05/2024] [Accepted: 08/05/2024] [Indexed: 09/11/2024] Open
Abstract
Background and purpose Magnetic resonance imaging (MRI)-only workflow is used in photon radiotherapy (RT) today, but not yet for protons. To bring MRI-only proton RT into clinical use, proton dose calculation on MRI-derived synthetic CT (sCT) must be validated. We evaluated proton dose calculation accuracy of prostate cancer proton plans using a commercially available sCT generator already validated for photon planning. Materials and methods The retrospective planning study included 10 prostate cancer patients who underwent MRI and planning CT (pCT) before RT. sCT were generated from the MRI with MRI Planner v2.3, and compared to pCT using structural mean absolute error (MAE). The pCT was used to create one-arc volumetric modulated arc therapy (VMAT) photon plan and two-field intensity modulated proton therapy (IMPT) proton plan. Each plan was recalculated on the sCT and compared to pCT doses. Dose volume histogram parameters, gamma analyses and range differences were evaluated. Results Median MAE for the body contour was 71 HU. Dose differences between pCT and sCT were small and similar for VMAT and IMPT plans. Median (range) gamma pass rates were lower for IMPT plans with 95.8 (89.3-98.7) % compared to VMAT plans with 99.4 (91.2-99.6) %. The proton range difference was 1.0 (interquartile range -0.1 - 0.2) mm deeper for sCT compared to the reference. Conclusion MRI-only IMPT planning for prostate cancer seems feasible in a clinical setting for the evaluated beam arrangement and sCT generator. More patients and evaluation of other beam arrangements are needed for a more general conclusion.
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Affiliation(s)
- Kajsa M L Fridström
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
- Cancer Clinic, St. Olav Hospital, Trondheim, Norway
| | - René M Winter
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
| | - Natalie Hornik
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
- Eberhard Karls University of Tübingen, Tübingen, Germany
| | | | - Signe Danielsen
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
- Cancer Clinic, St. Olav Hospital, Trondheim, Norway
| | - Kathrine R Redalen
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
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Marants R, Tattenberg S, Scholey J, Kaza E, Miao X, Benkert T, Magneson O, Fischer J, Vinas L, Niepel K, Bortfeld T, Landry G, Parodi K, Verburg J, Sudhyadhom A. Validation of an MR-based multimodal method for molecular composition and proton stopping power ratio determination using ex vivo animal tissues and tissue-mimicking phantoms. Phys Med Biol 2023; 68:10.1088/1361-6560/ace876. [PMID: 37463589 PMCID: PMC10645122 DOI: 10.1088/1361-6560/ace876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 07/18/2023] [Indexed: 07/20/2023]
Abstract
Objective. Range uncertainty in proton therapy is an important factor limiting clinical effectiveness. Magnetic resonance imaging (MRI) can measure voxel-wise molecular composition and, when combined with kilovoltage CT (kVCT), accurately determine mean ionization potential (Im), electron density, and stopping power ratio (SPR). We aimed to develop a novel MR-based multimodal method to accurately determine SPR and molecular compositions. This method was evaluated in tissue-mimicking andex vivoporcine phantoms, and in a brain radiotherapy patient.Approach. Four tissue-mimicking phantoms with known compositions, two porcine tissue phantoms, and a brain cancer patient were imaged with kVCT and MRI. Three imaging-based values were determined: SPRCM(CT-based Multimodal), SPRMM(MR-based Multimodal), and SPRstoich(stoichiometric calibration). MRI was used to determine two tissue-specific quantities of the Bethe Bloch equation (Im, electron density) to compute SPRCMand SPRMM. Imaging-based SPRs were compared to measurements for phantoms in a proton beam using a multilayer ionization chamber (SPRMLIC).Main results. Root mean square errors relative to SPRMLICwere 0.0104(0.86%), 0.0046(0.45%), and 0.0142(1.31%) for SPRCM, SPRMM, and SPRstoich, respectively. The largest errors were in bony phantoms, while soft tissue and porcine tissue phantoms had <1% errors across all SPR values. Relative to known physical molecular compositions, imaging-determined compositions differed by approximately ≤10%. In the brain case, the largest differences between SPRstoichand SPRMMwere in bone and high lipids/fat tissue. The magnitudes and trends of these differences matched phantom results.Significance. Our MR-based multimodal method determined molecular compositions and SPR in various tissue-mimicking phantoms with high accuracy, as confirmed with proton beam measurements. This method also revealed significant SPR differences compared to stoichiometric kVCT-only calculation in a clinical case, with the largest differences in bone. These findings support that including MRI in proton therapy treatment planning can improve the accuracy of calculated SPR values and reduce range uncertainties.
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Affiliation(s)
- Raanan Marants
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Sebastian Tattenberg
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany
- Division of Radiation Biophysics, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jessica Scholey
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, California, United States of America
| | - Evangelia Kaza
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Xin Miao
- Siemens Medical Solutions USA Inc., Boston, Massachusetts, United States of America
| | | | - Olivia Magneson
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jade Fischer
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, and Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Medical Physics, University of Calgary, Calgary, Alberta, Canada
| | - Luciano Vinas
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, and Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Statistics, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Katharina Niepel
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| | - Thomas Bortfeld
- Division of Radiation Biophysics, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Katia Parodi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Joost Verburg
- Division of Radiation Biophysics, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Atchar Sudhyadhom
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, and Harvard Medical School, Boston, Massachusetts, United States of America
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4
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Yan S, Ngoma TA, Ngwa W, Bortfeld TR. Global democratisation of proton radiotherapy. Lancet Oncol 2023; 24:e245-e254. [PMID: 37269856 DOI: 10.1016/s1470-2045(23)00184-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 04/05/2023] [Accepted: 04/19/2023] [Indexed: 06/05/2023]
Abstract
Proton radiotherapy is an advanced treatment option compared with conventional x-ray treatment, delivering much lower doses of radiation to healthy tissues surrounding the tumour. However, proton therapy is currently not widely available. In this Review, we summarise the evolution of proton therapy to date, together with the benefits to patients and society. These developments have led to an exponential growth in the number of hospitals using proton radiotherapy worldwide. However, the gap between the number of patients who should be treated with proton radiotherapy and those who have access to it remains large. We summarise the ongoing research and development that is contributing to closing this gap, including the improvement of treatment efficiency and efficacy, and advances in fixed-beam treatments that do not require an enormously large, heavy, and costly gantry. The ultimate goal of decreasing the size of proton therapy machines to fit into standard treatment rooms appears to be within reach, and we discuss future research and development opportunities to achieve this goal.
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Affiliation(s)
- Susu Yan
- Division of Radiation Biophysics, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
| | - Twalib A Ngoma
- Department Clinical Oncology, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
| | - Wilfred Ngwa
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Information and Sciences, ICT University, Yaoundé, Cameroon
| | - Thomas R Bortfeld
- Division of Radiation Biophysics, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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5
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Qiu Z, Olberg S, den Hertog D, Ajdari A, Bortfeld T, Pursley J. Online adaptive planning methods for intensity-modulated radiotherapy. Phys Med Biol 2023; 68:10.1088/1361-6560/accdb2. [PMID: 37068488 PMCID: PMC10637515 DOI: 10.1088/1361-6560/accdb2] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 04/17/2023] [Indexed: 04/19/2023]
Abstract
Online adaptive radiation therapy aims at adapting a patient's treatment plan to their current anatomy to account for inter-fraction variations before daily treatment delivery. As this process needs to be accomplished while the patient is immobilized on the treatment couch, it requires time-efficient adaptive planning methods to generate a quality daily treatment plan rapidly. The conventional planning methods do not meet the time requirement of online adaptive radiation therapy because they often involve excessive human intervention, significantly prolonging the planning phase. This article reviews the planning strategies employed by current commercial online adaptive radiation therapy systems, research on online adaptive planning, and artificial intelligence's potential application to online adaptive planning.
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Affiliation(s)
- Zihang Qiu
- Department of Business Analytics, University of Amsterdam, The Netherlands
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, United States of America
| | - Sven Olberg
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, United States of America
| | - Dick den Hertog
- Department of Business Analytics, University of Amsterdam, The Netherlands
| | - Ali Ajdari
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, United States of America
| | - Thomas Bortfeld
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, United States of America
| | - Jennifer Pursley
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, United States of America
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Chen S, Peng Y, Qin A, Liu Y, Zhao C, Deng X, Deraniyagala R, Stevens C, Ding X. MR-based synthetic CT image for intensity-modulated proton treatment planning of nasopharyngeal carcinoma patients. Acta Oncol 2022; 61:1417-1424. [DOI: 10.1080/0284186x.2022.2140017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Shupeng Chen
- Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, MI, USA
| | - Yinglin Peng
- Department of Radiation Oncology, Sun Yat-Sen University, Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, PR China
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, PR China
| | - An Qin
- Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, MI, USA
| | - Yimei Liu
- Department of Radiation Oncology, Sun Yat-Sen University, Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, PR China
| | - Chong Zhao
- Department of Radiation Oncology, Sun Yat-Sen University, Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, PR China
| | - Xiaowu Deng
- Department of Radiation Oncology, Sun Yat-Sen University, Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, PR China
| | - Rohan Deraniyagala
- Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, MI, USA
| | - Craig Stevens
- Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, MI, USA
| | - Xuanfeng Ding
- Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, MI, USA
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7
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Gurney-Champion OJ, Landry G, Redalen KR, Thorwarth D. Potential of Deep Learning in Quantitative Magnetic Resonance Imaging for Personalized Radiotherapy. Semin Radiat Oncol 2022; 32:377-388. [DOI: 10.1016/j.semradonc.2022.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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Anto GJ, Sekaran S, Perumal B, Ramar N, Vaitheeswaran R, Karthikeyan SK. A study to determine the impact of IMPT optimization techniques on prostate synthetic CT image sets dose comparison against CT image sets. Rep Pract Oncol Radiother 2022; 27:161-169. [PMID: 35402035 PMCID: PMC8989438 DOI: 10.5603/rpor.a2022.0015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 01/20/2022] [Indexed: 11/25/2022] Open
Abstract
Background The objective of this study is to determine the impact of intensity modulated proton therapty (IMPT) optimization techniques on the proton dose comparison of commercially available magnetic resonance for calculating attenuation (MRCA T) images, a synthetic computed tomography CT (sCT) based on magnetic resonance imaging (MRI) scan against the CT images and find out the optimization technique which creates plans with the least dose differences against the regular CT image sets. Material and methods Regular CT data sets and sCT image sets were obtained for 10 prostate patients for the study. Six plans were created using six distinct IMPT optimization techniques including multi-field optimization (MFO), single field uniform dose (SFUD) optimization, and robust optimization (RO) in CT image sets. These plans were copied to MRCA T, sCT datasets and doses were computed. Doses from CT and MRCA T data sets were compared for each patient using 2D dose distribution display, dose volume histograms (DVH), homogeneity index (HI), conformation number (CN) and 3D gamma analysis. A two tailed t-test was conducted on HI and CN with 5% significance level with a null hypothesis for CT and sCT image sets. Results Analysis of ten CT and sCT image sets with different IMPT optimization techniques shows that a few of the techniques show significant differences between plans for a few evaluation parameters. Isodose lines, DVH, HI, CN and t-test analysis shows that robust optimizations with 2% range error incorporated results in plans, when re-computed in sCT image sets results in the least dose differences against CT plans compared to other optimization techniques. The second best optimization technique with the least dose differences was robust optimization with 5% range error. Conclusion This study affirmatively demonstrates the impact of IMPT optimization techniques on synthetic CT image sets dose comparison against CT images and determines the robust optimization with 2% range error as the optimization technique which gives the least dose difference when compared to CT plans.
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Affiliation(s)
- Gipson Joe Anto
- Department of Medical Physics, Bharathiar University, Coimbatore, India
| | - Sureka Sekaran
- Department of Medical Physics, Bharathiar University, Coimbatore, India
| | - Bojarajan Perumal
- Department of Medical Physics, Bharathiar University, Coimbatore, India
| | - Natarajan Ramar
- Department of Physics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, India
| | - R Vaitheeswaran
- Department of Medical Physics, Bharathiar University, Coimbatore, India
| | - S K Karthikeyan
- Department of Medical Physics, Bharathiar University, Coimbatore, India
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Zimmermann L, Knäusl B, Stock M, Lütgendorf-Caucig C, Georg D, Kuess P. An MRI sequence independent convolutional neural network for synthetic head CT generation in proton therapy. Z Med Phys 2021; 32:218-227. [PMID: 34920940 PMCID: PMC9948837 DOI: 10.1016/j.zemedi.2021.10.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 10/11/2021] [Accepted: 10/19/2021] [Indexed: 12/11/2022]
Abstract
A magnetic resonance imaging (MRI) sequence independent deep learning technique was developed and validated to generate synthetic computed tomography (sCT) scans for MR guided proton therapy. 47 meningioma patients previously undergoing proton therapy based on pencil beam scanning were divided into training (33), validation (6), and test (8) cohorts. T1, T2, and contrast enhanced T1 (T1CM) MRI sequences were used in combination with the planning CT (pCT) data to train a 3D U-Net architecture with ResNet-Blocks. A hyperparameter search was performed including two loss functions, two group sizes of normalisation, and depth of the network. Training outcome was compared between models trained for each individual MRI sequence and for all sequences combined. The performance was evaluated based on a metric and dosimetric analysis as well as spot difference maps. Furthermore, the influence of immobilisation masks that are not visible on MRIs was investigated. Based on the hyperparameter search, the final model was trained with fixed features per group for the group normalisation, six down-convolution steps, an input size of 128×192×192, and feature loss. For the test dataset for body/bone the mean absolute error (MAE) values were on average 79.8/216.3Houndsfield unit (HU) when trained using T1 images, 71.1/186.1HU for T2, and 82.9/236.4HU for T1CM. The structural similarity metric (SSIM) ranged from 0.95 to 0.98 for all sequences. The investigated dose parameters of the target structures agreed within 1% between original proton treatment plans and plans recalculated on sCTs. The spot difference maps had peaks at ±0.2cm and for 98% of all spots the difference was less than 1cm. A novel MRI sequence independent sCT generator was developed, which suggests that the training phase of neural networks can be disengaged from specific MRI acquisition protocols. In contrast to previous studies, the patient cohort consisted exclusively of actual proton therapy patients (i.e. "real-world data").
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Affiliation(s)
- Lukas Zimmermann
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria,Faculty of Engineering, University of Applied Sciences Wiener Neustadt, Austria,Competence Center for Preclinical Imaging and Biomedical Engineering, University of Applied Sciences Wiener Neustadt, Austria
| | - Barbara Knäusl
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria,MedAustron Ion Therapy Center, Wiener Neustadt, Austria
| | - Markus Stock
- MedAustron Ion Therapy Center, Wiener Neustadt, Austria
| | | | - Dietmar Georg
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Peter Kuess
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria; MedAustron Ion Therapy Center, Wiener Neustadt, Austria.
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Guerreiro F, Svensson S, Seravalli E, Traneus E, Raaymakers BW. Intra-fractional per-beam adaptive workflow to mitigate the need for a rotating gantry during MRI-guided proton therapy. Phys Med Biol 2021; 66. [PMID: 34298523 DOI: 10.1088/1361-6560/ac176f] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 07/23/2021] [Indexed: 11/12/2022]
Abstract
The integration of real-time magnetic resonance imaging (MRI)-guidance and proton therapy would potentially improve the proton dose steering capability by reducing daily uncertainties due to anatomical variations. The use of a fixed beamline coupled with an axial patient couch rotation would greatly simplify the proton delivery with MRI-guidance. Nonetheless, it is mandatory to assure that the plan quality is not deteriorated by the anatomical deformations due to patient rotation. In this work, an in-house tool allowing for intra-fractional per-beam adaptation of intensity-modulated proton plans (BeamAdapt) was implemented through features available in RayStation. A set of three MRIs was acquired for two healthy volunteers (V1, V2): (1) no rotation/static, (2) rotation to the right and (3) left. V1 was rotated by 15º, to simulate a clinical pediatric abdominal case and V2 by 45º, to simulate an extreme patient rotation case. For each volunteer, a total of four intensity-modulated pencil beam scanning plans were optimized on the static MRI using virtual abdominal targets and 2-3 posterior-oblique beams. Beam angles were defined according to the angulations on the rotated MRIs. With BeamAdapt, each original plan was first converted into separate plans with one beam per plan. In an iterative order, individual beam doses were non-rigidly deformed to the rotated anatomies and re-optimized accounting for the consequent deformations and the beam doses delivered so far. For evaluation, the final adapted dose distribution was propagated back to the static MRI. Planned and adapted dose distributions were compared by computing relative differences between dose-volume histogram (DVH) metrics. Absolute target dose differences were on average below 1% and mean dose organs-at-risk differences were below 3%. With BeamAdapt, not only intra-fractional per-beam proton plan adaptation coupled with axial patient rotation is possible but also the need for a rotating gantry during MRI-guidance might be mitigated.
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Affiliation(s)
- Filipa Guerreiro
- Department of Radiotherapy, University Medical Center Utrecht Imaging Division, Utrecht, NETHERLANDS
| | | | - Enrica Seravalli
- Department of Radiotherapy, University Medical Center Utrecht Imaging Division, Utrecht, NETHERLANDS
| | - Erik Traneus
- RaySearch Laboratories AB, Stockholm, Stockholm, SWEDEN
| | - Bas W Raaymakers
- Department of Radiotherapy, University Medical Center Utrecht Imaging Division, Utrecht, NETHERLANDS
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11
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van Lier ALHMW, Meijers LTC, Philippens MEP, Hes J, Raaymakers BW, van der Voort van Zyp JRN, de Boer JCJ. Geometrical imaging accuracy and imaging and plan quality for prostate cancer on a 1.5T MRLinac in patients with a unilateral hip implant. Phys Med Biol 2021; 66. [PMID: 34243173 DOI: 10.1088/1361-6560/ac1302] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 07/09/2021] [Indexed: 11/11/2022]
Abstract
PURPOSE To assess the feasibility of prostate cancer radiotherapy for patients with a hip implant on an 1.5T MRI-Linac (MRL) in terms of geometrical image accuracy, image quality, and plan quality. METHODS Pretreatment MRI images on a 1.5T MRL and 3T MRI consisting of a T2-weighted 3D delineation scan and main magnetic field homogeneity (B0) scan were performed in 6 patients with a unilateral hip implant. System specific geometrical errors due to gradient non-linearity were determined for the MRL. Within the prostate and skin contour, B0 inhomogeneity, gradient non-linearity error and the total geometrical error (vector summation of the prior two) was determined. Image quality was determined by visually scoring the extent of implant-born image artifacts. A treatment planning study was performed on 5 patients to quantify the impact of the implant on plan quality, in which conventional MRL IMRT plans were created, as well as plans which avoid radiation through the left or right femur. RESULTS The total maximum geometrical error in the prostate was < 1 mm and the skin contour < 1.7 mm; in all cases the machine-specific gradient error was most dominant. The B0 error for the MRlinac MRI could partly be predicted based on the pre-treatment 3T scan. Image quality for all patients was sufficient at 1.5T MRL. Plan comparison showed that, even with avoidance of the hips, in all cases sufficient target coverage could be obtained with similar D1cc and D5cc to rectum and bladder, while V28Gy was slightly poorer in only the rectum for femur avoidance. CONCLUSION We showed that geometrical accuracy, image quality and plan quality for six prostate patients with a hip implant or hip fixation treated on a 1.5T MRL did not show relevant deterioration for the used image settings, which allowed safe treatment.
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Affiliation(s)
- Astrid L H M W van Lier
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands, Utrecht, NETHERLANDS
| | - Lieke T C Meijers
- radiotherapy, University Medical Center Utrecht, Utrecht, NETHERLANDS
| | - Marielle E P Philippens
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands, Utrecht, NETHERLANDS
| | - Jochem Hes
- Department of Radiotherapy, UMC Utrecht, Utrecht, NETHERLANDS
| | - Bas W Raaymakers
- Department of Radiotherapy, Universitair Medisch Centrum Utrecht, HP Q.00.118, Heidelberglaan 100, 3584 CX Utrecht, THE NETHERLANDS, Utrecht, NETHERLANDS
| | | | - J C J de Boer
- Department of Radiotherapy, Universitair Medisch Centrum Utrecht, Utrecht, NETHERLANDS
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12
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Nosrati R, Lam WW, Paudel M, Pejović-Milić A, Morton G, Stanisz GJ. Feasibility of using a single MRI acquisition for fiducial marker localization and synthetic CT generation towards MRI-only prostate radiation therapy treatment planning. Biomed Phys Eng Express 2021; 7. [PMID: 34034242 DOI: 10.1088/2057-1976/ac0501] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 05/25/2021] [Indexed: 11/12/2022]
Abstract
Purpose.To investigate the feasibility of using a single MRI acquisition for fiducial marker identification and synthetic CT (sCT) generation towards MRI-only treatment planning for prostate external beam radiation therapy (EBRT).Methods.Seven prostate cancer patients undergoing EBRT, each with three implanted gold fiducial markers, participated in this study. In addition to the planning CT scan, all patients were scanned on a 3 T MR scanner with a 3D double-echo gradient echo (GRE) sequence. Quantitative susceptibility mapping (QSM) was performed for marker localization. QSM-derived marker positions were compared to those from CT. The bulk density assignment technique for sCT generation was adopted. The magnitude GRE images were segmented into muscle, bone, fat, and air using a combination of unsupervised intensity-based classification of soft tissue and convolutional neural networks (CNN) for bone segmentation.Results.All implanted markers were visualized and accurately identified (average error: 0.7 ± 0.5 mm). QSM generated distinctive contrast for hemorrhage, calcifications, and gold fiducial markers. The estimated susceptibility/HU values on QSM/CT for gold and calcifications were 31.5 ± 2.9 ppm/1220 ± 100 HU and 14.6 ± 0.9 ppm/440 ± 100 HU, respectively. The intensity-based soft tissue classification resulted in an average Dice score of 0.97 ± 0.02; bone segmentation using CNN resulted in an average Dice score of 0.93 ± 0.03.Conclusion.This work indicates the feasibility of simultaneous fiducial marker identification and sCT generation using a single MRI acquisition. Future works includes evaluation of the proposed method in a large cohort of patients with optimized acquisition parameters as well as dosimetric evaluations.
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Affiliation(s)
- R Nosrati
- Harvard Medical School, Boston, MA, United States of America.,Boston Children's Hospital, Boston, MA, United States of America
| | - W W Lam
- Sunnybrook Health Sciences Centre, ON, Canada
| | - M Paudel
- Sunnybrook Health Sciences Centre, ON, Canada.,University of Toronto, Toronto, ON, Canada
| | | | - G Morton
- Sunnybrook Health Sciences Centre, ON, Canada.,University of Toronto, Toronto, ON, Canada
| | - G J Stanisz
- Sunnybrook Health Sciences Centre, ON, Canada.,University of Toronto, Toronto, ON, Canada
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13
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Groot Koerkamp ML, de Hond YJM, Maspero M, Kontaxis C, Mandija S, Vasmel JE, Charaghvandi RK, Philippens MEP, van Asselen B, van den Bongard HJGD, Hackett SS, Houweling AC. Synthetic CT for single-fraction neoadjuvant partial breast irradiation on an MRI-linac. Phys Med Biol 2021; 66. [PMID: 33761491 DOI: 10.1088/1361-6560/abf1ba] [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: 01/29/2021] [Accepted: 03/24/2021] [Indexed: 01/08/2023]
Abstract
A synthetic computed tomography (sCT) is required for daily plan optimization on an MRI-linac. Yet, only limited information is available on the accuracy of dose calculations on sCT for breast radiotherapy. This work aimed to (1) evaluate dosimetric accuracy of treatment plans for single-fraction neoadjuvant partial breast irradiation (PBI) on a 1.5 T MRI-linac calculated on a) bulk-density sCT mimicking the current MRI-linac workflow and b) deep learning-generated sCT, and (2) investigate the number of bulk-density levels required. For ten breast cancer patients we created three bulk-density sCTs of increasing complexity from the planning-CT, using bulk-density for: (1) body, lungs, and GTV (sCTBD1); (2) volumes for sCTBD1plus chest wall and ipsilateral breast (sCTBD2); (3) volumes for sCTBD2plus ribs (sCTBD3); and a deep learning-generated sCT (sCTDL) from a 1.5 T MRI in supine position. Single-fraction neoadjuvant PBI treatment plans for a 1.5 T MRI-linac were optimized on each sCT and recalculated on the planning-CT. Image evaluation was performed by assessing mean absolute error (MAE) and mean error (ME) in Hounsfield Units (HU) between the sCTs and the planning-CT. Dosimetric evaluation was performed by assessing dose differences, gamma pass rates, and dose-volume histogram (DVH) differences. The following results were obtained (median across patients for sCTBD1/sCTBD2/sCTBD3/sCTDLrespectively): MAE inside the body contour was 106/104/104/75 HU and ME was 8/9/6/28 HU, mean dose difference in the PTVGTVwas 0.15/0.00/0.00/-0.07 Gy, median gamma pass rate (2%/2 mm, 10% dose threshold) was 98.9/98.9/98.7/99.4%, and differences in DVH parameters were well below 2% for all structures except for the skin in the sCTDL. Accurate dose calculations for single-fraction neoadjuvant PBI on an MRI-linac could be performed on both bulk-density and deep learning sCT, facilitating further implementation of MRI-guided radiotherapy for breast cancer. Balancing simplicity and accuracy, sCTBD2showed the optimal number of bulk-density levels for a bulk-density approach.
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Affiliation(s)
- M L Groot Koerkamp
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Y J M de Hond
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - M Maspero
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.,Computational Imaging Group for MR diagnostics & therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - C Kontaxis
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - S Mandija
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.,Computational Imaging Group for MR diagnostics & therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - J E Vasmel
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - R K Charaghvandi
- Department of Radiation Oncology, Radboudumc, Nijmegen, The Netherlands
| | - M E P Philippens
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - B van Asselen
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - S S Hackett
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - A C Houweling
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
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14
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Paganetti H, Beltran C, Both S, Dong L, Flanz J, Furutani K, Grassberger C, Grosshans DR, Knopf AC, Langendijk JA, Nystrom H, Parodi K, Raaymakers BW, Richter C, Sawakuchi GO, Schippers M, Shaitelman SF, Teo BKK, Unkelbach J, Wohlfahrt P, Lomax T. Roadmap: proton therapy physics and biology. Phys Med Biol 2021; 66. [DOI: 10.1088/1361-6560/abcd16] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 11/23/2020] [Indexed: 12/12/2022]
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15
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Zimmermann L, Buschmann M, Herrmann H, Heilemann G, Kuess P, Goldner G, Nyholm T, Georg D, Nesvacil N. An MR-only acquisition and artificial intelligence based image-processing protocol for photon and proton therapy using a low field MR. Z Med Phys 2021; 31:78-88. [PMID: 33455822 DOI: 10.1016/j.zemedi.2020.10.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 09/14/2020] [Accepted: 10/27/2020] [Indexed: 10/22/2022]
Abstract
OBJECTIVE Recent developments on synthetically generated CTs (sCT), hybrid MRI linacs and MR-only simulations underlined the clinical feasibility and acceptance of MR guided radiation therapy. However, considering clinical application of open and low field MR with a limited field of view can result in truncation of the patient's anatomy which further affects the MR to sCT conversion. In this study an acquisition protocol and subsequent MR image stitching is proposed to overcome the limited field of view restriction of open MR scanners, for MR-only photon and proton therapy. MATERIAL AND METHODS 12 prostate cancer patients scanned with an open 0.35T scanner were included. To obtain the full body contour an enhanced imaging protocol including two repeated scans after bilateral table movement was introduced. All required structures (patient contour, target and organ at risk) were delineated on a post-processed combined transversal image set (stitched MRI). The postprocessed MR was converted into a sCT by a pretrained neural network generator. Inversely planned photon and proton plans (VMAT and SFUD) were designed using the sCT and recalculated for rigidly and deformably registered CT images and compared based on D2%, D50%, V70Gy for organs at risk and based on D2%, D50%, D98% for the CTV and PTV. The stitched MRI and the untruncated MRI were compared to the CT, and the maximum surface distance was calculated. The sCT was evaluated with respect to delineation accuracy by comparing on stitched MRI and sCT using the DICE coefficient for femoral bones and the whole body. RESULTS Maximum surface distance analysis revealed uncertainties in lateral direction of 1-3mm on average. DICE coefficient analysis confirms good performance of the sCT conversion, i.e. 92%, 93%, and 100% were obtained for femoral bone left and right and whole body. Dose comparison resulted in uncertainties below 1% between deformed CT and sCT and below 2% between rigidly registered CT and sCT in the CTV for photon and proton treatment plans. DISCUSSION A newly developed acquisition protocol for open MR scanners and subsequent Sct generation revealed good acceptance for photon and proton therapy. Moreover, this protocol tackles the restriction of the limited FOVs and expands the capacities towards MR guided proton therapy with horizontal beam lines.
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Affiliation(s)
- Lukas Zimmermann
- Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria.
| | - Martin Buschmann
- Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Harald Herrmann
- Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Gerd Heilemann
- Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Peter Kuess
- Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Gregor Goldner
- Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Tufve Nyholm
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Dietmar Georg
- Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Nicole Nesvacil
- Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
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16
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Maspero M, Bentvelzen LG, Savenije MH, Guerreiro F, Seravalli E, Janssens GO, van den Berg CA, Philippens ME. Deep learning-based synthetic CT generation for paediatric brain MR-only photon and proton radiotherapy. Radiother Oncol 2020; 153:197-204. [DOI: 10.1016/j.radonc.2020.09.029] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 09/14/2020] [Accepted: 09/14/2020] [Indexed: 02/07/2023]
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17
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Florkow MC, Guerreiro F, Zijlstra F, Seravalli E, Janssens GO, Maduro JH, Knopf AC, Castelein RM, van Stralen M, Raaymakers BW, Seevinck PR. Deep learning-enabled MRI-only photon and proton therapy treatment planning for paediatric abdominal tumours. Radiother Oncol 2020; 153:220-227. [DOI: 10.1016/j.radonc.2020.09.056] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 09/24/2020] [Accepted: 09/28/2020] [Indexed: 01/24/2023]
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18
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Dumlu HS, Meschini G, Kurz C, Kamp F, Baroni G, Belka C, Paganelli C, Riboldi M. Dosimetric impact of geometric distortions in an MRI-only proton therapy workflow for lung, liver and pancreas. Z Med Phys 2020; 32:85-97. [PMID: 33168274 PMCID: PMC9948883 DOI: 10.1016/j.zemedi.2020.10.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 09/02/2020] [Accepted: 10/01/2020] [Indexed: 12/25/2022]
Abstract
In a radiation therapy workflow based on Magnetic Resonance Imaging (MRI), dosimetric errors may arise due to geometric distortions introduced by MRI. The aim of this study was to quantify the dosimetric effect of system-dependent geometric distortions in an MRI-only workflow for proton therapy applied at extra-cranial sites. An approach was developed, in which computed tomography (CT) images were distorted using an MRI displacement map, which represented the MR distortions in a spoiled gradient-echo sequence due to gradient nonlinearities and static magnetic field inhomogeneities. A retrospective study was conducted on 4DCT/MRI digital phantoms and 18 4DCT clinical datasets of the thoraco-abdominal site. The treatment plans were designed and separately optimized for each beam in a beam specific Planning Target Volume on the distorted CT, and the final dose distribution was obtained as the average. The dose was then recalculated in undistorted CT using the same beam geometry and beam weights. The analysis was performed in terms of Dose Volume Histogram (DVH) parameters. No clinically relevant dosimetric impact was observed on organs at risk, whereas in the target structure, geometric distortions caused statistically significant variations in the planned dose DVH parameters and dose homogeneity index (DHI). The dosimetric variations in the target structure were smaller in abdominal cases (ΔD2%, ΔD98%, and ΔDmean all below 0.1% and ΔDHI below 0.003) compared to the lung cases. Indeed, lung patients with tumors isolated inside lung parenchyma exhibited higher dosimetric variations (ΔD2%≥0.3%, ΔD98%≥15.9%, ΔDmean≥3.3% and ΔDHI≥0.102) than lung patients with tumor close to soft tissue (ΔD2%≤0.4%, ΔD98%≤5.6%, ΔDmean≤0.9% and ΔDHI≤0.027) potentially due to higher density variations along the beam path. Results suggest the potential applicability of MRI-only proton therapy, provided that specific analysis is applied for isolated lung tumors.
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Affiliation(s)
- Hatice Selcen Dumlu
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy; Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748 Garching bei München, Germany
| | - Giorgia Meschini
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377 München, Germany
| | - Florian Kamp
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377 München, Germany
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy; Centro Nazionale di Adroterapia Oncologica, Strada Campeggi 53, 27100 Pavia, Italy
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377 München, Germany; German Cancer Consortium (DKTK) partner site Munich, Germany and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy
| | - Marco Riboldi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748 Garching bei München, Germany.
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19
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Tyagi N, Zelefsky MJ, Wibmer A, Zakian K, Burleson S, Happersett L, Halkola A, Kadbi M, Hunt M. Clinical experience and workflow challenges with magnetic resonance-only radiation therapy simulation and planning for prostate cancer. Phys Imaging Radiat Oncol 2020; 16:43-49. [PMID: 33134566 PMCID: PMC7598055 DOI: 10.1016/j.phro.2020.09.009] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 08/24/2020] [Accepted: 09/25/2020] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND AND PURPOSE Magnetic Resonance (MR)-only planning has been implemented clinically for radiotherapy of prostate cancer. However, fewer studies exist regarding the overall success rate of MR-only workflows. We report on successes and challenges of implementing MR-only workflows for prostate. MATERIALS AND METHODS A total of 585 patients with prostate cancer underwent an MR-only simulation and planning between 06/2016-06/2018. MR simulation included images for contouring, synthetic-CT generation and fiducial identification. Workflow interruptions occurred that required a backup CT, a re-simulation or an update to our current quality assurance (QA) process. The challenges were prospectively evaluated and classified into syn-CT generation, motion/artifacts in the MRs, fiducial QA and bowel preparation guidelines. RESULTS MR-only simulation was successful in 544 (93.2 %) patients. . In seventeen patients (2.9%), reconstruction of synthetic-CT failed due to patient size, femur angulation, or failure to determine the body contour. Twenty-four patients (4.1%) underwent a repeat/backup CT scan because of artifacts on the MR such as image blur due to patient motion or biopsy/surgical artifacts that hampered identification of the implanted fiducial markers. In patients requiring large coverage due to nodal involvement, inhomogeneity artifacts were resolved by using a two-stack acquisition and adaptive inhomogeneity correction. Bowel preparation guidelines were modified to address frequent rectum/gas issues due to longer MR scan time. CONCLUSIONS MR-only simulation has been successfully implemented for a majority of patients in the clinic. However, MR-CT or CT-only pathway may still be needed for patients where MR-only solution fails or patients with MR contraindications.
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Affiliation(s)
- Neelam Tyagi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, NY, NY 10065, United States
| | - Michael J. Zelefsky
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, NY, NY 10065, United States
| | - Andreas Wibmer
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, NY, NY 10065, United States
| | - Kristen Zakian
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, NY, NY 10065, United States
| | - Sarah Burleson
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, NY, NY 10065, United States
| | - Laura Happersett
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, NY, NY 10065, United States
| | - Aleksi Halkola
- Philips Healthcare, 595 Milner Road, Cleveland, OH 44143, United States
| | - Mo Kadbi
- Philips Healthcare, 595 Milner Road, Cleveland, OH 44143, United States
| | - Margie Hunt
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, NY, NY 10065, United States
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20
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Hoffmann A, Oborn B, Moteabbed M, Yan S, Bortfeld T, Knopf A, Fuchs H, Georg D, Seco J, Spadea MF, Jäkel O, Kurz C, Parodi K. MR-guided proton therapy: a review and a preview. Radiat Oncol 2020; 15:129. [PMID: 32471500 PMCID: PMC7260752 DOI: 10.1186/s13014-020-01571-x] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 05/17/2020] [Indexed: 02/14/2023] Open
Abstract
Background The targeting accuracy of proton therapy (PT) for moving soft-tissue tumours is expected to greatly improve by real-time magnetic resonance imaging (MRI) guidance. The integration of MRI and PT at the treatment isocenter would offer the opportunity of combining the unparalleled soft-tissue contrast and real-time imaging capabilities of MRI with the most conformal dose distribution and best dose steering capability provided by modern PT. However, hybrid systems for MR-integrated PT (MRiPT) have not been realized so far due to a number of hitherto open technological challenges. In recent years, various research groups have started addressing these challenges and exploring the technical feasibility and clinical potential of MRiPT. The aim of this contribution is to review the different aspects of MRiPT, to report on the status quo and to identify important future research topics. Methods Four aspects currently under study and their future directions are discussed: modelling and experimental investigations of electromagnetic interactions between the MRI and PT systems, integration of MRiPT workflows in clinical facilities, proton dose calculation algorithms in magnetic fields, and MRI-only based proton treatment planning approaches. Conclusions Although MRiPT is still in its infancy, significant progress on all four aspects has been made, showing promising results that justify further efforts for research and development to be undertaken. First non-clinical research solutions have recently been realized and are being thoroughly characterized. The prospect that first prototype MRiPT systems for clinical use will likely exist within the next 5 to 10 years seems realistic, but requires significant work to be performed by collaborative efforts of research groups and industrial partners.
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Affiliation(s)
- Aswin Hoffmann
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology-OncoRay, Dresden, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Bradley Oborn
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia.,Illawarra Cancer Care Centre, Wollongong Hospital, Wollongong, Australia
| | - Maryam Moteabbed
- Division of Radiation Biophysics, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Susu Yan
- Division of Radiation Biophysics, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Thomas Bortfeld
- Division of Radiation Biophysics, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Antje Knopf
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Herman Fuchs
- Department of Radiation Oncology, Medical University of Vienna/AKH, Vienna, Austria.,Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Dietmar Georg
- Department of Radiation Oncology, Medical University of Vienna/AKH, Vienna, Austria.,Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Joao Seco
- Biomedical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum DKFZ, Heidelberg, Germany.,Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - Maria Francesca Spadea
- Biomedical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum DKFZ, Heidelberg, Germany.,Department of Experimental and Clinical Medicine, Magna Graecia University, Catanzaro, Italy
| | - Oliver Jäkel
- Medical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum DKFZ and Heidelberg Ion-Beam Therapy Center at the University Medical Center, Heidelberg, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.,Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| | - Katia Parodi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany.
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21
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Kazemifar S, Barragán Montero AM, Souris K, Rivas ST, Timmerman R, Park YK, Jiang S, Geets X, Sterpin E, Owrangi A. Dosimetric evaluation of synthetic CT generated with GANs for MRI-only proton therapy treatment planning of brain tumors. J Appl Clin Med Phys 2020; 21:76-86. [PMID: 32216098 PMCID: PMC7286008 DOI: 10.1002/acm2.12856] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 02/10/2020] [Accepted: 02/14/2020] [Indexed: 12/23/2022] Open
Abstract
PURPOSE The purpose of this study was to address the dosimetric accuracy of synthetic computed tomography (sCT) images of patients with brain tumor generated using a modified generative adversarial network (GAN) method, for their use in magnetic resonance imaging (MRI)-only treatment planning for proton therapy. METHODS Dose volume histogram (DVH) analysis was performed on CT and sCT images of patients with brain tumor for plans generated for intensity-modulated proton therapy (IMPT). All plans were robustly optimized using a commercially available treatment planning system (RayStation, from RaySearch Laboratories) and standard robust parameters reported in the literature. The IMPT plan was then used to compute the dose on CT and sCT images for dosimetric comparison, using RayStation analytical (pencil beam) dose algorithm. We used a second, independent Monte Carlo dose calculation engine to recompute the dose on both CT and sCT images to ensure a proper analysis of the dosimetric accuracy of the sCT images. RESULTS The results extracted from RayStation showed excellent agreement for most DVH metrics computed on the CT and sCT for the nominal case, with a mean absolute difference below 0.5% (0.3 Gy) of the prescription dose for the clinical target volume (CTV) and below 2% (1.2 Gy) for the organs at risk (OARs) considered. This demonstrates a high dosimetric accuracy for the generated sCT images, especially in the target volume. The metrics obtained from the Monte Carlo doses mostly agreed with the values extracted from RayStation for the nominal and worst-case scenarios (mean difference below 3%). CONCLUSIONS This work demonstrated the feasibility of using sCT generated with a GAN-based deep learning method for MRI-only treatment planning of patients with brain tumor in intensity-modulated proton therapy.
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Affiliation(s)
- Samaneh Kazemifar
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ana M Barragán Montero
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.,Institut de Recherche Expérimentale et Clinique, Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Université catholique de Louvain, Brussels, Belgium
| | - Kevin Souris
- Institut de Recherche Expérimentale et Clinique, Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Université catholique de Louvain, Brussels, Belgium
| | - Sara T Rivas
- Institut de Recherche Expérimentale et Clinique, Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Université catholique de Louvain, Brussels, Belgium
| | - Robert Timmerman
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Yang K Park
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Steve Jiang
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Xavier Geets
- Institut de Recherche Expérimentale et Clinique, Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Université catholique de Louvain, Brussels, Belgium.,Department of Radiation Oncology, Cliniques universitaires Saint-Luc, Brussels, Belgium
| | - Edmond Sterpin
- Institut de Recherche Expérimentale et Clinique, Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Université catholique de Louvain, Brussels, Belgium.,Department of Oncology, Laboratory of Experimental Radiotherapy, KULeuven, Leuven, Belgium
| | - Amir Owrangi
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
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22
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Wohlfahrt P, Richter C. Status and innovations in pre-treatment CT imaging for proton therapy. Br J Radiol 2020; 93:20190590. [PMID: 31642709 PMCID: PMC7066941 DOI: 10.1259/bjr.20190590] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 10/04/2019] [Accepted: 10/21/2019] [Indexed: 12/19/2022] Open
Abstract
Pre-treatment CT imaging is a topic of growing importance in particle therapy. Improvements in the accuracy of stopping-power prediction are demanded to allow for a dose conformality that is not inferior to state-of-the-art image-guided photon therapy. Although range uncertainty has been kept practically constant over the last decades, recent technological and methodological developments, like the clinical application of dual-energy CT, have been introduced or arise at least on the horizon to improve the accuracy and precision of range prediction. This review gives an overview of the current status, summarizes the innovations in dual-energy CT and its potential impact on the field as well as potential alternative technologies for stopping-power prediction.
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Affiliation(s)
- Patrick Wohlfahrt
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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23
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Albertini F, Matter M, Nenoff L, Zhang Y, Lomax A. Online daily adaptive proton therapy. Br J Radiol 2020; 93:20190594. [PMID: 31647313 PMCID: PMC7066958 DOI: 10.1259/bjr.20190594] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 10/15/2019] [Accepted: 10/22/2019] [Indexed: 12/11/2022] Open
Abstract
It is recognized that the use of a single plan calculated on an image acquired some time before the treatment is generally insufficient to accurately represent the daily dose to the target and to the organs at risk. This is particularly true for protons, due to the physical finite range. Although this characteristic enables the generation of steep dose gradients, which is essential for highly conformal radiotherapy, it also tightens the dependency of the delivered dose to the range accuracy. In particular, the use of an outdated patient anatomy is one of the most significant sources of range inaccuracy, thus affecting the quality of the planned dose distribution. A plan should be ideally adapted as soon as anatomical variations occur, ideally online. In this review, we describe in detail the different steps of the adaptive workflow and discuss the challenges and corresponding state-of-the art developments in particular for an online adaptive strategy.
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Affiliation(s)
| | | | | | - Ye Zhang
- Paul Scherrer Institute, Center for Proton Therapy, Switzerland
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24
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Aramburu Núñez D, Fontenla S, Rydquist L, Del Rosario G, Han Z, Chen CC, Mah D, Tyagi N. Dosimetric evaluation of MR-derived synthetic-CTs for MR-only proton treatment planning. Med Dosim 2020; 45:264-270. [PMID: 32089396 DOI: 10.1016/j.meddos.2020.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 01/16/2020] [Accepted: 01/17/2020] [Indexed: 11/18/2022]
Abstract
PURPOSE To evaluate proton dose calculation accuracy of optimized pencil beam scanning (PBS) plans on MR-derived synthetic-CTs for prostate patients. MATERIAL AND METHODS Ten patient datasets with both a CT and an MRI were planned with opposed lateral proton beams optimized to single field uniform dose under an IRB-approved study. The proton plans were created on CT datasets generated by a commercial synthetic CT-based software called MRCAT (MR for Calculating ATtenuation) routinely used in our clinic for photon-based MR-only planning. A standard prescription of 79.2 Gy (RBE) and 68.4 Gy (RBE) was used for intact prostate and prostate bed cases, respectively. Proton plans were first generated and optimized using the MRCAT synthetic-CT (syn-CT), and then recalculated on the planning CT rigidly aligned with the syn-CT (aligned-CT) and a deformed planning CT (deformed-CT), which was deformed to match outer contour between syn-CT and aligned-CT. The same beam arrangement, total MUs, MUs/spot, spot positions were used to recalculate dose on deformed-CT and aligned-CT without renormalization. DVH analysis was performed on aligned-CT, deformed-CT, and syn-CT to compare D98%, V100%, V95% for PTV, PTVeval, and GTV as well as V70Gy, V50Gy for OARs. RESULTS The relative percentage dose difference between syn-CT and deformed-CT, were (0.17 ± 0.33 %) for PTVeval D98% and (0.07 ± 0.1 %) for CTV D98%. Rectum V70Gy, V50Gy, and Bladder V70Gy were (2.76 ± 4.01 %), (11.6 ± 11.2 %), and (3.41 ± 2.86 %), respectively for the syn-CT, and (3.23 ± 3.63 %), (11.3 ± 8.18 %), and (3.29 ± 2.76 %), respectively for the deformed-CT, and (1.37 ± 1.84 %), (8.48 ± 6.67 %), and (4.91 ± 3.65 %), respectively for aligned-CT. CONCLUSION Dosimetric analysis shows that MR-only proton planning is feasible using syn-CT based on current clinical margins that account for a range uncertainty.
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Affiliation(s)
| | - Sandra Fontenla
- Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | | | | | - Zhiqiang Han
- ProCure Proton Therapy Center, Somerset, NJ 08873, USA
| | | | - Dennis Mah
- ProCure Proton Therapy Center, Somerset, NJ 08873, USA
| | - Neelam Tyagi
- Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
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25
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Depauw N, Keyriläinen J, Suilamo S, Warner L, Bzdusek K, Olsen C, Kooy H. MRI-based IMPT planning for prostate cancer. Radiother Oncol 2019; 144:79-85. [PMID: 31734604 DOI: 10.1016/j.radonc.2019.10.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2019] [Revised: 10/15/2019] [Accepted: 10/16/2019] [Indexed: 11/15/2022]
Abstract
PURPOSE Treatment planning for proton therapy requires the relative proton stopping power ratio (RSP) information of the patient for accurate dose calculations. RSP are conventionally obtained after mapping of the Hounsfield units (HU) from a calibrated patient computed tomography (CT). One or multiple CT are needed for a given treatment which represents additional, undesired dose to the patient. For prostate cancer, magnetic resonance imaging (MRI) scans are the gold standard for segmentation while offering dose-less imaging. We here quantify the clinical applicability of converted MR images as a substitute for intensity modulated proton therapy (IMPT) treatment of the prostate. METHODS MRCAT (Magnetic Resonance for Calculating ATtenuation) is a Philips-developed technology which produces a synthetic CT image consisting of five HU from a specific set of MRI acquisitions. MRCAT and original planning CT data sets were obtained for ten patients. An IMPT plan was generated on the MRCAT for each patient. Plans were produced such that they fulfill the prostate protocol in use at Massachusetts General Hospital (MGH). The plans were then recomputed onto the nominal planning CT for each patient. Robustness analyses (±5 mm setup shifts and ±3.5 % range uncertainties) were also performed. RESULTS Comparison of MRCAT plans and their recomputation onto the planning CT plan showed excellent agreement. Likewise, dose perturbations due to setup shifts and range uncertainties were well within clinical acceptance demonstrating the clinical viability of the approach. CONCLUSIONS This work demonstrate the clinical acceptability of substituting MR converted RSP images instead of CT for IMPT planning of prostate cancer. This further translates into higher contouring accuracy along with lesser imaging dose.
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Affiliation(s)
- Nicolas Depauw
- Francis H. Burr Proton Therapy Center, Department of Radiation Oncology, Massachusetts General Hospital (MGH), Boston, USA.
| | - Jani Keyriläinen
- Department of Medical Physics & Department of Oncology and Radiotherapy, Turku University Hospital, Turku, Finland
| | - Sami Suilamo
- Department of Medical Physics & Department of Oncology and Radiotherapy, Turku University Hospital, Turku, Finland
| | | | - Karl Bzdusek
- Philips Healthcare, Philips Radiation Oncology Systems, Fitchburg, USA
| | - Christine Olsen
- Francis H. Burr Proton Therapy Center, Department of Radiation Oncology, Massachusetts General Hospital (MGH), Boston, USA
| | - Hanne Kooy
- Francis H. Burr Proton Therapy Center, Department of Radiation Oncology, Massachusetts General Hospital (MGH), Boston, USA
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26
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Burigo LN, Oborn BM. MRI-guided proton therapy planning: accounting for an inline MRI fringe field. Phys Med Biol 2019; 64:215015. [PMID: 31509819 DOI: 10.1088/1361-6560/ab436a] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
MRI-guided proton therapy is being pursued for its promise to provide a more conformal, accurate proton therapy. However, the presence of the magnetic field imposes a challenge for the beam delivery as protons are deflected due to the Lorenz force. In this study, the impact of realistic inline MRI fringe field on IMPT plan delivery is investigated for a water phantom, liver tumor and prostate cancer differing in target volume, shape, and field configuration using Monte Carlo simulations. A method to correct for the shift of the beam spot positions in the presence of the inline magnetic field is presented. Results show that when not accounting for the effect of the magnetic field on the pencil beam delivery, the spot positions are substantially shifted and the quality of delivered plans is significantly deteriorated leading to dose inhomogeneities and creation of hot and cold spots. However, by correcting the pencil beam delivery, the dose quality of the IMPT plans is restored to a high degree. Nevertheless, adaptation of beam delivery alone is not robust regarding different treatment sites. By fully accounting during plan optimization for the dose distortions caused by the fringe and imaging fields, highly conformal IMPT plans are achieved. These results demonstrate proton pencil beam scanning and treatment planning can be adapted for precise delivery of state-of-the-art IMPT plans in MR-guided proton therapy in the presence of an inline MRI fringe field.
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Affiliation(s)
- Lucas N Burigo
- German Cancer Research Center (DKFZ), Heidelberg, Germany. National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO) Heidelberg, Germany
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27
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Liu Y, Lei Y, Wang Y, Shafai-Erfani G, Wang T, Tian S, Patel P, Jani AB, McDonald M, Curran WJ, Liu T, Zhou J, Yang X. Evaluation of a deep learning-based pelvic synthetic CT generation technique for MRI-based prostate proton treatment planning. Phys Med Biol 2019; 64:205022. [PMID: 31487698 DOI: 10.1088/1361-6560/ab41af] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The purpose of this work is to validate the application of a deep learning-based method for pelvic synthetic CT (sCT) generation that can be used for prostate proton beam therapy treatment planning. We propose to integrate dense block minimization into 3D cycle-consistent generative adversarial networks (cycleGAN) framework to effectively learn the nonlinear mapping between MRI and CT pairs. A cohort of 17 patients with co-registered CT and MR pairs were used to test the deep learning-based sCT generation method by leave-one-out cross-validation. Image quality between the sCT and CT images, gamma analysis passing rate, dose-volume metrics, distal range displacement, and the individual pencil beam Bragg peak shift between sCT- and CT-based proton plans were evaluated. The average mean absolute error (MAE) was 51.32 ± 16.91 HU. The relative differences of the statistics of the PTV dose-volume histogram (DVH) metrics in between sCT and CT were generally less than 1%. Mean values of dose difference, absolute dose difference (in percent of the prescribed dose) were -0.07% ± 0.07% and 0.23% ± 0.08%. Mean gamma analysis pass rate of 1 mm/1%, 2 mm/2%, 3 mm/3% criteria with 10% dose threshold were 92.39% ± 5.97%, 97.95% ± 2.95% and 98.97% ± 1.62% respectively. The median, mean and standard deviation of absolute maximum range differences were 0.09 cm and 0.23 ± 0.25 cm. The median and mean Bragg peak shifts among the 17 patients were 0.09 cm and 0.18 ± 0.07 cm. The image similarity, dosimetric and distal range agreement between sCT and original CT suggests the feasibility of further development of an MRI-only workflow for prostate proton radiotherapy.
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Affiliation(s)
- Yingzi Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
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28
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Neppl S, Landry G, Kurz C, Hansen DC, Hoyle B, Stöcklein S, Seidensticker M, Weller J, Belka C, Parodi K, Kamp F. Evaluation of proton and photon dose distributions recalculated on 2D and 3D Unet-generated pseudoCTs from T1-weighted MR head scans. Acta Oncol 2019; 58:1429-1434. [PMID: 31271093 DOI: 10.1080/0284186x.2019.1630754] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Introduction: The recent developments of magnetic resonance (MR) based adaptive strategies for photon and, potentially for proton therapy, require a fast and reliable conversion of MR images to X-ray computed tomography (CT) values. CT values are needed for photon and proton dose calculation. The improvement of conversion results employing a 3D deep learning approach is evaluated. Material and methods: A database of 89 T1-weighted MR head scans with about 100 slices each, including rigidly registered CTs, was created. Twenty-eight validation patients were randomly sampled, and four patients were selected for application. The remaining patients were used to train a 2D and a 3D U-shaped convolutional neural network (Unet). A stack size of 32 slices was used for 3D training. For all application cases, volumetric modulated arc therapy photon and single-field uniform dose pencil-beam scanning proton plans at four different gantry angles were optimized for a generic target on the CT and recalculated on 2D and 3D Unet-based pseudoCTs. Mean (absolute) error (MAE/ME) and a gradient sharpness estimate were used to quantify the image quality. Three-dimensional gamma and dose difference analyses were performed for photon (gamma criteria: 1%, 1 mm) and proton dose distributions (gamma criteria: 2%, 2 mm). Range (80% fall off) differences for beam's eye view profiles were evaluated for protons. Results: Training 36 h for 1000 epochs in 3D (6 h for 200 epochs in 2D) yielded a maximum MAE of 147 HU (135 HU) for the application patients. Except for one patient gamma pass rates for photon and proton dose distributions were above 96% for both Unets. Slice discontinuities were reduced for 3D training at the cost of sharpness. Conclusions: Image analysis revealed a slight advantage of 2D Unets compared to 3D Unets. Similar dose calculation performance was reached for the 2D and 3D network.
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Affiliation(s)
- Sebastian Neppl
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching bei München, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching bei München, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching bei München, Germany
| | - David C. Hansen
- Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | - Ben Hoyle
- University Observatory, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Munich, Germany
| | - Sophia Stöcklein
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Max Seidensticker
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Jochen Weller
- University Observatory, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Munich, Germany
- Optical and Interpretative Astronomy, Max Planck Institute for Extraterrestrial Physics, Garching bei München, Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner site Munich, Munich, Germany
| | - Katia Parodi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching bei München, Germany
| | - Florian Kamp
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
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29
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Olberg S, Zhang H, Kennedy WR, Chun J, Rodriguez V, Zoberi I, Thomas MA, Kim JS, Mutic S, Green OL, Park JC. Synthetic CT reconstruction using a deep spatial pyramid convolutional framework for MR‐only breast radiotherapy. Med Phys 2019; 46:4135-4147. [DOI: 10.1002/mp.13716] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 06/14/2019] [Accepted: 07/03/2019] [Indexed: 12/31/2022] Open
Affiliation(s)
- Sven Olberg
- Department of Radiation Oncology Washington University in St. Louis St. Louis MO 63110USA
- Department of Biomedical Engineering Washington University in St. Louis St. Louis MO 63110USA
| | - Hao Zhang
- Department of Radiation Oncology Washington University in St. Louis St. Louis MO 63110USA
| | - William R. Kennedy
- Department of Radiation Oncology Washington University in St. Louis St. Louis MO 63110USA
| | - Jaehee Chun
- Department of Radiation Oncology Washington University in St. Louis St. Louis MO 63110USA
- Department of Radiation Oncology, Yonsei Cancer Center Yonsei University College of Medicine Seoul South Korea
| | - Vivian Rodriguez
- Department of Radiation Oncology Washington University in St. Louis St. Louis MO 63110USA
| | - Imran Zoberi
- Department of Radiation Oncology Washington University in St. Louis St. Louis MO 63110USA
| | - Maria A. Thomas
- Department of Radiation Oncology Washington University in St. Louis St. Louis MO 63110USA
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei Cancer Center Yonsei University College of Medicine Seoul South Korea
| | - Sasa Mutic
- Department of Radiation Oncology Washington University in St. Louis St. Louis MO 63110USA
| | - Olga L. Green
- Department of Radiation Oncology Washington University in St. Louis St. Louis MO 63110USA
| | - Justin C. Park
- Department of Radiation Oncology Washington University in St. Louis St. Louis MO 63110USA
- Department of Biomedical Engineering Washington University in St. Louis St. Louis MO 63110USA
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30
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Liu Y, Lei Y, Wang Y, Wang T, Ren L, Lin L, McDonald M, Curran WJ, Liu T, Zhou J, Yang X. MRI-based treatment planning for proton radiotherapy: dosimetric validation of a deep learning-based liver synthetic CT generation method. Phys Med Biol 2019; 64:145015. [PMID: 31146267 PMCID: PMC6635951 DOI: 10.1088/1361-6560/ab25bc] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Magnetic resonance imaging (MRI) has been widely used in combination with computed tomography (CT) radiation therapy because MRI improves the accuracy and reliability of target delineation due to its superior soft tissue contrast over CT. The MRI-only treatment process is currently an active field of research since it could eliminate systematic MR-CT co-registration errors, reduce medical cost, avoid diagnostic radiation exposure, and simplify clinical workflow. The purpose of this work is to validate the application of a deep learning-based method for abdominal synthetic CT (sCT) generation by image evaluation and dosimetric assessment in a commercial proton pencil beam treatment planning system (TPS). This study proposes to integrate dense block into a 3D cycle-consistent generative adversarial networks (cycle GAN) framework in an effort to effectively learn the nonlinear mapping between MRI and CT pairs. A cohort of 21 patients with co-registered CT and MR pairs were used to test the deep learning-based sCT image quality by leave-one-out cross validation. The CT image quality, dosimetric accuracy and the distal range fidelity were rigorously checked, using side-by-side comparison against the corresponding original CT images. The average mean absolute error (MAE) was 72.87 ± 18.16 HU. The relative differences of the statistics of the PTV dose volume histogram (DVH) metrics between sCT and CT were generally less than 1%. Mean 3D gamma analysis passing rate of 1 mm/1%, 2 mm/2%, 3 mm/3% criteria with 10% dose threshold were 90.76% ± 5.94%, 96.98% ± 2.93% and 99.37% ± 0.99%, respectively. The median, mean and standard deviation of absolute maximum range differences were 0.170 cm, 0.186 cm and 0.155 cm. The image similarity, dosimetric and distal range agreement between sCT and original CT suggests the feasibility of further development of an MRI-only workflow for liver proton radiotherapy.
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Affiliation(s)
- Yingzi Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322
| | - Yinan Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322
| | - Tonghe Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322
| | - Lei Ren
- Department of Radiation Oncology, Duke University, Durham, NC 27708
| | - Liyong Lin
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322
| | - Mark McDonald
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322
| | - Walter J. Curran
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322
| | - Jun Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322
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31
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Liu Y, Lei Y, Wang T, Kayode O, Tian S, Liu T, Patel P, Curran WJ, Ren L, Yang X. MRI-based treatment planning for liver stereotactic body radiotherapy: validation of a deep learning-based synthetic CT generation method. Br J Radiol 2019; 92:20190067. [PMID: 31192695 DOI: 10.1259/bjr.20190067] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVE The purpose of this work is to develop and validate a learning-based method to derive electron density from routine anatomical MRI for potential MRI-based SBRT treatment planning. METHODS We proposed to integrate dense block into cycle generative adversarial network (GAN) to effectively capture the relationship between the CT and MRI for CT synthesis. A cohort of 21 patients with co-registered CT and MR pairs were used to evaluate our proposed method by the leave-one-out cross-validation. Mean absolute error, peak signal-to-noise ratio and normalized cross-correlation were used to quantify the imaging differences between the synthetic CT (sCT) and CT. The accuracy of Hounsfield unit (HU) values in sCT for dose calculation was evaluated by comparing the dose distribution in sCT-based and CT-based treatment planning. Clinically relevant dose-volume histogram metrics were then extracted from the sCT-based and CT-based plans for quantitative comparison. RESULTS The mean absolute error, peak signal-to-noise ratio and normalized cross-correlation of the sCT were 72.87 ± 18.16 HU, 22.65 ± 3.63 dB and 0.92 ± 0.04, respectively. No significant differences were observed in the majority of the planning target volume and organ at risk dose-volume histogram metrics ( p > 0.05). The average pass rate of γ analysis was over 99% with 1%/1 mm acceptance criteria on the coronal plane that intersects with isocenter. CONCLUSION The image similarity and dosimetric agreement between sCT and original CT warrant further development of an MRI-only workflow for liver stereotactic body radiation therapy. ADVANCES IN KNOWLEDGE This work is the first deep-learning-based approach to generating abdominal sCT through dense-cycle-GAN. This method can successfully generate the small bony structures such as the rib bones and is able to predict the HU values for dose calculation with comparable accuracy to reference CT images.
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Affiliation(s)
- Yingzi Liu
- 1 Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
| | - Yang Lei
- 1 Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
| | - Tonghe Wang
- 1 Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
| | - Oluwatosin Kayode
- 1 Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
| | - Sibo Tian
- 1 Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
| | - Tian Liu
- 1 Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
| | - Pretesh Patel
- 1 Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
| | - Walter J Curran
- 1 Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
| | - Lei Ren
- 2 Department of Radiation Oncology, Duke University, Durham, North Carolina
| | - Xiaofeng Yang
- 1 Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
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Ranta I, Kemppainen R, Keyriläinen J, Suilamo S, Heikkinen S, Kapanen M, Saunavaara J. Quality assurance measurements of geometric accuracy for magnetic resonance imaging-based radiotherapy treatment planning. Phys Med 2019; 62:47-52. [PMID: 31153398 DOI: 10.1016/j.ejmp.2019.04.022] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 03/29/2019] [Accepted: 04/24/2019] [Indexed: 10/26/2022] Open
Abstract
BACKGROUND Using magnetic resonance imaging (MRI) as the only imaging method for radiotherapy treatment planning (RTP) is becoming more common as MRI-only RTP solutions have evolved. The geometric accuracy of MR images is an essential factor of image quality when determining the suitability of MRI for RTP. The need is therefore clear for clinically feasible quality assurance (QA) methods for the geometric accuracy measurement. MATERIALS AND METHODS This work evaluates long-term stability of geometric accuracy and the validity of a 2D geometric accuracy QA method compared to a prototype 3D method and analysis software in routine QA. The long-term follow-up measurements were conducted on one of the 1.5 T scanners over a period of 19 months using both methods. Inter-scanner variability of geometric distortions was also evaluated in three 1.5 T and three 3 T MRI scanners from a single vendor by using the prototype 3D QA method. RESULTS The geometric accuracy of the magnetic resonance for radiotherapy (MR-RT) platform remained stable within 2 mm at distances of <250 mm from isocenter. All scanners achieved good geometric accuracy with mean geometric distortions of <1 mm at <150 mm and <2 mm at <250 mm from the isocenter. Both measurement methods provided relevant information about geometric distortions. CONCLUSIONS Geometric distortions are often considered a limitation of MRI-only RTP. Results indicate that geometric accuracy of modern scanners remain within acceptable limits by default even after many years of clinical use based on the 3D QA evaluation.
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Affiliation(s)
- Iiro Ranta
- Department of Physics and Astronomy, University of Turku, Vesilinnantie 5, FI-20014 Turku, Finland; Department of Medical Physics, Turku University Hospital, Hämeentie 11, P.O. Box 52, FI-20521 Turku, Finland; Department of Oncology and Radiotherapy, Turku University Hospital, Hämeentie 11, P.O. Box 52, FI-20521 Turku, Finland.
| | | | - Jani Keyriläinen
- Department of Physics and Astronomy, University of Turku, Vesilinnantie 5, FI-20014 Turku, Finland; Department of Medical Physics, Turku University Hospital, Hämeentie 11, P.O. Box 52, FI-20521 Turku, Finland; Department of Oncology and Radiotherapy, Turku University Hospital, Hämeentie 11, P.O. Box 52, FI-20521 Turku, Finland
| | - Sami Suilamo
- Department of Medical Physics, Turku University Hospital, Hämeentie 11, P.O. Box 52, FI-20521 Turku, Finland; Department of Oncology and Radiotherapy, Turku University Hospital, Hämeentie 11, P.O. Box 52, FI-20521 Turku, Finland
| | - Samuli Heikkinen
- Department of Medical Physics, Turku University Hospital, Hämeentie 11, P.O. Box 52, FI-20521 Turku, Finland
| | - Mika Kapanen
- Department of Medical Physics, Medical Imaging Center, Tampere University Hospital, P.O. Box 2000, FI-33521 Tampere, Finland; Department of Oncology, Unit of Radiotherapy, Tampere University Hospital, P.O. Box 2000, FI-33521 Tampere, Finland
| | - Jani Saunavaara
- Department of Medical Physics, Turku University Hospital, Hämeentie 11, P.O. Box 52, FI-20521 Turku, Finland
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Kan H, Eguchi Y, Tsuchiya T, Kondo T, Kitagawa Y, Mekata Y, Fukuma H, Yoshida R, Kasai H, Kunitomo H, Hirose Y, Shibamoto Y. Geometric discrepancy of image-guided radiation therapy in patients with prostate cancer without implanted fiducial markers using a commercial pseudo-CT generation method. Phys Med Biol 2019; 64:06NT01. [DOI: 10.1088/1361-6560/ab02cc] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Guerreiro F, Koivula L, Seravalli E, Janssens GO, Maduro JH, Brouwer CL, Korevaar EW, Knopf AC, Korhonen J, Raaymakers BW. Feasibility of MRI-only photon and proton dose calculations for pediatric patients with abdominal tumors. Phys Med Biol 2019; 64:055010. [PMID: 30669135 DOI: 10.1088/1361-6560/ab0095] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The purpose of this study was to develop a method enabling synthetic computed tomography (sCT) generation of the whole abdomen using magnetic resonance imaging (MRI) scans of pediatric patients with abdominal tumors. The proposed method relies on an automatic atlas-based segmentation of bone and lungs followed by an MRI intensity to synthetic Hounsfield unit conversion. Separate conversion algorithms were used for bone, lungs and soft-tissue. Rigidly registered CT and T2-weighted MR images of 30 patients in treatment position and with the same field of view were used for the evaluation of the atlas and the conversion algorithms. The dose calculation accuracy of the generated sCTs was verified for volumetric modulated arc therapy (VMAT) and pencil beam scanning (PBS). VMAT and PBS plans were robust optimized on an internal target volume (ITV) against a patient set-up uncertainty of 5 mm. Average differences between CT and sCT dose calculations for the ITV V 95% were 0.5% (min 0.0%; max 5.0%) and 0.0% (min -0.1%; max 0.1%) for VMAT and PBS dose distributions, respectively. Average differences for the mean dose to the organs at risk were <0.2% (min -0.6%; max 1.2%) and <0.2% (min -2.0%; max 2.6%) for VMAT and PBS dose distributions, respectively. Results show that MRI-only photon and proton dose calculations are feasible for children with abdominal tumors.
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Affiliation(s)
- Filipa Guerreiro
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
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Santos DM, Wachowicz K, Burke B, Fallone BG. Proton beam behavior in a parallel configured
MRI
‐proton therapy hybrid: Effects of time‐varying gradient magnetic fields. Med Phys 2018; 46:822-838. [DOI: 10.1002/mp.13309] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 11/18/2018] [Accepted: 11/19/2018] [Indexed: 01/01/2023] Open
Affiliation(s)
- D. M. Santos
- Department of Medical Physics Cross Cancer Institute 11560 University Avenue AB T6G 1Z2 Canada
| | - K. Wachowicz
- Department of Medical Physics Cross Cancer Institute 11560 University Avenue AB T6G 1Z2 Canada
- Department of Oncology Medical Physics Division University of Alberta 11560 University Avenue Edmonton AB T6G 1Z2 Canada
| | - B. Burke
- Department of Oncology Medical Physics Division University of Alberta 11560 University Avenue Edmonton AB T6G 1Z2 Canada
| | - B. G. Fallone
- Department of Medical Physics Cross Cancer Institute 11560 University Avenue AB T6G 1Z2 Canada
- Department of Oncology Medical Physics Division University of Alberta 11560 University Avenue Edmonton AB T6G 1Z2 Canada
- Department of Physics University of Alberta 11322 – 89 Avenue Edmonton AB T6G 2G7 Canada
- MagnetTx Oncology Solutions, Ltd. PO Box 52112 Edmonton AB Canada
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Paganelli C, Whelan B, Peroni M, Summers P, Fast M, van de Lindt T, McClelland J, Eiben B, Keall P, Lomax T, Riboldi M, Baroni G. MRI-guidance for motion management in external beam radiotherapy: current status and future challenges. Phys Med Biol 2018; 63:22TR03. [PMID: 30457121 DOI: 10.1088/1361-6560/aaebcf] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
High precision conformal radiotherapy requires sophisticated imaging techniques to aid in target localisation for planning and treatment, particularly when organ motion due to respiration is involved. X-ray based imaging is a well-established standard for radiotherapy treatments. Over the last few years, the ability of magnetic resonance imaging (MRI) to provide radiation-free images with high-resolution and superb soft tissue contrast has highlighted the potential of this imaging modality for radiotherapy treatment planning and motion management. In addition, these advantageous properties motivated several recent developments towards combined MRI radiation therapy treatment units, enabling in-room MRI-guidance and treatment adaptation. The aim of this review is to provide an overview of the state-of-the-art in MRI-based image guidance for organ motion management in external beam radiotherapy. Methodological aspects of MRI for organ motion management are reviewed and their application in treatment planning, in-room guidance and adaptive radiotherapy described. Finally, a roadmap for an optimal use of MRI-guidance is highlighted and future challenges are discussed.
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Affiliation(s)
- C Paganelli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy. Author to whom any correspondence should be addressed. www.cartcas.polimi.it
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Rädler M, Landry G, Rit S, Schulte RW, Parodi K, Dedes G. Two-dimensional noise reconstruction in proton computed tomography using distance-driven filtered back-projection of simulated projections. Phys Med Biol 2018; 63:215009. [PMID: 30277469 DOI: 10.1088/1361-6560/aae5c9] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
We present a formalism for two-dimensional (2D) noise reconstruction in proton computed tomography (pCT). This is necessary for the application of fluence modulated pCT (FMpCT) since it permits image noise prescription and the corresponding proton fuence optimization. We aimed at extending previously published formalisms to account for the impact of multiple Coulomb scattering (MCS) on projection noise, and the use of filtered back projection (FBP) reconstruction along curved paths with distance driven binning (DDB). 2D noise reconstruction for a beam of protons with parallel initial momentum vectors, and for projections binned both at the rear tracker and with DDB, was established. Monte Carlo (MC) simulations of pCT scans of a water cylinder were employed to generate pCT projections and to calculate their noise for use in 2D noise reconstruction. These were compared to results from an analytical model accounting for MCS for rear tracker binning as well as against the previously published central pixel model which ignores MCS. Image noise reconstructed with the formalism for rear tracker binning and DDB were compared to MC results using annular regions of interest (ROIs). Agreement better than 8% was obtained between the noise of projections calculated with MC simulation and our model. Noise from annular ROIs agreed with our noise reconstructions for rear tracker binning and DDB. The central pixel model ignoring MCS underestimated projection and thus image noise by up to 40% towards the object's edge. The use of DDB decreased the image noise towards the object's edge when compared to rear tracker binning and yielded more uniform noise throughout the image. MCS should not be neglected when predicting image noise for pixels away from the center of an object in a pCT scan due to the increasing influence of the gradient of the object's hull closer to the edges.
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Affiliation(s)
- Martin Rädler
- Department of Medical Physics, Ludwig-Maximilians-Universität München, 85748 Garching b. München, Germany. Authors contributed equally
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Kerkmeijer LGW, Maspero M, Meijer GJ, van der Voort van Zyp JRN, de Boer HCJ, van den Berg CAT. Magnetic Resonance Imaging only Workflow for Radiotherapy Simulation and Planning in Prostate Cancer. Clin Oncol (R Coll Radiol) 2018; 30:692-701. [PMID: 30244830 DOI: 10.1016/j.clon.2018.08.009] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 06/29/2018] [Accepted: 08/21/2018] [Indexed: 01/06/2023]
Abstract
Magnetic resonance imaging (MRI) is often combined with computed tomography (CT) in prostate radiotherapy to optimise delineation of the target and organs-at-risk (OAR) while maintaining accurate dose calculation. Such a dual-modality workflow requires two separate imaging sessions, and it has some fundamental and logistical drawbacks. Due to the availability of new MRI hardware and software solutions, CT examinations can be omitted for prostate radiotherapy simulations. All information for treatment planning, including electron density maps and bony anatomy, can nowadays be obtained with MRI. Such an MRI-only simulation workflow reduces delineation ambiguities, eases planning logistics, and improves patient comfort; however, careful validation of the complete MRI-only workflow is warranted. The first institutes are now adopting this MRI-only workflow for prostate radiotherapy. In this article, we will review technology and workflow requirements for an MRI-only prostate simulation workflow.
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Affiliation(s)
- L G W Kerkmeijer
- Department of Radiotherapy, University Medical Center Utrecht, The Netherlands.
| | - M Maspero
- Department of Radiotherapy, University Medical Center Utrecht, The Netherlands
| | - G J Meijer
- Department of Radiotherapy, University Medical Center Utrecht, The Netherlands
| | | | - H C J de Boer
- Department of Radiotherapy, University Medical Center Utrecht, The Netherlands
| | - C A T van den Berg
- Department of Radiotherapy, University Medical Center Utrecht, The Netherlands
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Maspero M, Savenije MHF, Dinkla AM, Seevinck PR, Intven MPW, Jurgenliemk-Schulz IM, Kerkmeijer LGW, van den Berg CAT. Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy. Phys Med Biol 2018; 63:185001. [PMID: 30109989 DOI: 10.1088/1361-6560/aada6d] [Citation(s) in RCA: 159] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
To enable magnetic resonance (MR)-only radiotherapy and facilitate modelling of radiation attenuation in humans, synthetic CT (sCT) images need to be generated. Considering the application of MR-guided radiotherapy and online adaptive replanning, sCT generation should occur within minutes. This work aims at assessing whether an existing deep learning network can rapidly generate sCT images for accurate MR-based dose calculations in the entire pelvis. A study was conducted on data of 91 patients with prostate (59), rectal (18) and cervical (14) cancer who underwent external beam radiotherapy acquiring both CT and MRI for patients' simulation. Dixon reconstructed water, fat and in-phase images obtained from a conventional dual gradient-recalled echo sequence were used to generate sCT images. A conditional generative adversarial network (cGAN) was trained in a paired fashion on 2D transverse slices of 32 prostate cancer patients. The trained network was tested on the remaining patients to generate sCT images. For 30 patients in the test set, dose recalculations of the clinical plan were performed on sCT images. Dose distributions were evaluated comparing voxel-based dose differences, gamma and dose-volume histogram (DVH) analysis. The sCT generation required 5.6 s and 21 s for a single patient volume on a GPU and CPU, respectively. On average, sCT images resulted in a higher dose to the target of maximum 0.3%. The average gamma pass rates using the 3%, 3 mm and 2%, 2 mm criteria were above 97 and 91%, respectively, for all volumes of interests considered. All DVH points calculated on sCT differed less than ±2.5% from the corresponding points on CT. Results suggest that accurate MR-based dose calculation using sCT images generated with a cGAN trained on prostate cancer patients is feasible for the entire pelvis. The sCT generation was sufficiently fast for integration in an MR-guided radiotherapy workflow.
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Affiliation(s)
- Matteo Maspero
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands. Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands. Image Science Institute, University Medical Center Utrecht, Utrecht, Netherlands. The authors equally contributed
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40
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Dinkla AM, Wolterink JM, Maspero M, Savenije MHF, Verhoeff JJC, Seravalli E, Išgum I, Seevinck PR, van den Berg CAT. MR-Only Brain Radiation Therapy: Dosimetric Evaluation of Synthetic CTs Generated by a Dilated Convolutional Neural Network. Int J Radiat Oncol Biol Phys 2018; 102:801-812. [PMID: 30108005 DOI: 10.1016/j.ijrobp.2018.05.058] [Citation(s) in RCA: 88] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Revised: 05/15/2018] [Accepted: 05/22/2018] [Indexed: 01/01/2023]
Abstract
PURPOSE This work aims to facilitate a fast magnetic resonance (MR)-only workflow for radiation therapy of intracranial tumors. Here, we evaluate whether synthetic computed tomography (sCT) images generated with a dilated convolutional neural network (CNN) enable accurate MR-based dose calculations in the brain. METHODS AND MATERIALS We conducted a retrospective study of 52 patients with brain tumors who underwent both computed tomography (CT) and MR imaging for radiation therapy treatment planning. To generate the sCTs, a T1-weighted gradient echo MR sequence was selected from the clinical protocol for multiple types of brain tumors. sCTs were created for all 52 patients with a dilated CNN using 2-fold cross validation; in each fold, 26 patients were used for training and the remaining 26 patients were used for evaluation. For each patient, the clinical CT-based treatment plan was recalculated on sCT. We calculated dose differences and gamma pass rates between CT- and sCT-based plans inside body and planning target volume. Geometric fidelity of the sCT and differences in beam depth and equivalent path length were assessed between both treatment plans. RESULTS sCT generation took 1 minute per patient. Over the patient population, the mean absolute error of the sCT within the intersection of body contours was 67 ± 11 HU (±1 standard deviation [SD], range: 51-117 HU), and the mean error was 13 ± 9 HU (±1 SD, range: -2 to 38 HU). Dosimetric analysis showed mean deviations of 0.00% ± 0.02% (±1 SD, range: -0.05 to 0.03) for dose within the body contours and -0.13% ± 0.39% (±1 SD, range: -1.43 to 0.80) inside the planning target volume. Mean γ1mm/1% was 98.8% ± 2.2% for doses >50% of the prescribed dose. CONCLUSIONS The presented dilated CNN generated sCTs from conventional MR images without adding scan time to the acquisition. Dosimetric evaluation suggests that dose calculations performed on the sCTs are accurate and can therefore be used for MR-only intracranial radiation therapy treatment planning.
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Affiliation(s)
- Anna M Dinkla
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Jelmer M Wolterink
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Matteo Maspero
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands; Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Mark H F Savenije
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Joost J C Verhoeff
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Enrica Seravalli
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Ivana Išgum
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Peter R Seevinck
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
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Abstract
Proton therapy is a promising but challenging treatment modality for the management of lung cancer. The technical challenges are due to respiratory motion, low dose tolerance of adjacent normal tissue and tissue density heterogeneity. Different imaging modalities are applied at various steps of lung proton therapy to provide information on target definition, target motion, proton range, patient setup and treatment outcome assessment. Imaging data is used to guide treatment design, treatment delivery, and treatment adaptation to ensure the treatment goal is achieved. This review article will summarize and compare various imaging techniques that can be used in every step of lung proton therapy to address these challenges.
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Affiliation(s)
- Miao Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Wei Zou
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - Boon-Keng Kevin Teo
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
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