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Nenoff L, Amstutz F, Murr M, Archibald-Heeren B, Fusella M, Hussein M, Lechner W, Zhang Y, Sharp G, Vasquez Osorio E. Review and recommendations on deformable image registration uncertainties for radiotherapy applications. Phys Med Biol 2023; 68:24TR01. [PMID: 37972540 PMCID: PMC10725576 DOI: 10.1088/1361-6560/ad0d8a] [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: 04/11/2023] [Revised: 10/30/2023] [Accepted: 11/15/2023] [Indexed: 11/19/2023]
Abstract
Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.
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Affiliation(s)
- Lena Nenoff
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, United States of America
- Harvard Medical School, Boston, MA, United States of America
- 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
| | - Florian Amstutz
- Department of Physics, ETH Zurich, Switzerland
- Center for Proton Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Martina Murr
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany
| | | | - Marco Fusella
- Department of Radiation Oncology, Abano Terme Hospital, Italy
| | - Mohammad Hussein
- Metrology for Medical Physics, National Physical Laboratory, Teddington, United Kingdom
| | - Wolfgang Lechner
- Department of Radiation Oncology, Medical University of Vienna, Austria
| | - Ye Zhang
- Center for Proton Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland
| | - Greg Sharp
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, United States of America
- Harvard Medical School, Boston, MA, United States of America
| | - Eliana Vasquez Osorio
- Division of Cancer Sciences, The University of Manchester, Manchester, United Kingdom
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Delaby N, Barateau A, Chiavassa S, Biston MC, Chartier P, Graulières E, Guinement L, Huger S, Lacornerie T, Millardet-Martin C, Sottiaux A, Caron J, Gensanne D, Pointreau Y, Coutte A, Biau J, Serre AA, Castelli J, Tomsej M, Garcia R, Khamphan C, Badey A. Practical and technical key challenges in head and neck adaptive radiotherapy: The GORTEC point of view. Phys Med 2023; 109:102568. [PMID: 37015168 DOI: 10.1016/j.ejmp.2023.102568] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 02/15/2023] [Accepted: 03/18/2023] [Indexed: 04/05/2023] Open
Abstract
Anatomical variations occur during head and neck (H&N) radiotherapy (RT) treatment. These variations may result in underdosage to the target volume or overdosage to the organ at risk. Replanning during the treatment course can be triggered to overcome this issue. Due to technological, methodological and clinical evolutions, tools for adaptive RT (ART) are becoming increasingly sophisticated. The aim of this paper is to give an overview of the key steps of an H&N ART workflow and tools from the point of view of a group of French-speaking medical physicists and physicians (from GORTEC). Focuses are made on image registration, segmentation, estimation of the delivered dose of the day, workflow and quality assurance for an implementation of H&N offline and online ART. Practical recommendations are given to assist physicians and medical physicists in a clinical workflow.
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Maraghechi B, Mazur T, Lam D, Price A, Henke L, Kim H, Hugo GD, Cai B. Phantom-based Quality Assurance of a Clinical Dose Accumulation Technique Used in an Online Adaptive Radiation Therapy Platform. Adv Radiat Oncol 2022; 8:101138. [PMID: 36691450 PMCID: PMC9860416 DOI: 10.1016/j.adro.2022.101138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 10/01/2022] [Indexed: 12/12/2022] Open
Abstract
Purpose This study aimed to develop a routine quality assurance method for a dose accumulation technique provided by a radiation therapy platform for online treatment adaptation. Methods and Materials Two commonly used phantoms were selected for the dose accumulation QA: Electron density and anthropomorphic pelvis. On a computed tomography (CT) scan of the electron density phantom, 1 target (gross tumor volume [GTV]; insert at 6 o'clock), a subvolume within this target, and 7 organs at risk (OARs; other inserts) were contoured in the treatment planning system (TPS). Two adaptation sessions were performed in which the GTV was recontoured, first at 7 o'clock and then at 5 o'clock. The accumulated dose was exported from the TPS after delivery. Deformable vector fields were also exported to manually accumulate doses for comparison. For the pelvis phantom, synthetic Gaussian deformations were applied to the planning CT image to simulate organ changes. Two single-fraction adaptive plans were created based on the deformed planning CT and cone beam CT images acquired onboard the radiation therapy platform. A manual dose accumulation was performed after delivery using the exported deformable vector fields for comparison with the system-generated result. Results All plans were successfully delivered, and the accumulated dose was both manually calculated and derived from the TPS. For the electron density phantom, the average mean dose differences in the GTV, boost volume, and OARs 1 to 7 were 0.0%, -0.2%, 92.0%, 78.4%, 1.8%, 1.9%, 0.0%, 0.0%, and 2.3%, respectively, between the manually summed and platform-accumulated doses. The gamma passing rates for the 3-dimensional dose comparison between the manually generated and TPS-provided dose accumulations were >99% for both phantoms. Conclusions This study demonstrated agreement between manually obtained and TPS-generated accumulated doses in terms of both mean structure doses and local 3-dimensional dose distributions. Large disagreements were observed for OAR1 and OAR2 defined on the electron density phantom due to OARs having lower deformation priority over the target in addition to artificially large changes in position induced for these structures fraction-by-fraction. The tests applied in this study to a commercial platform provide a straightforward approach toward the development of routine quality assurance of dose accumulation in online adaptation.
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Affiliation(s)
- Borna Maraghechi
- Department of Radiation Oncology, Washington University, St Louis, Missouri
| | - Thomas Mazur
- Department of Radiation Oncology, Washington University, St Louis, Missouri
| | - Dao Lam
- Department of Radiation Oncology, Washington University, St Louis, Missouri
| | - Alex Price
- Department of Radiation Oncology, Washington University, St Louis, Missouri
| | - Lauren Henke
- Department of Radiation Oncology, Washington University, St Louis, Missouri
| | - Hyun Kim
- Department of Radiation Oncology, Washington University, St Louis, Missouri
| | - Geoffrey D. Hugo
- Department of Radiation Oncology, Washington University, St Louis, Missouri,Corresponding author: Geoffrey Hugo, PhD
| | - Bin Cai
- Department of Radiation Oncology, Washington University, St Louis, Missouri,Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas
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Lowther N, Louwe R, Yuen J, Hardcastle N, Yeo A, Jameson M. MIRSIG position paper: the use of image registration and fusion algorithms in radiotherapy. Phys Eng Sci Med 2022; 45:421-428. [PMID: 35522369 PMCID: PMC9239966 DOI: 10.1007/s13246-022-01125-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/28/2022] [Indexed: 12/12/2022]
Abstract
The report of the American Association of Physicists in Medicine (AAPM) Task Group No. 132 published in 2017 reviewed rigid image registration and deformable image registration (DIR) approaches and solutions to provide recommendations for quality assurance and quality control of clinical image registration and fusion techniques in radiotherapy. However, that report did not include the use of DIR for advanced applications such as dose warping or warping of other matrices of interest. Considering that DIR warping tools are now readily available, discussions were hosted by the Medical Image Registration Special Interest Group (MIRSIG) of the Australasian College of Physical Scientists & Engineers in Medicine in 2018 to form a consensus on best practice guidelines. This position statement authored by MIRSIG endorses the recommendations of the report of AAPM task group 132 and expands on the best practice advice from the ‘Deforming to Best Practice’ MIRSIG publication to provide guidelines on the use of DIR for advanced applications.
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Affiliation(s)
- Nicholas Lowther
- Department of Radiation Oncology, Wellington Blood and Cancer Centre, Wellington, New Zealand
| | - Rob Louwe
- Holland Proton Therapy Centre, Delft, Netherlands
| | - Johnson Yuen
- St George Hospital Cancer Care Centre, Kogarah, New South Wales, 2217, Australia.,South Western Clinical School, University of New South Wales, Sydney, Australia.,Ingham Institute for Applied Medical Research, Sydney, NSW, Australia
| | - Nicholas Hardcastle
- Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia.,The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Adam Yeo
- Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,School of Applied Sciences, RMIT University, Melbourne, VIC, Australia
| | - Michael Jameson
- GenesisCare, Sydney, NSW, 2015, Australia. .,St Vincent's Clinical School, University of New South Wales, Sydney, Australia.
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Chen L, Liang X, Shen C, Nguyen D, Jiang S, Wang J. Synthetic CT generation from CBCT images via unsupervised deep learning. Phys Med Biol 2021; 66. [PMID: 34061043 DOI: 10.1088/1361-6560/ac01b6] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 05/14/2021] [Indexed: 11/12/2022]
Abstract
Adaptive-radiation-therapy (ART) is applied to account for anatomical variations observed over the treatment course. Daily or weekly cone-beam computed tomography (CBCT) is commonly used in clinic for patient positioning, but CBCT's inaccuracy in Hounsfield units (HU) prevents its application to dose calculation and treatment planning. Adaptive re-planning can be performed by deformably registering planning CT (pCT) to CBCT. However, scattering artifacts and noise in CBCT decrease the accuracy of deformable registration and induce uncertainty in treatment plan. Hence, generating from CBCT a synthetic CT (sCT) that has the same anatomical structure as CBCT but accurate HU values is desirable for ART. We proposed an unsupervised style-transfer-based approach to generate sCT based on CBCT and pCT. Unsupervised learning was desired because exactly matched CBCT and CT are rarely available, even when they are taken a few minutes apart. In the proposed model, CBCT and pCT are two inputs that provide anatomical structure and accurate HU information, respectively. The training objective function is designed to simultaneously minimize (1) contextual loss between sCT and CBCT to maintain the content and structure of CBCT in sCT and (2) style loss between sCT and pCT to achieve pCT-like image quality in sCT. We used CBCT and pCT images of 114 patients to train and validate the designed model, and another 29 independent patient cases to test the model's effectiveness. We quantitatively compared the resulting sCT with the original CBCT using the deformed same-day pCT as reference. Structure-similarity-index, peak-signal-to-noise-ratio, and mean-absolute-error in HU of sCT were 0.9723, 33.68, and 28.52, respectively, while those of CBCT were 0.9182, 29.67, and 49.90, respectively. We have demonstrated the effectiveness of the proposed model in using CBCT and pCT to synthesize CT-quality images. This model may permit using CBCT for advanced applications such as adaptive treatment planning.
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Affiliation(s)
- Liyuan Chen
- Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX 75390 United States of America
| | - Xiao Liang
- Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX 75390 United States of America
| | - Chenyang Shen
- Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX 75390 United States of America
| | - Dan Nguyen
- Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX 75390 United States of America
| | - Steve Jiang
- Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX 75390 United States of America
| | - Jing Wang
- Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX 75390 United States of America
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Training a deep neural network coping with diversities in abdominal and pelvic images of children and young adults for CBCT-based adaptive proton therapy. Radiother Oncol 2021; 160:250-258. [PMID: 33992626 DOI: 10.1016/j.radonc.2021.05.006] [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: 12/08/2020] [Revised: 05/04/2021] [Accepted: 05/05/2021] [Indexed: 11/24/2022]
Abstract
PURPOSE To train a deep neural network for correcting abdominal and pelvic cone-beam computed tomography (CBCT) of children and young adults in the presence of diverse patient size, anatomic extent, and scan parameters. MATERIALS AND METHODS Pretreatment CBCT and planning/repeat CT image pairs from 64 children and young adults treated with proton therapy (aged 1-23 years) were analyzed. To evaluate the impact of anatomic extent in CBCT and data size in the training data, we compared the performance of three cycle-consistent generative adversarial network models that were separately trained by three datasets comprising abdominal (n = 21), pelvic (n = 29), and combined abdominal-pelvic image pairs (n = 50), respectively. The maximum body width of each patient was normalized to a fixed width before training and model application to reduce the impact of variations in body size. The corrected CBCT images by the three models were comparatively evaluated against the repeat CT closest in time to the CBCT (median gap, 0 days; range, 0-6 days) in HU accuracy, estimated dose distribution, and proton range. RESULTS The network model trained by the combined dataset significantly outperformed the abdomen and pelvis models in mean absolute HU error of the corrected CBCT from 14 testing patients (47 ± 7 HU versus 51 ± 8 HU; paired Wilcoxon signed-rank test, P < 0.01). The larger error (60 ± 7 HU) without the body-size normalization confirmed the efficacy of the preprocessing. The model trained with the combined dataset resulted in gamma passing rates of 98.5 ± 1.9% (2%/2 mm criterion) and the range (80% distal fall-off) differences from the reference within ±3 mm for 91.2 ± 11.5% beamlets. CONCLUSION Combining data from adjacent anatomic sites and normalizing age-dependent body sizes in children and young adults were beneficial in training a neural network to accurately estimate proton dose from CBCT despite limited training data size and anatomic diversities.
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Shi L, Chen Q, Barley S, Cui Y, Shang L, Qiu J, Rong Y. Benchmarking of Deformable Image Registration for Multiple Anatomic Sites Using Digital Data Sets With Ground-Truth Deformation Vector Fields. Pract Radiat Oncol 2021; 11:404-414. [PMID: 33722783 DOI: 10.1016/j.prro.2021.02.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 01/27/2021] [Accepted: 02/15/2021] [Indexed: 11/26/2022]
Abstract
PURPOSE This study aimed to evaluate the accuracy of deformable image registration (DIR) algorithms using data sets with different levels of ground-truth deformation vector fields (DVFs) and to investigate the correlation between DVF errors and contour-based metrics. METHODS AND MATERIALS Nine pairs of digital data sets were generated through contour-controlled deformations based on 3 anonymized patients' CTs (head and neck, thorax/abdomen, and pelvis) with low, medium, and high deformation intensity for each site using the ImSimQA software. Image pairs and their associated contours were imported to MIM-Maestro, Raystation, and Velocity systems, followed by DIR and contour propagation. The system-generated DVF and propagated contours were compared with the ground-truth data. The correlation between DVF errors and contour-based metrics was evaluated using the Pearson correlation coefficient (r), while their correlation with volumes were calculated using Spearman correlation coefficient (rho). RESULTS The DVF errors increased with increasing deformation intensity. All DIR algorithms performed well for esophagus, trachea, left femoral, right femoral, and urethral (mean and maximum DVF errors <2.50 mm and <4.27 mm, respectively; Dice similarity coefficient: 0.93-0.99). Brain, liver, left lung, and bladder showed large DVF errors for all 3 systems (dmax: 2.8-91.90 mm). The minimum and maximum DVF errors, conformity index, and Dice similarity coefficient were correlated with volumes (|rho|: 0.41-0.64), especially for very large or small structures (|rho|: 0.64-0.80). Only mean distance to agreement of Raystation and Velocity correlated with some indices of DVF errors (r: 0.70-0.78). CONCLUSIONS Most contour-based metrics had no correlation with DVF errors. For adaptive radiation therapy, well-performed contour propagation does not directly indicate accurate dose deformation and summation/accumulation within each contour (determined by DVF accuracy). Tolerance values for DVF errors should vary as the acceptable accuracy for overall adaptive radiation therapy depends on anatomic site, deformation intensity, organ size, and so forth. This study provides benchmark tables for evaluating DIR accuracy in various clinical scenarios.
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Affiliation(s)
- Liting Shi
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, California; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China; Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, Jiangsu, China
| | - Quan Chen
- Department of Radiation Oncology, University of Kentucky, Lexington, Kentucky
| | - Susan Barley
- Oncology Systems Limited (OSL), Shrewsbury, Shropshire, United Kingdom
| | - Yunfeng Cui
- Department of Radiation Oncology, Duke University, Durham, North Carolina
| | - Lu Shang
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, California
| | - Jianfeng Qiu
- Medical Engineering and Technology Research Center; Imaging-X Joint Laboratory; Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, Shandong, China
| | - Yi Rong
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, California; Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona.
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Glide-Hurst CK, Lee P, Yock AD, Olsen JR, Cao M, Siddiqui F, Parker W, Doemer A, Rong Y, Kishan AU, Benedict SH, Li XA, Erickson BA, Sohn JW, Xiao Y, Wuthrick E. Adaptive Radiation Therapy (ART) Strategies and Technical Considerations: A State of the ART Review From NRG Oncology. Int J Radiat Oncol Biol Phys 2020; 109:1054-1075. [PMID: 33470210 DOI: 10.1016/j.ijrobp.2020.10.021] [Citation(s) in RCA: 93] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 10/08/2020] [Accepted: 10/19/2020] [Indexed: 12/21/2022]
Abstract
The integration of adaptive radiation therapy (ART), or modifying the treatment plan during the treatment course, is becoming more widely available in clinical practice. ART offers strong potential for minimizing treatment-related toxicity while escalating or de-escalating target doses based on the dose to organs at risk. Yet, ART workflows add complexity into the radiation therapy planning and delivery process that may introduce additional uncertainties. This work sought to review presently available ART workflows and technological considerations such as image quality, deformable image registration, and dose accumulation. Quality assurance considerations for ART components and minimum recommendations are described. Personnel and workflow efficiency recommendations are provided, as is a summary of currently available clinical evidence supporting the implementation of ART. Finally, to guide future clinical trial protocols, an example ART physician directive and a physics template following standard NRG Oncology protocol is provided.
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Affiliation(s)
- Carri K Glide-Hurst
- Department of Human Oncology, University of Wisconsin-Madison, Madison, Wisconsin.
| | - Percy Lee
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Adam D Yock
- Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jeffrey R Olsen
- Department of Radiation Oncology, University of Colorado- Denver, Denver, Colorado
| | - Minsong Cao
- Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, California
| | - Farzan Siddiqui
- Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, Michigan
| | - William Parker
- Department of Radiation Oncology, McGill University, Montreal, Quebec, Canada
| | - Anthony Doemer
- Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, Michigan
| | - Yi Rong
- Department of Radiation Oncology, University of California-Davis, Sacramento, California
| | - Amar U Kishan
- Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, California
| | - Stanley H Benedict
- Department of Radiation Oncology, University of California-Davis, Sacramento, California
| | - X Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Beth A Erickson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Jason W Sohn
- Department of Radiation Oncology, Allegheny Health Network, Pittsburgh, Pennsylvania
| | - Ying Xiao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Evan Wuthrick
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida
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Bjarnason TA, Rees R, Kainz J, Le LH, Stewart EE, Preston B, Elbakri I, Fife IAJ, Lee T, Gagnon IMB, Arsenault C, Therrien P, Kendall E, Tonkopi E, Cottreau M, Aldrich JE. An international survey on the clinical use of rigid and deformable image registration in radiotherapy. J Appl Clin Med Phys 2020; 21:10-24. [PMID: 32915492 PMCID: PMC7075391 DOI: 10.1002/acm2.12957] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 01/13/2020] [Accepted: 05/14/2020] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVES Rigid image registration (RIR) and deformable image registration (DIR) are widely used in radiotherapy. This project aims to capture current international approaches to image registration. METHODS A survey was designed to identify variations in use, resources, implementation, and decision-making criteria for clinical image registration. This was distributed to radiotherapy centers internationally in 2018. RESULTS There were 57 responses internationally, from the Americas (46%), Australia/New Zealand (32%), Europe (12%), and Asia (10%). Rigid image registration and DIR were used clinically for computed tomography (CT)-CT registration (96% and 51%, respectively), followed by CT-PET (81% and 47%), CT-CBCT (84% and 19%), CT-MR (93% and 19%), MR-MR (49% and 5%), and CT-US (9% and 0%). Respondent centers performed DIR using dedicated software (75%) and treatment planning systems (29%), with 84% having some form of DIR software. Centers have clinically implemented DIR for atlas-based segmentation (47%), multi-modality treatment planning (65%), and dose deformation (63%). The clinical use of DIR for multi-modality treatment planning and accounting for retreatments was considered to have the highest benefit-to-risk ratio (69% and 67%, respectively). CONCLUSIONS This survey data provides useful insights on where, when, and how image registration has been implemented in radiotherapy centers around the world. DIR is mainly in clinical use for CT-CT (51%) and CT-PET (47%) for the head and neck (43-57% over all use cases) region. The highest benefit-risk ratio for clinical use of DIR was for multi-modality treatment planning and accounting for retreatments, which also had higher clinical use than for adaptive radiotherapy and atlas-based segmentation.
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Affiliation(s)
- Thorarin A. Bjarnason
- Medical ImagingInterior Health AuthorityKelownaBCCanada
- RadiologyUniversity of British ColumbiaVancouverBCCanada
- PhysicsUniversity of British Columbia OkanaganKelownaBCCanada
| | - Robert Rees
- Occupational Health & SafetyYukon Workers' Compensation Health and Safety BoardWhitehorseYKCanada
| | - Judy Kainz
- Workers' Safety and Compensation Commission for Northwest Territories and NunavutYellowknifeNTCanada
| | - Lawrence H. Le
- Diagnostic ImagingAlberta Health ServicesCalgaryABCanada
- Radiology and Diagnostic ImagingUniversity of AlbertaEdmontonABCanada
| | | | - Brent Preston
- Radiation Safety UnitGovernment of SaskatchewanSaskatoonSKCanada
| | - Idris Elbakri
- Cancer Care ManitobaWinnipegMBCanada
- Physics and AstronomyUniversity of ManitobaWinnipegMBCanada
- RadiologyUniversity of ManitobaWinnipegMBCanada
| | - Ingvar A. J. Fife
- Cancer Care ManitobaWinnipegMBCanada
- Physics and AstronomyUniversity of ManitobaWinnipegMBCanada
- RadiologyUniversity of ManitobaWinnipegMBCanada
| | - Ting‐Yim Lee
- St Joseph’s Health Care LondonLondonONCanada
- Lawson Research InstituteLondonONCanada
- Medical ImagingMedical Biophysics, OncologyRobarts Research InstituteUniversity of Western OntarioLondonONCanada
| | | | - Clément Arsenault
- Hôpital Dr Georges–L. DumontCentre d'Oncologie Dr Léon–RichardMonctonNBCanada
| | | | | | - Elena Tonkopi
- Nova Scotia Health AuthorityHalifaxNSCanada
- Diagnostic RadiologyDalhousie UniversityHalifaxNSCanada
- Radiation OncologyDalhousie UniversityHalifaxNSCanada
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Vrtovec T, Močnik D, Strojan P, Pernuš F, Ibragimov B. Auto-segmentation of organs at risk for head and neck radiotherapy planning: From atlas-based to deep learning methods. Med Phys 2020; 47:e929-e950. [PMID: 32510603 DOI: 10.1002/mp.14320] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2019] [Revised: 05/27/2020] [Accepted: 05/29/2020] [Indexed: 02/06/2023] Open
Abstract
Radiotherapy (RT) is one of the basic treatment modalities for cancer of the head and neck (H&N), which requires a precise spatial description of the target volumes and organs at risk (OARs) to deliver a highly conformal radiation dose to the tumor cells while sparing the healthy tissues. For this purpose, target volumes and OARs have to be delineated and segmented from medical images. As manual delineation is a tedious and time-consuming task subjected to intra/interobserver variability, computerized auto-segmentation has been developed as an alternative. The field of medical imaging and RT planning has experienced an increased interest in the past decade, with new emerging trends that shifted the field of H&N OAR auto-segmentation from atlas-based to deep learning-based approaches. In this review, we systematically analyzed 78 relevant publications on auto-segmentation of OARs in the H&N region from 2008 to date, and provided critical discussions and recommendations from various perspectives: image modality - both computed tomography and magnetic resonance image modalities are being exploited, but the potential of the latter should be explored more in the future; OAR - the spinal cord, brainstem, and major salivary glands are the most studied OARs, but additional experiments should be conducted for several less studied soft tissue structures; image database - several image databases with the corresponding ground truth are currently available for methodology evaluation, but should be augmented with data from multiple observers and multiple institutions; methodology - current methods have shifted from atlas-based to deep learning auto-segmentation, which is expected to become even more sophisticated; ground truth - delineation guidelines should be followed and participation of multiple experts from multiple institutions is recommended; performance metrics - the Dice coefficient as the standard volumetric overlap metrics should be accompanied with at least one distance metrics, and combined with clinical acceptability scores and risk assessments; segmentation performance - the best performing methods achieve clinically acceptable auto-segmentation for several OARs, however, the dosimetric impact should be also studied to provide clinically relevant endpoints for RT planning.
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Affiliation(s)
- Tomaž Vrtovec
- Faculty Electrical Engineering, University of Ljubljana, Tržaška cesta 25, Ljubljana, SI-1000, Slovenia
| | - Domen Močnik
- Faculty Electrical Engineering, University of Ljubljana, Tržaška cesta 25, Ljubljana, SI-1000, Slovenia
| | - Primož Strojan
- Institute of Oncology Ljubljana, Zaloška cesta 2, Ljubljana, SI-1000, Slovenia
| | - Franjo Pernuš
- Faculty Electrical Engineering, University of Ljubljana, Tržaška cesta 25, Ljubljana, SI-1000, Slovenia
| | - Bulat Ibragimov
- Faculty Electrical Engineering, University of Ljubljana, Tržaška cesta 25, Ljubljana, SI-1000, Slovenia.,Department of Computer Science, University of Copenhagen, Universitetsparken 1, Copenhagen, D-2100, Denmark
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11
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Mittauer KE, Hill PM, Bassetti MF, Bayouth JE. Validation of an MR-guided online adaptive radiotherapy (MRgoART) program: Deformation accuracy in a heterogeneous, deformable, anthropomorphic phantom. Radiother Oncol 2020; 146:97-109. [DOI: 10.1016/j.radonc.2020.02.012] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 02/12/2020] [Accepted: 02/15/2020] [Indexed: 01/11/2023]
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12
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Performance of a deformable image registration algorithm for CT and cone beam CT using physical multi-density geometric and digital anatomic phantoms. Radiol Med 2020; 126:106-116. [PMID: 32350795 DOI: 10.1007/s11547-020-01208-9] [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: 01/25/2020] [Accepted: 04/16/2020] [Indexed: 10/24/2022]
Abstract
PURPOSE To study the accuracy of deformable registration algorithm for CT and cone beam CT (CBCT) using a combination of physical and digital phantoms. MATERIALS AND METHODS The physical phantoms consisted of objects over a range of electron densities, shape and sizes. The system was tested for simple and complex scenarios including performance in the presence of metallic artefacts. Clinically present deformations were simulated using a set of five geometric and anatomic virtual phantoms. RESULTS The system could not account for large changes in size, shape and Hounsfield units. Deformations of low intensity structures and small objects were highly inaccurate, and errors were prominent for volume reduction scenario than volume growth. The presence of artefacts did alter the performance of the algorithm. Objects of low density and that close to artefacts were affected the most. Overall, deformations to CBCT were poor. In virtual phantoms, the system could not handle gas pockets and deformation errors in inverse direction were higher than that in forward direction. CONCLUSION The algorithm was tested for several non-clinical and clinical scenarios. The performance was acceptable for realistic and clinically present deformations. However, it is necessary to tread cautiously for structures with small volumes and large reductions in volume.
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13
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Bohoudi O, Lagerwaard FJ, Bruynzeel AM, Niebuhr NI, Johnen W, Senan S, Slotman BJ, Pfaffenberger A, Palacios MA. End-to-end empirical validation of dose accumulation in MRI-guided adaptive radiotherapy for prostate cancer using an anthropomorphic deformable pelvis phantom. Radiother Oncol 2019; 141:200-207. [DOI: 10.1016/j.radonc.2019.09.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Revised: 09/12/2019] [Accepted: 09/14/2019] [Indexed: 10/25/2022]
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14
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Wu RY, Liu AY, Williamson TD, Yang J, Wisdom PG, Zhu XR, Frank SJ, Fuller CD, Gunn GB, Gao S. Quantifying the accuracy of deformable image registration for cone-beam computed tomography with a physical phantom. J Appl Clin Med Phys 2019; 20:92-100. [PMID: 31541526 PMCID: PMC6806467 DOI: 10.1002/acm2.12717] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 08/16/2019] [Accepted: 08/21/2019] [Indexed: 01/31/2023] Open
Abstract
PURPOSE Kilo-voltage cone-beam computed tomography (CBCT) is widely used for patient alignment, contour propagation, and adaptive treatment planning in radiation therapy. In this study, we evaluated the accuracy of deformable image registration (DIR) for CBCT under various imaging protocols with different noise and patient dose levels. METHODS A physical phantom previously developed to facilitate end-to-end testing of the DIR accuracy was used with Varian Velocity v4.0 software to evaluate the performance of image registration from CT to CT, CBCT to CT, and CBCT to CBCT. The phantom is acrylic and includes several inserts that simulate different tissue shapes and properties. Deformations and anatomic changes were simulated by changing the rotations of both the phantom and the inserts. CT images (from a head and neck protocol) and CBCT images (from pelvis, head and "Image Gently" protocols) were obtained with different image noise and dose levels. Large inserts were filled with Mobil DTE oil to simulate soft tissue, and small inserts were filled with bone materials. All inserts were contoured before the DIR process to provide a ground truth contour size and shape for comparison. After the DIR process, all deformed contours were compared with the originals using Dice similarity coefficient (DSC) and mean distance to agreement (MDA). Both large and small volume of interests (VOIs) for DIR volume selection were tested by simulating a DIR process that included whole patient image volume and clinical target volumes (CTV) only (for CTVs propagation). RESULTS For cross-modality DIR registration (CT to CBCT), the DSC were >0.8 and the MDA were <3 mm for CBCT pelvis, and CBCT head protocols. For CBCT to CBCT and CT to CT, the DIR accuracy was improved relative to the cross-modality tests. For smaller VOIs, the DSC were >0.8 and MDA <2 mm for all modalities. CONCLUSIONS The accuracy of DIR depends on the quality of the CBCT image at different dose and noise levels.
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Affiliation(s)
- Richard Y. Wu
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | - Amy Y. Liu
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | - Tyler D. Williamson
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | - Jinzhong Yang
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | - Paul G. Wisdom
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | - Xiaorong R. Zhu
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | - Steven J. Frank
- Department of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | - Clifton D. Fuller
- Department of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | - Gary B. Gunn
- Department of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | - Song Gao
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
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15
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Liang X, Chen L, Nguyen D, Zhou Z, Gu X, Yang M, Wang J, Jiang S. Generating synthesized computed tomography (CT) from cone-beam computed tomography (CBCT) using CycleGAN for adaptive radiation therapy. Phys Med Biol 2019; 64:125002. [PMID: 31108465 DOI: 10.1088/1361-6560/ab22f9] [Citation(s) in RCA: 130] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Throughout the course of delivering a radiation therapy treatment, which may take several weeks, a patient's anatomy may change drastically, and adaptive radiation therapy (ART) may be needed. Cone-beam computed tomography (CBCT), which is often available during the treatment process, can be used for both patient positioning and ART re-planning. However, due to the prominent amount of noise, artifacts, and inaccurate Hounsfield unit (HU) values, the dose calculation based on CBCT images could be inaccurate for treatment planning. One way to solve this problem is to convert CBCT images to more accurate synthesized CT (sCT) images. In this work, we have developed a cycle-consistent generative adversarial network framework (CycleGAN) to synthesize CT images from CBCT images. This model is capable of image-to-image translation using unpaired CT and CBCT images in an unsupervised learning setting. The sCT images generated from CBCT through this CycleGAN model are visually and quantitatively similar to real CT images with decreased mean absolute error (MAE) from 69.29 HU to 29.85 HU for head-and-neck (H&N) cancer patients. The dose distributions calculated on the sCT by CycleGAN demonstrated a higher accuracy than those on CBCT in a 3D gamma index analysis with increased gamma index pass rate from 86.92% to 96.26% under 1 mm/1% criteria, when using the deformed planning CT image (dpCT) as the reference. We also compared the CycleGAN model with other unsupervised learning methods, including deep convolutional generative adversarial networks (DCGAN) and progressive growing of GANs (PGGAN), and demonstrated that CycleGAN outperformed the other two models. A phantom study has been conducted to compare sCT with dpCT, and the increase of structural similarity index from 0.91 to 0.93 shows that CycleGAN performed better than DIR in terms of preserving anatomical accuracy.
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Affiliation(s)
- Xiao Liang
- Department of Radiation Oncology, Medical Artificial Intelligence and Automation Laboratory, University of Texas Southwestern Medical Center, Dallas, TX, United States of America. Co-first authors
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16
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Abstract
As deformable image registration makes its way into the clinical routine, the summation of doses from fractionated treatment regimens to evaluate cumulative doses to targets and healthy tissues is also becoming a frequently utilized tool in the context of image-guided adaptive radiotherapy. Accounting for daily geometric changes using deformable image registration and dose accumulation potentially enables a better understanding of dose-volume-effect relationships, with the goal of translation of this knowledge to personalization of treatment, to further enhance treatment outcomes. Treatment adaptation involving image deformation requires patient-specific quality assurance of the image registration and dose accumulation processes, to ensure that uncertainties in the 3D dose distributions are identified and appreciated from a clinical relevance perspective. While much research has been devoted to identifying and managing the uncertainties associated with deformable image registration and dose accumulation approaches, there are still many unanswered questions. Here, we provide a review of current deformable image registration and dose accumulation techniques, and related clinical application. We also discuss salient issues that need to be deliberated when applying deformable algorithms for dose mapping and accumulation in the context of adaptive radiotherapy and response assessment.
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17
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Calusi S, Labanca G, Zani M, Casati M, Marrazzo L, Noferini L, Talamonti C, Fusi F, Desideri I, Bonomo P, Livi L, Pallotta S. A multiparametric method to assess the MIM deformable image registration algorithm. J Appl Clin Med Phys 2019; 20:75-82. [PMID: 30924286 PMCID: PMC6448167 DOI: 10.1002/acm2.12564] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Revised: 01/19/2019] [Accepted: 02/25/2019] [Indexed: 11/07/2022] Open
Abstract
A quantitative evaluation of the performances of the deformable image registration (DIR) algorithm implemented in MIM-Maestro was performed using multiple similarity indices. Two phantoms, capable of mimicking different anatomical bending and tumor shrinking were built and computed tomography (CT) studies were acquired after applying different deformations. Three different contrast levels between internal structures were artificially created modifying the original CT values of one dataset. DIR algorithm was applied between datasets with increasing deformations and different contrast levels and manually refined with the Reg Refine tool. DIR algorithm ability in reproducing positions, volumes, and shapes of deformed structures was evaluated using similarity indices such as: landmark distances, Dice coefficients, Hausdorff distances, and maximum diameter differences between segmented structures. Similarity indices values worsen with increasing bending and volume difference between reference and target image sets. Registrations between images with low contrast (40 HU) obtain scores lower than those between images with high contrast (970 HU). The use of Reg Refine tool leads generally to an improvement of similarity parameters values, but the advantage is generally less evident for images with low contrast or when structures with large volume differences are involved. The dependence of DIR algorithm on image deformation extent and different contrast levels is well characterized through the combined use of multiple similarity indices.
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Affiliation(s)
- Silvia Calusi
- Department of Clinical and Experimental Biomedical Sciences "Mario Serio", University of Florence, Florence, Italy
| | - Giusy Labanca
- Department of Clinical and Experimental Biomedical Sciences "Mario Serio", University of Florence, Florence, Italy
| | - Margherita Zani
- Department of Clinical and Experimental Biomedical Sciences "Mario Serio", University of Florence, Florence, Italy
| | - Marta Casati
- Medical Physics Unit, AOU Careggi, Florence, Italy
| | | | | | - Cinzia Talamonti
- Department of Clinical and Experimental Biomedical Sciences "Mario Serio", University of Florence, Florence, Italy.,Medical Physics Unit, AOU Careggi, Florence, Italy
| | - Franco Fusi
- Department of Clinical and Experimental Biomedical Sciences "Mario Serio", University of Florence, Florence, Italy
| | - Isacco Desideri
- Department of Clinical and Experimental Biomedical Sciences "Mario Serio", University of Florence, Florence, Italy.,Radiation Therapy Unit, AOU Careggi, Florence, Italy
| | | | - Lorenzo Livi
- Department of Clinical and Experimental Biomedical Sciences "Mario Serio", University of Florence, Florence, Italy.,Radiation Therapy Unit, AOU Careggi, Florence, Italy
| | - Stefania Pallotta
- Department of Clinical and Experimental Biomedical Sciences "Mario Serio", University of Florence, Florence, Italy.,Medical Physics Unit, AOU Careggi, Florence, Italy
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Castelli J, Simon A, Lafond C, Perichon N, Rigaud B, Chajon E, De Bari B, Ozsahin M, Bourhis J, de Crevoisier R. Adaptive radiotherapy for head and neck cancer. Acta Oncol 2018; 57:1284-1292. [PMID: 30289291 DOI: 10.1080/0284186x.2018.1505053] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
INTRODUCTION Large anatomical variations can be observed during the treatment course intensity-modulated radiotherapy (IMRT) for head and neck cancer (HNC), leading to potential dose variations. Adaptive radiotherapy (ART) uses one or several replanning sessions to correct these variations and thus optimize the delivered dose distribution to the daily anatomy of the patient. This review, which is focused on ART in the HNC, aims to identify the various strategies of ART and to estimate the dosimetric and clinical benefits of these strategies. MATERIAL AND METHODS We performed an electronic search of articles published in PubMed/MEDLINE and Science Direct from January 2005 to December 2016. Among a total of 134 articles assessed for eligibility, 29 articles were ultimately retained for the review. Eighteen studies evaluated dosimetric variations without ART, and 11 studies reported the benefits of ART. RESULTS Eight in silico studies tested a number of replanning sessions, ranging from 1 to 6, aiming primarily to reduce the dose to the parotid glands. The optimal timing for replanning appears to be early during the first two weeks of treatment. Compared to standard IMRT, ART decreases the mean dose to the parotid gland from 0.6 to 6 Gy and the maximum dose to the spinal cord from 0.1 to 4 Gy while improving target coverage and homogeneity in most studies. Only five studies reported the clinical results of ART, and three of those studies included a non-randomized comparison with standard IMRT. These studies suggest a benefit of ART in regard to decreasing xerostomia, increasing quality of life, and increasing local control. Patients with the largest early anatomical and dose variations are the best candidates for ART. CONCLUSION ART may decrease toxicity and improve local control for locally advanced HNC. However, randomized trials are necessary to demonstrate the benefit of ART before using the technique in routine practice.
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Affiliation(s)
- J. Castelli
- Radiotherapy Department, Centre Eugene Marquis, Rennes, France
- INSERM U1099 LTSI, Rennes, France
- Université de Rennes 1, Rennes, France
| | - A. Simon
- INSERM U1099 LTSI, Rennes, France
- Université de Rennes 1, Rennes, France
| | - C. Lafond
- Radiotherapy Department, Centre Eugene Marquis, Rennes, France
- INSERM U1099 LTSI, Rennes, France
- Université de Rennes 1, Rennes, France
| | - N. Perichon
- Radiotherapy Department, Centre Eugene Marquis, Rennes, France
| | - B. Rigaud
- INSERM U1099 LTSI, Rennes, France
- Université de Rennes 1, Rennes, France
| | - E. Chajon
- Radiotherapy Department, Centre Eugene Marquis, Rennes, France
| | - B. De Bari
- Radiotherapy Department, CHU Jean-Minjoz, Besançon, France
| | - M. Ozsahin
- Radiotherapy Department, Lausanne University Hospital, Lausanne, Switzerland
| | - J. Bourhis
- Radiotherapy Department, Lausanne University Hospital, Lausanne, Switzerland
| | - R. de Crevoisier
- Radiotherapy Department, Centre Eugene Marquis, Rennes, France
- INSERM U1099 LTSI, Rennes, France
- Université de Rennes 1, Rennes, France
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19
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Rigaud B, Simon A, Gobeli M, Lafond C, Leseur J, Barateau A, Jaksic N, Castelli J, Williaume D, Haigron P, De Crevoisier R. CBCT-guided evolutive library for cervical adaptive IMRT. Med Phys 2018; 45:1379-1390. [DOI: 10.1002/mp.12818] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Revised: 12/29/2017] [Accepted: 02/02/2018] [Indexed: 11/09/2022] Open
Affiliation(s)
- Bastien Rigaud
- LTSI; Université de Rennes 1; Campus de Beaulieu Rennes F-35042 France
- INSERM; U1099, Campus de Beaulieu Rennes F-35042 France
| | - Antoine Simon
- LTSI; Université de Rennes 1; Campus de Beaulieu Rennes F-35042 France
- INSERM; U1099, Campus de Beaulieu Rennes F-35042 France
| | - Maxime Gobeli
- Radiotherapy Department; Centre Eugene Marquis; Rennes F-35000 France
| | - Caroline Lafond
- LTSI; Université de Rennes 1; Campus de Beaulieu Rennes F-35042 France
- Radiotherapy Department; Centre Eugene Marquis; Rennes F-35000 France
| | - Julie Leseur
- Radiotherapy Department; Centre Eugene Marquis; Rennes F-35000 France
| | - Anais Barateau
- LTSI; Université de Rennes 1; Campus de Beaulieu Rennes F-35042 France
- INSERM; U1099, Campus de Beaulieu Rennes F-35042 France
| | - Nicolas Jaksic
- Radiotherapy Department; Centre Eugene Marquis; Rennes F-35000 France
| | - Joël Castelli
- LTSI; Université de Rennes 1; Campus de Beaulieu Rennes F-35042 France
- INSERM; U1099, Campus de Beaulieu Rennes F-35042 France
- Radiotherapy Department; Centre Eugene Marquis; Rennes F-35000 France
| | - Danièle Williaume
- Radiotherapy Department; Centre Eugene Marquis; Rennes F-35000 France
| | - Pascal Haigron
- LTSI; Université de Rennes 1; Campus de Beaulieu Rennes F-35042 France
- INSERM; U1099, Campus de Beaulieu Rennes F-35042 France
| | - Renaud De Crevoisier
- LTSI; Université de Rennes 1; Campus de Beaulieu Rennes F-35042 France
- INSERM; U1099, Campus de Beaulieu Rennes F-35042 France
- Radiotherapy Department; Centre Eugene Marquis; Rennes F-35000 France
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Jamema SV, Phurailatpam R, Paul SN, Joshi K, Deshpande D. Commissioning and validation of commercial deformable image registration software for adaptive contouring. Phys Med 2018; 47:1-8. [DOI: 10.1016/j.ejmp.2018.01.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Revised: 01/08/2018] [Accepted: 01/17/2018] [Indexed: 10/18/2022] Open
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21
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Richter A, Weick S, Krieger T, Exner F, Kellner S, Polat B, Flentje M. Evaluation of a software module for adaptive treatment planning and re-irradiation. Radiat Oncol 2017; 12:205. [PMID: 29282089 PMCID: PMC5745858 DOI: 10.1186/s13014-017-0943-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Accepted: 12/06/2017] [Indexed: 12/02/2022] Open
Abstract
Background The aim of this work is to validate the Dynamic Planning Module in terms of usability and acceptance in the treatment planning workflow. Methods The Dynamic Planning Module was used for decision making whether a plan adaptation was necessary within one course of radiation therapy. The Module was also used for patients scheduled for re-irradiation to estimate the dose in the pretreated region and calculate the accumulated dose to critical organs at risk. During one year, 370 patients were scheduled for plan adaptation or re-irradiation. All patient cases were classified according to their treated body region. For a sub-group of 20 patients treated with RT for lung cancer, the dosimetric effect of plan adaptation during the main treatment course was evaluated in detail. Changes in tumor volume, frequency of re-planning and the time interval between treatment start and plan adaptation were assessed. Results The Dynamic Planning Tool was used in 20% of treated patients per year for both approaches nearly equally (42% plan adaptation and 58% re-irradiation). Most cases were assessed for the thoracic body region (51%) followed by pelvis (21%) and head and neck cases (10%). The sub-group evaluation showed that unintended plan adaptation was performed in 38% of the scheduled cases. A median time span between first day of treatment and necessity of adaptation of 17 days (range 4–35 days) was observed. PTV changed by 12 ± 12% on average (maximum change 42%). PTV decreased in 18 of 20 cases due to tumor shrinkage and increased in 2 of 20 cases. Re-planning resulted in a reduction of the mean lung dose of the ipsilateral side in 15 of 20 cases. Conclusion The experience of one year showed high acceptance of the Dynamic Planning Module in our department for both physicians and medical physicists. The re-planning can potentially reduce the accumulated dose to the organs at risk and ensure a better target volume coverage. In the re-irradiation situation, the Dynamic Planning Tool was used to consider the pretreatment dose, to adapt the actual treatment schema more specifically and to review the accumulated dose.
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Affiliation(s)
- Anne Richter
- Department of Radiation Oncology, University of Wuerzburg, Josef-Schneider-Str. 11, 97080, Wuerzburg, Germany.
| | - Stefan Weick
- Department of Radiation Oncology, University of Wuerzburg, Josef-Schneider-Str. 11, 97080, Wuerzburg, Germany
| | - Thomas Krieger
- Department of Radiation Oncology, University of Wuerzburg, Josef-Schneider-Str. 11, 97080, Wuerzburg, Germany
| | - Florian Exner
- Department of Radiation Oncology, University of Wuerzburg, Josef-Schneider-Str. 11, 97080, Wuerzburg, Germany
| | - Sonja Kellner
- Department of Radiation Oncology, University of Wuerzburg, Josef-Schneider-Str. 11, 97080, Wuerzburg, Germany
| | - Bülent Polat
- Department of Radiation Oncology, University of Wuerzburg, Josef-Schneider-Str. 11, 97080, Wuerzburg, Germany
| | - Michael Flentje
- Department of Radiation Oncology, University of Wuerzburg, Josef-Schneider-Str. 11, 97080, Wuerzburg, Germany
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22
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Liao Y, Wang L, Xu X, Chen H, Chen J, Zhang G, Lei H, Wang R, Zhang S, Gu X, Zhen X, Zhou L. An anthropomorphic abdominal phantom for deformable image registration accuracy validation in adaptive radiation therapy. Med Phys 2017; 44:2369-2378. [PMID: 28317122 DOI: 10.1002/mp.12229] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Revised: 11/23/2016] [Accepted: 03/12/2017] [Indexed: 11/06/2022] Open
Affiliation(s)
- Yuliang Liao
- Department of Biomedical Engineering; Southern Medical University; Guangzhou Guangdong 510515 China
| | - Linjing Wang
- Radiotherapy Center; Affiliated Cancer Hospital & Institute of Guangzhou Medical University; Guangzhou Guangdong 510095 China
| | - Xiangdong Xu
- Department of Radiology; Guangzhou First People's Hospital; Guangzhou Medical University; Guangzhou Guangdong 510180 China
| | - Haibin Chen
- Department of Biomedical Engineering; Southern Medical University; Guangzhou Guangdong 510515 China
| | - Jiawei Chen
- Department of Biomedical Engineering; Southern Medical University; Guangzhou Guangdong 510515 China
| | - Guoqian Zhang
- Radiotherapy Center; Affiliated Cancer Hospital & Institute of Guangzhou Medical University; Guangzhou Guangdong 510095 China
| | - Huaiyu Lei
- Radiotherapy Center; Affiliated Cancer Hospital & Institute of Guangzhou Medical University; Guangzhou Guangdong 510095 China
| | - Ruihao Wang
- Radiotherapy Center; Affiliated Cancer Hospital & Institute of Guangzhou Medical University; Guangzhou Guangdong 510095 China
| | - Shuxu Zhang
- Radiotherapy Center; Affiliated Cancer Hospital & Institute of Guangzhou Medical University; Guangzhou Guangdong 510095 China
| | - Xuejun Gu
- Department of Radiation Oncology; The University of Texas; Southwestern Medical Center; Dallas Texas 75390 USA
| | - Xin Zhen
- Department of Biomedical Engineering; Southern Medical University; Guangzhou Guangdong 510515 China
| | - Linghong Zhou
- Department of Biomedical Engineering; Southern Medical University; Guangzhou Guangdong 510515 China
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Teske H, Bartelheimer K, Meis J, Bendl R, Stoiber EM, Giske K. Construction of a biomechanical head and neck motion model as a guide to evaluation of deformable image registration. Phys Med Biol 2017; 62:N271-N284. [PMID: 28350540 DOI: 10.1088/1361-6560/aa69b6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The use of deformable image registration methods in the context of adaptive radiotherapy leads to uncertainties in the simulation of the administered dose distributions during the treatment course. Evaluation of these methods is a prerequisite to decide if a plan adaptation will improve the individual treatment. Current approaches using manual references limit the validity of evaluation, especially for low-contrast regions. In particular, for the head and neck region, the highly flexible anatomy and low soft tissue contrast in control images pose a challenge to image registration and its evaluation. Biomechanical models promise to overcome this issue by providing anthropomorphic motion modelling of the patient. We introduce a novel biomechanical motion model for the generation and sampling of different postures of the head and neck anatomy. Motion propagation behaviour of the individual bones is defined by an underlying kinematic model. This model interconnects the bones by joints and thus is capable of providing a wide range of motion. Triggered by the motion of the individual bones, soft tissue deformation is described by an extended heterogeneous tissue model based on the chainmail approach. This extension, for the first time, allows the propagation of decaying rotations within soft tissue without the necessity for explicit tissue segmentation. Overall motion simulation and sampling of deformed CT scans including a basic noise model is achieved within 30 s. The proposed biomechanical motion model for the head and neck site generates displacement vector fields on a voxel basis, approximating arbitrary anthropomorphic postures of the patient. It was developed with the intention of providing input data for the evaluation of deformable image registration.
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Affiliation(s)
- Hendrik Teske
- Division of Medical Physics in Radiation Oncology, 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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Jahnke P, Limberg FRP, Gerbl A, Ardila Pardo GL, Braun VPB, Hamm B, Scheel M. Radiopaque Three-dimensional Printing: A Method to Create Realistic CT Phantoms. Radiology 2017; 282:569-575. [DOI: 10.1148/radiol.2016152710] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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25
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Zeng C, Plastaras JP, James P, Tochner ZA, Hill-Kayser CE, Hahn SM, Both S. Proton pencil beam scanning for mediastinal lymphoma: treatment planning and robustness assessment. Acta Oncol 2016; 55:1132-1138. [PMID: 27332881 DOI: 10.1080/0284186x.2016.1191665] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
BACKGROUND Modern radiotherapy (RT) for lymphoma is highly personalized. While advanced imaging is largely employed to define limited treatment volumes, the use of proton pencil beam scanning (PBS) for highly conformal lymphoma RT is still in its infancy. Here, we assess the dosimetric benefits and feasibility of PBS for mediastinal lymphoma (ML). MATERIALS AND METHODS Ten patients were planned using PBS for involved-site RT. The initial plans were calculated on the average four-dimensional computed tomography (4D-CT). PBS plans were compared with 3D conformal radiotherapy (3D-CRT), intensity-modulated radiotherapy (IMRT), and proton double scattering (DS). In order to evaluate the feasibility of PBS and the plan robustness against inter- and intra-fractional uncertainties, the 4D dose was calculated on initial and verification CTs. The deviation of planned dose from delivered dose was measured. The same proton beamline was used for all patients, while another beamline with larger spots was employed for patients with large motion perpendicular to the beam. RESULTS PBS provided the lowest mean lung dose (MLD) and mean heart dose (MHD) for all patients in comparison with 3D-CRT, IMRT, and DS. For eight patients, internal target volume (ITV) D98% was degraded by <3%; and the MLD and MHD deviated by <10% of prescription over the course of treatment when the PBS field was painted twice in each session. For one patient with target motion perpendicular to the beam (>5 mm), the degradation of ITV D98% was 9%, which was effectively mitigated by employing large spots. One patient exhibited large dose degradation due to pericardial effusion, which required replanning across all modalities. CONCLUSIONS This study demonstrates that PBS plans significantly reduce MLD and MHD relative to 3D-CRT, IMRT, and DS and identifies requirements for robust free-breathing ML PBS treatments, showing that PBS plan robustness can be maintained with repainting and/or large spots.
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Affiliation(s)
- Chuan Zeng
- Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - John P. Plastaras
- Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Paul James
- Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Zelig A. Tochner
- Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Christine E. Hill-Kayser
- Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Stephen M. Hahn
- Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Stefan Both
- Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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Liao Y, Chen H, Zhou L, Zhen X. Construction of an anthropopathic abdominal phantom for accuracy validation of deformable image registration. Technol Health Care 2016; 24 Suppl 2:S717-23. [DOI: 10.3233/thc-161200] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Niebuhr NI, Johnen W, Güldaglar T, Runz A, Echner G, Mann P, Möhler C, Pfaffenberger A, Jäkel O, Greilich S. Technical Note: Radiological properties of tissue surrogates used in a multimodality deformable pelvic phantom for MR-guided radiotherapy. Med Phys 2016; 43:908-16. [DOI: 10.1118/1.4939874] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Lafond C, Simon A, Henry O, Périchon N, Castelli J, Acosta O, de Crevoisier R. Radiothérapie adaptative en routine ? État de l’art : point de vue du physicien médical. Cancer Radiother 2015; 19:450-7. [DOI: 10.1016/j.canrad.2015.06.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2015] [Accepted: 06/01/2015] [Indexed: 12/22/2022]
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