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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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Lallement A, Noblet V, Antoni D, Meyer P. Detecting and quantifying spatial misalignment between longitudinal kilovoltage computed tomography (kVCT) scans of the head and neck by using convolutional neural networks (CNNs). Technol Health Care 2023:THC220519. [PMID: 36776082 DOI: 10.3233/thc-220519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
BACKGROUND Adaptive radiotherapy (ART) aims to address anatomical modifications appearing during the treatment of patients by modifying the planning treatment according to the daily positioning image. Clinical implementation of ART relies on the quality of the deformable image registration (DIR) algorithms included in the ART workflow. To translate ART into clinical practice, automatic DIR assessment is needed. OBJECTIVE This article aims to estimate spatial misalignment between two head and neck kilovoltage computed tomography (kVCT) images by using two convolutional neural networks (CNNs). METHODS The first CNN quantifies misalignments between 0 mm and 15 mm and the second CNN detects and classifies misalignments into two classes (poor alignment and good alignment). Both networks take pairs of patches of 33x33x33 mm3 as inputs and use only the image intensity information. The training dataset was built by deforming kVCT images with basis splines (B-splines) to simulate DIR error maps. The test dataset was built using 2500 landmarks, consisting of hard and soft landmark tissues annotated by 6 clinicians at 10 locations. RESULTS The quantification CNN reaches a mean error of 1.26 mm (± 1.75 mm) on the landmark set which, depending on the location, has annotation errors between 1 mm and 2 mm. The errors obtained for the quantification network fit the computed interoperator error. The classification network achieves an overall accuracy of 79.32%, and although the classification network overdetects poor alignments, it performs well (i.e., it achieves a rate of 90.4%) in detecting poor alignments when given one. CONCLUSION The performances of the networks indicate the feasibility of using CNNs for an agnostic and generic approach to misalignment quantification and detection.
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Affiliation(s)
| | | | - Delphine Antoni
- Department of Radiation Therapy, Institut de Cancérologie de Strasbourg, Strasbourg, France
| | - Philippe Meyer
- ICube-UMR 7357, Strasbourg, France.,Department of Medical Physics, Institut de Cancérologie de Strasbourg, Strasbourg, France
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Spadea MF, Maspero M, Zaffino P, Seco J. Deep learning based synthetic-CT generation in radiotherapy and PET: A review. Med Phys 2021; 48:6537-6566. [PMID: 34407209 DOI: 10.1002/mp.15150] [Citation(s) in RCA: 80] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 06/06/2021] [Accepted: 07/13/2021] [Indexed: 01/22/2023] Open
Abstract
Recently,deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: (i) to replace computed tomography in magnetic resonance (MR) based treatment planning, (ii) facilitate cone-beam computed tomography based image-guided adaptive radiotherapy, and (iii) derive attenuation maps for the correction of positron emission tomography. Appropriate database searching was performed on journal articles published between January 2014 and December 2020. The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarizing the achievements. Lastly, the statistics of all the cited works from various aspects were analyzed, revealing the popularity and future trends and the potential of DL-based sCT generation. The current status of DL-based sCT generation was evaluated, assessing the clinical readiness of the presented methods.
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Affiliation(s)
- Maria Francesca Spadea
- Department Experimental and Clinical Medicine, University "Magna Graecia" of Catanzaro, Catanzaro, 88100, Italy
| | - Matteo Maspero
- Division of Imaging & Oncology, Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan, Utrecht, The Netherlands.,Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan, Utrecht, The Netherlands
| | - Paolo Zaffino
- Department Experimental and Clinical Medicine, University "Magna Graecia" of Catanzaro, Catanzaro, 88100, Italy
| | - Joao Seco
- Division of Biomedical Physics in Radiation Oncology, DKFZ German Cancer Research Center, Heidelberg, Germany.,Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
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Kubli A, Pukala J, Shah AP, Kelly P, Langen KM, Bova FJ, Mañon RR, Meeks SL. Variability in commercially available deformable image registration: A multi-institution analysis using virtual head and neck phantoms. J Appl Clin Med Phys 2021; 22:89-96. [PMID: 33783960 PMCID: PMC8130225 DOI: 10.1002/acm2.13242] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 01/28/2021] [Accepted: 03/02/2021] [Indexed: 11/09/2022] Open
Abstract
PURPOSE The purpose of this study was to evaluate the performance of three common deformable image registration (DIR) packages across algorithms and institutions. METHODS AND MATERIALS The Deformable Image Registration Evaluation Project (DIREP) provides ten virtual phantoms derived from computed tomography (CT) datasets of head-and-neck cancer patients over a single treatment course. Using the DIREP phantoms, DIR results from 35 institutions were submitted using either Velocity, MIM, or Eclipse. Submitted deformation vector fields (DVFs) were compared to ground-truth DVFs to calculate target registration error (TRE) for six regions of interest (ROIs). Statistical analysis was performed to determine the variability between each DIR software package and the variability of users within each algorithm. RESULTS Overall mean TRE was 2.04 ± 0.35 mm for Velocity, 1.10 ± 0.29 mm for MIM, and 2.35 ± 0.15 mm for Eclipse. The MIM mean TRE was significantly different than both Velocity and Eclipse for all ROIs. Velocity and Eclipse mean TREs were not significantly different except for when evaluating the registration of the cord or mandible. Significant differences between institutions were found for the MIM and Velocity platforms. However, these differences could be explained by variations in Velocity DIR parameters and MIM software versions. CONCLUSIONS Average TRE was shown to be <3 mm for all three software platforms. However, maximum errors could be larger than 2 cm indicating that care should be exercised when using DIR. While MIM performed statistically better than the other packages, all evaluated algorithms had an average TRE better than the largest voxel dimension. For the phantoms studied here, significant differences between algorithm users were minimal suggesting that the algorithm used may have more impact on DIR accuracy than the particular registration technique employed. A significant difference in TRE was discovered between MIM versions showing that DIR QA should be performed after software upgrades as recommended by TG-132.
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Affiliation(s)
- Alex Kubli
- Department of Radiation Oncology, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Jason Pukala
- Department of Radiation Oncology, Orlando Health Cancer Institute, Orlando, FL, USA
| | - Amish P Shah
- Department of Radiation Oncology, Orlando Health Cancer Institute, Orlando, FL, USA
| | - Patrick Kelly
- Department of Radiation Oncology, Orlando Health Cancer Institute, Orlando, FL, USA
| | - Katja M Langen
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - Frank J Bova
- Department of Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Rafael R Mañon
- Department of Radiation Oncology, Orlando Health Cancer Institute, Orlando, FL, USA
| | - Sanford L Meeks
- Department of Radiation Oncology, Orlando Health Cancer Institute, Orlando, FL, USA
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Mione C, Martin F, Miroir J, Moreau J, Saroul N, Pham Dang N, Bellini R, Lapeyre M, Biau J. Impact of the method chosen for the analysis of recurrences after radiotherapy for head and neck cancers: volume-based, point-based and combined methods. Cancer Radiother 2021; 25:502-506. [PMID: 33762149 DOI: 10.1016/j.canrad.2020.05.021] [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: 04/23/2020] [Accepted: 05/21/2020] [Indexed: 11/25/2022]
Abstract
Intensity modulated radiation therapy for head and neck is a complex technique. Inappropriate delineation and/or dose distribution can lead to recurrences. Analysis of these recurrences should lead to improve clinical practice. For several years, different methods of analysis have been described. The purpose of this review is to describe these different methods and to discuss their advantages and limitations. The first published methods used a volume-based approach studying the entire volume of recurrence according to initial target volumes, or dose distribution. The main limitation of these methods was that the volume of recurrence studied was dependent on the delay in diagnosis of that recurrence. Subsequently, other methods used point-based approaches, conceptualizing recurrence either as a spherical expansion from a core of radioresistant cells (center of mass of recurrence volume) or using a more clinical approach, taking into account tumor expansion pathways. More recently, more precise combined methods have been described, combining the different approaches. The choice of method is decisive for conclusions on the origin of recurrence.
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Affiliation(s)
- C Mione
- Department of Radiotherapy, Jean-Perrin Centre, 58, rue Montalembert, 63011 Clermont-Ferrand, France
| | - F Martin
- Department of Radiotherapy, Jean-Perrin Centre, 58, rue Montalembert, 63011 Clermont-Ferrand, France
| | - J Miroir
- Department of Radiotherapy, Jean-Perrin Centre, 58, rue Montalembert, 63011 Clermont-Ferrand, France
| | - J Moreau
- Department of Radiotherapy, Jean-Perrin Centre, 58, rue Montalembert, 63011 Clermont-Ferrand, France
| | - N Saroul
- Department of ENT Surgery, Centre Hospitalier Universitaire Hôpital Gabriel Montpied, 58, rue Montalembert, 63003 Clermont Ferrand, France
| | - N Pham Dang
- Department of Maxillo-Facial Surgery, Centre Hospitalier Universitaire Hôpital Estaing, 63003 Clermont-Ferrand, France
| | - R Bellini
- Department of Radiology, Centre Jean Perrin, 58, rue Montalembert, 63011 Clermont-Ferrand, France
| | - M Lapeyre
- Department of Radiotherapy, Jean-Perrin Centre, 58, rue Montalembert, 63011 Clermont-Ferrand, France
| | - J Biau
- Department of Radiotherapy, Jean-Perrin Centre, 58, rue Montalembert, 63011 Clermont-Ferrand, France.
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Cao Y, Zhu Z, Rao Y, Qin C, Lin D, Dou Q, Ni D, Wang Y. Edge-Aware Pyramidal Deformable Network for Unsupervised Registration of Brain MR Images. Front Neurosci 2021; 14:620235. [PMID: 33551730 PMCID: PMC7859447 DOI: 10.3389/fnins.2020.620235] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 12/28/2020] [Indexed: 01/17/2023] Open
Abstract
Deformable image registration is of essential important for clinical diagnosis, treatment planning, and surgical navigation. However, most existing registration solutions require separate rigid alignment before deformable registration, and may not well handle the large deformation circumstances. We propose a novel edge-aware pyramidal deformable network (referred as EPReg) for unsupervised volumetric registration. Specifically, we propose to fully exploit the useful complementary information from the multi-level feature pyramids to predict multi-scale displacement fields. Such coarse-to-fine estimation facilitates the progressive refinement of the predicted registration field, which enables our network to handle large deformations between volumetric data. In addition, we integrate edge information with the original images as dual-inputs, which enhances the texture structures of image content, to impel the proposed network pay extra attention to the edge-aware information for structure alignment. The efficacy of our EPReg was extensively evaluated on three public brain MRI datasets including Mindboggle101, LPBA40, and IXI30. Experiments demonstrate our EPReg consistently outperformed several cutting-edge methods with respect to the metrics of Dice index (DSC), Hausdorff distance (HD), and average symmetric surface distance (ASSD). The proposed EPReg is a general solution for the problem of deformable volumetric registration.
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Affiliation(s)
- Yiqin Cao
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Zhenyu Zhu
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Yi Rao
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | | | - Di Lin
- The College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Qi Dou
- Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Dong Ni
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Yi Wang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
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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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Free-to-use DIR solutions in radiotherapy: Benchmark against commercial platforms through a contour-propagation study. Phys Med 2020; 74:110-117. [PMID: 32464468 DOI: 10.1016/j.ejmp.2020.05.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Revised: 05/08/2020] [Accepted: 05/17/2020] [Indexed: 11/22/2022] Open
Abstract
PURPOSE A contour propagation study has been conducted to benchmark three algorithms for Deformable Image Registration (DIR) freely available online against well-established commercial solutions. METHODS ElastiX, BRAINS and Plastimach, available as modules in the open source platform 3DSlicer, were tested as the recent AAPM Task group 132 guidelines proposes. The overlap of the DIR-mapped ROIs in four computational anthropomorphic phantoms was measured. To avoid bias every algorithm was left to run without any human interaction nor particular registration strategy. The accuracy of the algorithms was measured using the Dice Similarity Coefficient (DSC) and Mean Distance to Conformity (MDC) metrics. The registration quality was compared to the recommended geometrical accuracy suggested by AAPM TG132 and to the results of a large population-based study performed with commercial DIR solutions. RESULTS The considered free-to-use DIR solutions generally meet acceptable accuracy and good overlap (DSC > 0.85). Mild failures (DSC < 0.75) were detected only for the smallest structures. In case of extremely severe deformations acceptable accuracy was not met (MDC > 3 mm). The morphing capability of the tested algorithms equals those of commercial systems when the user interaction is avoided. Underperformances were detected only in cases where a specific registration strategy is mandatory to obtain a satisfying match. CONCLUSIONS All of the considered algorithms show performances not inferior to previously published data and have the potential to be good candidates for use in the clinical routine. The results and conclusions only apply to the considered phantoms and should not be considered to be generally applicable and extendable to patient cases.
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