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Missimer JH, Emert F, Lomax AJ, Weber DC. Automatic lung segmentation of magnetic resonance images: A new approach applied to healthy volunteers undergoing enhanced Deep-Inspiration-Breath-Hold for motion-mitigated 4D proton therapy of lung tumors. Phys Imaging Radiat Oncol 2024; 29:100531. [PMID: 38292650 PMCID: PMC10825631 DOI: 10.1016/j.phro.2024.100531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 12/20/2023] [Accepted: 12/30/2023] [Indexed: 02/01/2024] Open
Abstract
Background and purpose Respiratory suppression techniques represent an effective motion mitigation strategy for 4D-irradiation of lung tumors with protons. A magnetic resonance imaging (MRI)-based study applied and analyzed methods for this purpose, including enhanced Deep-Inspiration-Breath-Hold (eDIBH). Twenty-one healthy volunteers (41-58 years) underwent thoracic MR scans in four imaging sessions containing two eDIBH-guided MRIs per session to simulate motion-dependent irradiation conditions. The automated MRI segmentation algorithm presented here was critical in determining the lung volumes (LVs) achieved during eDIBH. Materials and methods The study included 168 MRIs acquired under eDIBH conditions. The lung segmentation algorithm consisted of four analysis steps: (i) image preprocessing, (ii) MRI histogram analysis with thresholding, (iii) automatic segmentation, (iv) 3D-clustering. To validate the algorithm, 46 eDIBH-MRIs were manually contoured. Sørensen-Dice similarity coefficients (DSCs) and relative deviations of LVs were determined as similarity measures. Assessment of intrasessional and intersessional LV variations and their differences provided estimates of statistical and systematic errors. Results Lung segmentation time for 100 2D-MRI planes was ∼ 10 s. Compared to manual lung contouring, the median DSC was 0.94 with a lower 95 % confidence level (CL) of 0.92. The relative volume deviations yielded a median value of 0.059 and 95 % CLs of -0.013 and 0.13. Artifact-based volume errors, mainly of the trachea, were estimated. Estimated statistical and systematic errors ranged between 6 and 8 %. Conclusions The presented analytical algorithm is fast, precise, and readily available. The results are comparable to time-consuming, manual segmentations and other automatic segmentation approaches. Post-processing to remove image artifacts is under development.
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Affiliation(s)
- John H. Missimer
- Center for Proton Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland
| | - Frank Emert
- Center for Proton Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland
| | - Antony J. Lomax
- Center for Proton Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland
- Department of Physics, ETH Zurich, Zurich, Switzerland
| | - Damien C. Weber
- Center for Proton Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
- Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Switzerland
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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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Dossun C, Niederst C, Noel G, Meyer P. Evaluation of DIR algorithm performance in real patients for radiotherapy treatments: A systematic review of operator-dependent strategies. Phys Med 2022; 101:137-157. [PMID: 36007403 DOI: 10.1016/j.ejmp.2022.08.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 07/21/2022] [Accepted: 08/16/2022] [Indexed: 11/15/2022] Open
Abstract
PURPOSE The performance of deformable medical image registration (DIR) algorithms has become a major concern. METHODS We aimed to obtain updated information on DIR algorithm performance quantification through a literature review of articles published between 2010 and 2022. We focused only on studies using operator-based methods to treat real patients. The PubMed, Google Scholar and Embase databases were searched following PRISMA guidelines. RESULTS One hundred and seven articles were identified. The mean number of patients and registrations per publication was 20 and 63, respectively. We found 23 different geometric metrics appearing at least twice, and the dosimetric impact of DIR was quantified in 32 articles. Forty-eight different at-risk organs were described, and target volumes were studied in 43 publications. Prostate, head-and-neck and thoracic locations represented more than ¾ of the studied locations. We summarized the type of DIR and the images used, and other key elements. Intra/interobserver variability, threshold values and the correlation between metrics were also discussed. CONCLUSIONS This literature review covers the past decade and should facilitate the implementation of DIR algorithms in clinical practice by providing practical and pertinent information to quantify their performance on real patients.
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Affiliation(s)
- C Dossun
- Department of Radiotherapy, Institut de Cancerologie Strasbourg Europe (ICANS), Strasbourg, France
| | - C Niederst
- Department of Radiotherapy, Institut de Cancerologie Strasbourg Europe (ICANS), Strasbourg, France
| | - G Noel
- Department of Radiotherapy, Institut de Cancerologie Strasbourg Europe (ICANS), Strasbourg, France
| | - P Meyer
- Department of Radiotherapy, Institut de Cancerologie Strasbourg Europe (ICANS), Strasbourg, France; ICUBE, CNRS UMR 7357, Team IMAGES, Strasbourg, France.
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Omidi A, Weiss E, Wilson JS, Rosu-Bubulac M. Quantitative assessment of intra- and inter-modality deformable image registration of the heart, left ventricle, and thoracic aorta on longitudinal 4D-CT and MR images. J Appl Clin Med Phys 2021; 23:e13500. [PMID: 34962065 PMCID: PMC8833287 DOI: 10.1002/acm2.13500] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 09/17/2021] [Accepted: 11/29/2021] [Indexed: 12/25/2022] Open
Abstract
Purpose Magnetic resonance imaging (MRI)‐based investigations into radiotherapy (RT)‐induced cardiotoxicity require reliable registrations of magnetic resonance (MR) imaging to planning computed tomography (CT) for correlation to regional dose. In this study, the accuracy of intra‐ and inter‐modality deformable image registration (DIR) of longitudinal four‐dimensional CT (4D‐CT) and MR images were evaluated for heart, left ventricle (LV), and thoracic aorta (TA). Methods and materials Non‐cardiac‐gated 4D‐CT and T1 volumetric interpolated breath‐hold examination (T1‐VIBE) MRI datasets from five lung cancer patients were obtained at two breathing phases (inspiration/expiration) and two time points (before treatment and 5 weeks after initiating RT). Heart, LV, and TA were manually contoured. Each organ underwent three intramodal DIRs ((A) CT modality over time, (B) MR modality over time, and (C) MR contrast effect at the same time) and two intermodal DIRs ((D) CT/MR multimodality at same time and (E) CT/MR multimodality over time). Hausdorff distance (HD), mean distance to agreement (MDA), and Dice were evaluated and assessed for compliance with American Association of Physicists in Medicine (AAPM) Task Group (TG)‐132 recommendations. Results Mean values of HD, MDA, and Dice under all registration scenarios for each region of interest ranged between 8.7 and 16.8 mm, 1.0 and 2.6 mm, and 0.85 and 0.95, respectively, and were within the TG‐132 recommended range (MDA < 3 mm, Dice > 0.8). Intramodal DIR showed slightly better results compared to intermodal DIR. Heart and TA demonstrated higher registration accuracy compared to LV for all scenarios except for HD and Dice values in Group A. Significant differences for each metric and tissue of interest were noted between Groups B and D and between Groups B and E. MDA and Dice significantly differed between LV and heart in all registrations except for MDA in Group E. Conclusions DIR of the heart, LV, and TA between non‐cardiac‐gated longitudinal 4D‐CT and MRI across two modalities, breathing phases, and pre/post‐contrast is acceptably accurate per AAPM TG‐132 guidelines. This study paves the way for future evaluation of RT‐induced cardiotoxicity and its related factors using multimodality DIR.
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Affiliation(s)
- Alireza Omidi
- Department of Biomedical Engineering, College of Engineering, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Elisabeth Weiss
- Department of Radiation Oncology, Virginia Commonwealth University Health, Richmond, Virginia, USA
| | - John S Wilson
- Department of Biomedical Engineering, College of Engineering, Virginia Commonwealth University, Richmond, Virginia, USA.,Pauley Heart Center, Virginia Commonwealth University Health System, Richmond, Virginia, USA
| | - Mihaela Rosu-Bubulac
- Department of Radiation Oncology, Virginia Commonwealth University Health, Richmond, Virginia, USA
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Konuthula N, Perez FA, Maga AM, Abuzeid WM, Moe K, Hannaford B, Bly RA. Automated atlas-based segmentation for skull base surgical planning. Int J Comput Assist Radiol Surg 2021; 16:933-941. [PMID: 34009539 DOI: 10.1007/s11548-021-02390-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 04/27/2021] [Indexed: 11/26/2022]
Abstract
PURPOSE Computational surgical planning tools could help develop novel skull base surgical approaches that improve safety and patient outcomes. This defines a need for automated skull base segmentation to improve the usability of surgical planning software. The objective of this work was to design and validate an algorithm for atlas-based automated segmentation of skull base structures in individual image sets for skull base surgical planning. METHODS Advanced Normalization Tools software was used to construct a synthetic CT template from 6 subjects, and skull base structures were manually segmented to create a reference atlas. Landmark registration followed by Elastix deformable registration was applied to the template to register it to each of the 30 trusted reference image sets. Dice coefficient, average Hausdorff distance, and clinical usability scoring were used to compare the atlas segmentations to those of the trusted reference image sets. RESULTS The mean for average Hausdorff distance for all structures was less than 2 mm (mean for 95th percentile Hausdorff distance was less than 5 mm). For structures greater than 2.5 mL in volume, the average Dice coefficient was 0.73 (range 0.59-0.82), and for structures less than 2.5 mL in volume the Dice coefficient was less than 0.7. The usability scoring survey was completed by three experts, and all structures met the criteria for acceptable effort except for the foramen spinosum, rotundum, and carotid artery, which required more than minor corrections. CONCLUSION Currently available open-source algorithms, such as the Elastix deformable algorithm, can be used for automated atlas-based segmentation of skull base structures with acceptable clinical accuracy and minimal corrections with the use of the proposed atlas. The first publicly available CT template and anterior skull base segmentation atlas being released (available at this link: http://hdl.handle.net/1773/46259 ) with this paper will allow for general use of automated atlas-based segmentation of the skull base.
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Affiliation(s)
- Neeraja Konuthula
- Department of Otolaryngology, Head and Neck Surgery, University of Washington, Seattle, WA, USA
| | - Francisco A Perez
- Department of Radiology, University of Washington, Seattle, WA, USA
- Division of Radiology, Seattle Children's Hospital, Seattle, WA, USA
| | - A Murat Maga
- Department of Craniofacial Medicine, University of Washington, Seattle, WA, USA
- Craniofacial Center, Seattle Children's Hospital, Seattle, WA, USA
| | - Waleed M Abuzeid
- Department of Otolaryngology, Head and Neck Surgery, University of Washington, Seattle, WA, USA
| | - Kris Moe
- Department of Otolaryngology, Head and Neck Surgery, University of Washington, Seattle, WA, USA
- Otolaryngology-Head and Neck Surgery, Harborview Medical Center, Seattle, WA, USA
| | - Blake Hannaford
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Randall A Bly
- Department of Otolaryngology, Head and Neck Surgery, University of Washington, Seattle, WA, USA.
- Division of Pediatric Otolaryngology, Head and Neck Surgery, Seattle Children's Hospital, Seattle, WA, USA.
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Vickress J, Rangel Baltazar MA, Afsharpour H. Evaluation of Varian's SmartAdapt for clinical use in radiation therapy for patients with thoracic lesions. J Appl Clin Med Phys 2021; 22:150-156. [PMID: 33570225 PMCID: PMC7984488 DOI: 10.1002/acm2.13194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 05/21/2020] [Accepted: 01/05/2021] [Indexed: 11/25/2022] Open
Abstract
INTRODUCTION Deformable image registration (DIR) is a required tool in any adaptive radiotherapy program to help account for anatomical changes that occur during a multifraction treatment. SmartAdapt is a DIR tool from Varian incorporated within the eclipse treatment planning system, that can be used for contour propagation and transfer of PET, MRI, or computed tomography (CT) data. The purpose of this work is to evaluate the registration and contour propagation accuracy of SmartAdapt for thoracic CT studies using the guidelines from AAPM TG 132. METHODS To evaluate the registration accuracy of SmartAdapt the mean target registration error (TRE) was measured for ten landmarked 4DCT images from the https://www.dir-labs.com/ which included 300 landmarks matching the inspiration and expiration phase images. To further characterize the registration accuracy, the magnitude of deformation for each 4DCT was measured and compared against the mean TRE for each study. Contour propagation accuracy was evaluated using 22 randomly selected lung cancer cases from our center where there was either a replan, or the patient was treated for a new lesion within the lung. Contours evaluated included the right and left lung, esophagus, spinal canal, heart and the GTV and the results were quantified using the DICE similarity coefficient. RESULTS The mean TRE from all ten cases was 1.89 mm, the maximum mean TRE per case was 3.8 mm from case #8, which also had the most landmark pairs with displacements >2 cm. For contour propagation accuracy, the DICE coefficient results for left lung, right lung, heart, esophagus, and spinal canal were 0.93, 0.94, 0.90, 0.61, and 0.82 respectively. CONCLUSION The results from our study demonstrate that for thoracic images SmartAdapt in most cases will be accurate to below 2 mm in registration error unless there is deformation greater than 2 cm.
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Affiliation(s)
- Jason Vickress
- Trillium Health Partners/the Credit Valley HospitalMississaugaONCanada
- Department of Radiation OncologyUniversity of TorontoTorontoONCanada
| | | | - Hossein Afsharpour
- Trillium Health Partners/the Credit Valley HospitalMississaugaONCanada
- Department of Radiation OncologyUniversity of TorontoTorontoONCanada
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Zaffino P, Pernelle G, Mastmeyer A, Mehrtash A, Zhang H, Kikinis R, Kapur T, Francesca Spadea M. Fully automatic catheter segmentation in MRI with 3D convolutional neural networks: application to MRI-guided gynecologic brachytherapy. Phys Med Biol 2019; 64:165008. [PMID: 31272095 DOI: 10.1088/1361-6560/ab2f47] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
External-beam radiotherapy followed by high dose rate (HDR) brachytherapy is the standard-of-care for treating gynecologic cancers. The enhanced soft-tissue contrast provided by magnetic resonance imaging (MRI) makes it a valuable imaging modality for diagnosing and treating these cancers. However, in contrast to computed tomography (CT) imaging, the appearance of the brachytherapy catheters, through which radiation sources are inserted to reach the cancerous tissue later on, is often variable across images. This paper reports, for the first time, a new deep-learning-based method for fully automatic segmentation of multiple closely spaced brachytherapy catheters in intraoperative MRI. Represented in the data are 50 gynecologic cancer patients treated by MRI-guided HDR brachytherapy. For each patient, a single intraoperative MRI was used. 826 catheters in the images were manually segmented by an expert radiation physicist who is also a trained radiation oncologist. The number of catheters in a patient ranged between 10 and 35. A deep 3D convolutional neural network (CNN) model was developed and trained. In order to make the learning process more robust, the network was trained 5 times, each time using a different combination of shown patients. Finally, each test case was processed by the five networks and the final segmentation was generated by voting on the obtained five candidate segmentations. 4-fold validation was executed and all the patients were segmented. An average distance error of 2.0 ± 3.4 mm was achieved. False positive and false negative catheters were 6.7% and 1.5% respectively. Average Dice score was equal to 0.60 ± 0.17. The algorithm is available for use in the open source software platform 3D Slicer allowing for wide scale testing and research discussion. In conclusion, to the best of our knowledge, fully automatic segmentation of multiple closely spaced catheters from intraoperative MR images was achieved for the first time in gynecological brachytherapy.
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Affiliation(s)
- Paolo Zaffino
- Department of Experimental and Clinical Medicine, Magna Graecia University, 88100, Catanzaro, Italy. Author to whom any correspondence should be addressed
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Paganelli C, Meschini G, Molinelli S, Riboldi M, Baroni G. “Patient-specific validation of deformable image registration in radiation therapy: Overview and caveats”. Med Phys 2018; 45:e908-e922. [DOI: 10.1002/mp.13162] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 07/30/2018] [Accepted: 08/24/2018] [Indexed: 12/26/2022] Open
Affiliation(s)
- Chiara Paganelli
- Dipartimento di Elettronica, Informazione e Bioingegneria; Politecnico di Milano; Milano 20133 Italy
| | - Giorgia Meschini
- Dipartimento di Elettronica, Informazione e Bioingegneria; Politecnico di Milano; Milano 20133 Italy
| | | | - Marco Riboldi
- Department of Medical Physics; Ludwig-Maximilians-Universitat Munchen; Munich 80539 Germany
| | - Guido Baroni
- Dipartimento di Elettronica, Informazione e Bioingegneria; Politecnico di Milano; Milano 20133 Italy
- Centro Nazionale di Adroterapia Oncologica; Pavia 27100 Italy
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Guy CL, Weiss E, Che S, Jan N, Zhao S, Rosu-Bubulac M. Evaluation of Image Registration Accuracy for Tumor and Organs at Risk in the Thorax for Compliance With TG 132 Recommendations. Adv Radiat Oncol 2018; 4:177-185. [PMID: 30706026 PMCID: PMC6349597 DOI: 10.1016/j.adro.2018.08.023] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Revised: 06/22/2018] [Accepted: 08/29/2018] [Indexed: 11/16/2022] Open
Abstract
Purpose To evaluate accuracy for 2 deformable image registration methods (in-house B-spline and MIM freeform) using image pairs exhibiting changes in patient orientation and lung volume and to assess the appropriateness of registration accuracy tolerances proposed by the American Association of Physicists in Medicine Task Group 132 under such challenging conditions via assessment by expert observers. Methods and Materials Four-dimensional computed tomography scans for 12 patients with lung cancer were acquired with patients in prone and supine positions. Tumor and organs at risk were delineated by a physician on all data sets: supine inhale (SI), supine exhale, prone inhale, and prone exhale. The SI image was registered to the other images using both registration methods. All SI contours were propagated using the resulting transformations and compared with physician delineations using Dice similarity coefficient, mean distance to agreement, and Hausdorff distance. Additionally, propagated contours were anonymized along with ground-truth contours and rated for quality by physician-observers. Results Averaged across all patients, the accuracy metrics investigated remained within tolerances recommended by Task Group 132 (Dice similarity coefficient >0.8, mean distance to agreement <3 mm). MIM performed better with both complex (vertebrae) and low-contrast (esophagus) structures, whereas the in-house method performed better with lungs (whole and individual lobes). Accuracy metrics worsened but remained within tolerances when propagating from supine to prone; however, the Jacobian determinant contained regions with negative values, indicating localized nonphysiologic deformations. For MIM and in-house registrations, 50% and 43.8%, respectively, of propagated contours were rated acceptable as is and 8.2% and 11.0% as clinically unacceptable. Conclusions The deformable image registration methods performed reliably and met recommended tolerances despite anatomically challenging cases exceeding typical interfraction variability. However, additional quality assurance measures are necessary for complex applications (eg, dose propagation). Human review rather than unsupervised implementation should always be part of the clinical registration workflow.
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Affiliation(s)
- Christopher L Guy
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia
| | - Elisabeth Weiss
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia
| | - Shaomin Che
- Department of Radiation Oncology, The 1st Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China
| | - Nuzhat Jan
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia
| | - Sherry Zhao
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia
| | - Mihaela Rosu-Bubulac
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia
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Zhang J, Markova S, Garcia A, Huang K, Nie X, Choi W, Lu W, Wu A, Rimner A, Li G. Evaluation of automatic contour propagation in T2-weighted 4DMRI for normal-tissue motion assessment using internal organ-at-risk volume (IRV). J Appl Clin Med Phys 2018; 19:598-608. [PMID: 30112797 PMCID: PMC6123161 DOI: 10.1002/acm2.12431] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Revised: 05/19/2018] [Accepted: 07/01/2018] [Indexed: 12/25/2022] Open
Abstract
Purpose The purpose of this study was to evaluate the quality of automatically propagated contours of organs at risk (OARs) based on respiratory‐correlated navigator‐triggered four‐dimensional magnetic resonance imaging (RC‐4DMRI) for calculation of internal organ‐at‐risk volume (IRV) to account for intra‐fractional OAR motion. Methods and Materials T2‐weighted RC‐4DMRI images were of 10 volunteers acquired and reconstructed using an internal navigator‐echo surrogate and concurrent external bellows under an IRB‐approved protocol. Four major OARs (lungs, heart, liver, and stomach) were delineated in the 10‐phase 4DMRI. Two manual‐contour sets were delineated by two clinical personnel and two automatic‐contour sets were propagated using free‐form deformable image registration. The OAR volume variation within the 10‐phase cycle was assessed and the IRV was calculated as the union of all OAR contours. The OAR contour similarity between the navigator‐triggered and bellows‐rebinned 4DMRI was compared. A total of 2400 contours were compared to the most probable ground truth with a 95% confidence level (S95) in similarity, sensitivity, and specificity using the simultaneous truth and performance level estimation (STAPLE) algorithm. Results Visual inspection of automatically propagated contours finds that approximately 5–10% require manual correction. The similarity, sensitivity, and specificity between manual and automatic contours are indistinguishable (P > 0.05). The Jaccard similarity indexes are 0.92 ± 0.02 (lungs), 0.89 ± 0.03 (heart), 0.92 ± 0.02 (liver), and 0.83 ± 0.04 (stomach). Volume variations within the breathing cycle are small for the heart (2.6 ± 1.5%), liver (1.2 ± 0.6%), and stomach (2.6 ± 0.8%), whereas the IRV is much larger than the OAR volume by: 20.3 ± 8.6% (heart), 24.0 ± 8.6% (liver), and 47.6 ± 20.2% (stomach). The Jaccard index is higher in navigator‐triggered than bellows‐rebinned 4DMRI by 4% (P < 0.05), due to the higher image quality of navigator‐based 4DMRI. Conclusion Automatic and manual OAR contours from Navigator‐triggered 4DMRI are not statistically distinguishable. The navigator‐triggered 4DMRI image provides higher contour quality than bellows‐rebinned 4DMRI. The IRVs are 20–50% larger than OAR volumes and should be considered in dose estimation.
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Affiliation(s)
- Jingjing Zhang
- Department of Radiation Oncology, Zhongshan Hospital of Sun Yat-Sen University, Zhongshan, China.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Svetlana Markova
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Alejandro Garcia
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Kirk Huang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Xingyu Nie
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Wookjin Choi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Wei Lu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Abraham Wu
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Andreas Rimner
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Guang Li
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
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Chapman CH, Polan D, Vineberg K, Jolly S, Maturen KE, Brock KK, Prisciandaro JI. Deformable image registration–based contour propagation yields clinically acceptable plans for MRI-based cervical cancer brachytherapy planning. Brachytherapy 2018; 17:360-367. [DOI: 10.1016/j.brachy.2017.11.019] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 11/27/2017] [Accepted: 11/30/2017] [Indexed: 11/25/2022]
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Sugawara Y, Tachibana H, Kadoya N, Kitamura N, Sawant A, Jingu K. Prognostic factors associated with the accuracy of deformable image registration in lung cancer patients treated with stereotactic body radiotherapy. Med Dosim 2017; 42:326-333. [PMID: 28802976 DOI: 10.1016/j.meddos.2017.07.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2016] [Revised: 03/28/2017] [Accepted: 07/03/2017] [Indexed: 11/19/2022]
Abstract
We evaluated the accuracy of an in-house program in lung stereotactic body radiation therapy (SBRT) cancer patients, and explored the prognostic factors associated with the accuracy of deformable image registrations (DIRs). The accuracy of the 3 programs which implement the free-form deformation and the B-spline algorithm was compared regarding the structures on 4-dimensional computed tomography (4DCT) image datasets between the peak-inhale and peak-exhale phases. The dice similarity coefficient (DSC) and normalized DSC (NDSC) were measured for the gross tumor volumes from 19 lung SBRT patients. We evaluated the accuracy of DIR using gross tumor volume, magnitude of displacement from 0% phase to 50% phase, whole lung volume in the 50% phase image, and status of tumor pleural attachment. The median NDSC values using the NiftyReg, MIM Maestro and Velocity AI programs were 1.027, 1.005, and 0.946, respectively, indicating that NiftyReg and MIM Maestro programs had similar accuracy with an uncertainty of < 1 mm. Larger uncertainty of 1 to 2 mm was observed using the Velocity AI program. The NiftyReg and the MIM programs provided higher NDSC values than the median values when the gross tumor volume was attached to the pleura (p <0.05). However, it showed a different trend in using the Velocity AI program. All software programs provided unexpected results, and there is a possibility that such results would reduce the accuracy of 4D treatment planning and adaptive radiotherapy. The unexpected results may be because the tumors are surrounded by other tissues, and there are differences regarding the region of interest for rigid and nonrigid registration. Furthermore, our results indicated that the pleural attachment status might be an important predictor of DIR accuracy for thoracic images, indicating that there is a potentially large dose distribution discrepancy concerning 4D treatment planning and adaptive radiotherapy.
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Affiliation(s)
- Yasuharu Sugawara
- Department of Radiology, National Center for Global Health and Medicine, Tokyo, Japan; Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Miyagi, Japan
| | - Hidenobu Tachibana
- Particle Therapy Division, Research Center for Innovative Oncology, National Cancer Center Hospital East, Chiba, Japan.
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Miyagi, Japan
| | - Nozomi Kitamura
- Department of Radiation Oncology, Cancer Institute Hospital of the Japanese Foundation of Cancer Research, Tokyo, Japan
| | - Amit Sawant
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Texas, USA
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Miyagi, Japan
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Moriya S, Tachibana H, Kitamura N, Sawant A, Sato M. Dose warping performance in deformable image registration in lung. Phys Med 2017; 37:16-23. [DOI: 10.1016/j.ejmp.2017.03.016] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Revised: 02/13/2017] [Accepted: 03/20/2017] [Indexed: 10/19/2022] Open
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Ciardo D, Gerardi MA, Vigorito S, Morra A, Dell'acqua V, Diaz FJ, Cattani F, Zaffino P, Ricotti R, Spadea MF, Riboldi M, Orecchia R, Baroni G, Leonardi MC, Jereczek-Fossa BA. Atlas-based segmentation in breast cancer radiotherapy: Evaluation of specific and generic-purpose atlases. Breast 2017; 32:44-52. [DOI: 10.1016/j.breast.2016.12.010] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2016] [Revised: 11/21/2016] [Accepted: 12/18/2016] [Indexed: 12/22/2022] Open
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15
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Ciller C, De Zanet SI, Rüegsegger MB, Pica A, Sznitman R, Thiran JP, Maeder P, Munier FL, Kowal JH, Cuadra MB. Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A Novel Statistical Shape Model for Treatment Planning of Retinoblastoma. Int J Radiat Oncol Biol Phys 2015; 92:794-802. [PMID: 26104933 DOI: 10.1016/j.ijrobp.2015.02.056] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2014] [Revised: 02/18/2015] [Accepted: 02/25/2015] [Indexed: 10/23/2022]
Abstract
PURPOSE Proper delineation of ocular anatomy in 3-dimensional (3D) imaging is a big challenge, particularly when developing treatment plans for ocular diseases. Magnetic resonance imaging (MRI) is presently used in clinical practice for diagnosis confirmation and treatment planning for treatment of retinoblastoma in infants, where it serves as a source of information, complementary to the fundus or ultrasonographic imaging. Here we present a framework to fully automatically segment the eye anatomy for MRI based on 3D active shape models (ASM), and we validate the results and present a proof of concept to automatically segment pathological eyes. METHODS AND MATERIALS Manual and automatic segmentation were performed in 24 images of healthy children's eyes (3.29 ± 2.15 years of age). Imaging was performed using a 3-T MRI scanner. The ASM consists of the lens, the vitreous humor, the sclera, and the cornea. The model was fitted by first automatically detecting the position of the eye center, the lens, and the optic nerve, and then aligning the model and fitting it to the patient. We validated our segmentation method by using a leave-one-out cross-validation. The segmentation results were evaluated by measuring the overlap, using the Dice similarity coefficient (DSC) and the mean distance error. RESULTS We obtained a DSC of 94.90 ± 2.12% for the sclera and the cornea, 94.72 ± 1.89% for the vitreous humor, and 85.16 ± 4.91% for the lens. The mean distance error was 0.26 ± 0.09 mm. The entire process took 14 seconds on average per eye. CONCLUSION We provide a reliable and accurate tool that enables clinicians to automatically segment the sclera, the cornea, the vitreous humor, and the lens, using MRI. We additionally present a proof of concept for fully automatically segmenting eye pathology. This tool reduces the time needed for eye shape delineation and thus can help clinicians when planning eye treatment and confirming the extent of the tumor.
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Affiliation(s)
- Carlos Ciller
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Ophthalmic Technology Group, ARTORG Center of the University of Bern, Bern, Switzerland; Centre d'Imagerie BioMédicale, University of Lausanne, Lausanne, Switzerland.
| | - Sandro I De Zanet
- Ophthalmic Technology Group, ARTORG Center of the University of Bern, Bern, Switzerland; Department of Ophthalmology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Michael B Rüegsegger
- Ophthalmic Technology Group, ARTORG Center of the University of Bern, Bern, Switzerland; Department of Ophthalmology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Alessia Pica
- Department of Radiation Oncology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Raphael Sznitman
- Ophthalmic Technology Group, ARTORG Center of the University of Bern, Bern, Switzerland; Department of Ophthalmology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Jean-Philippe Thiran
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Philippe Maeder
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Francis L Munier
- Unit of Pediatric Ocular Oncology, Jules Gonin Eye Hospital, Lausanne, Switzerland
| | - Jens H Kowal
- Ophthalmic Technology Group, ARTORG Center of the University of Bern, Bern, Switzerland; Department of Ophthalmology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Meritxell Bach Cuadra
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Centre d'Imagerie BioMédicale, University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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Contrast-Enhanced Proton Radiography for Patient Set-up by Using X-Ray CT Prior Knowledge. Int J Radiat Oncol Biol Phys 2014; 90:628-36. [DOI: 10.1016/j.ijrobp.2014.06.057] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2014] [Revised: 05/28/2014] [Accepted: 06/20/2014] [Indexed: 11/17/2022]
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Kupelian P, Sonke JJ. Magnetic Resonance–Guided Adaptive Radiotherapy: A Solution to the Future. Semin Radiat Oncol 2014; 24:227-32. [DOI: 10.1016/j.semradonc.2014.02.013] [Citation(s) in RCA: 81] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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