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Palacios MA, Gerganov G, Cobussen P, Tetar SU, Finazzi T, Slotman BJ, Senan S, Haasbeek CJ, Kawrakow I. Accuracy of deformable image registration-based intra-fraction motion management in Magnetic Resonance-guided radiotherapy. Phys Imaging Radiat Oncol 2023; 26:100437. [PMID: 37089906 PMCID: PMC10113900 DOI: 10.1016/j.phro.2023.100437] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 03/29/2023] [Accepted: 03/30/2023] [Indexed: 04/07/2023] Open
Abstract
Background and Purpose Intra-fraction motion management is key in Stereotactic Ablative Radiotherapy (SABR) gated delivery. This study assessed the accuracy of automatic tumor segmentation in the delivery of MR-guided radiotherapy (MRgRT) by comparing it to manual delineations performed by experienced observers. Materials and Methods Twenty patients previously treated with MR-guided SABR for thoracic and abdominal tumors were included. Five observers with at least two years of experience in MRgRT manually delineated the gross tumor volume (GTV) for 20 patients on 240 frames of a cine MRI on a sagittal plane. Deformable Image Registration (DIR) based GTV contours were propagated using four different algorithms from a reference frame to subsequent frames.Geometrical analysis based on the Dice Similarity Coefficient (DSC), centroid distance and Hausdorff Distance (HDD) were performed to assess the inter-observer variability and the accuracy of automatic segmentation. A Confidence Value (CV) metric for the reliability of the tumor auto-contouring was also calculated. Results Inter-observer delineation variability resulted in mean DSC of 0.89, HDD of 5.8 mm and centroid distance of 1.7 mm. Tumor auto-contouring by the four DIR algorithms resulted in an excellent agreement with the manual delineations by the experienced observers. Mean DSC for each algorithm across all patients was greater than 0.90, whereas the HDD and centroid distances were below 4.0 mm and 1.5 mm, respectively. The CV showed a strong correlation with the DSC. Conclusions DIR-based auto-contouring in MRgRT exhibited a high level of agreement with the manual contouring performed by experts, allowing accurate gated delivery.
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Frueh M, Kuestner T, Nachbar M, Thorwarth D, Schilling A, Gatidis S. Self-supervised learning for automated anatomical tracking in medical image data with minimal human labeling effort. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 225:107085. [PMID: 36044801 DOI: 10.1016/j.cmpb.2022.107085] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 08/02/2022] [Accepted: 08/23/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Tracking of anatomical structures in time-resolved medical image data plays an important role for various tasks such as volume change estimation or treatment planning. State-of-the-art deep learning techniques for automated tracking, while providing accurate results, require large amounts of human-labeled training data making their wide-spread use time- and resource-intensive. Our contribution in this work is the implementation and adaption of a self-supervised learning (SSL) framework that addresses this bottleneck of training data generation. METHODS To this end we adapted and implemented an SSL framework that allows for automated anatomical tracking without the necessity for human-labeled training data. We evaluated this method by comparison to conventional- and deep learning optical flow (OF)-based tracking methods. We applied all methods on three different time-resolved medical image datasets (abdominal MRI, cardiac MRI, and echocardiography) and assessed their accuracy regarding tracking of pre-defined anatomical structures within and across individuals. RESULTS We found that SSL-based tracking as well as OF-based methods provide accurate results for simple, rigid and smooth motion patterns. However, regarding more complex motion, e.g. non-rigid or discontinuous motion patterns in the cardiac region, and for cross-subject anatomical matching, SSL-based tracking showed markedly superior performance. CONCLUSION We conclude that automated tracking of anatomical structures on time-resolved medical image data with minimal human labeling effort is feasible using SSL and can provide superior results compared to conventional and deep learning OF-based methods.
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Affiliation(s)
- Marcel Frueh
- University Hospital Tuebingen, Department of Radiology, University of Tuebingen, Hoppe-Seyler-Straße 3 Tuebingen 72076, Germany; University of Tuebingen, Institute for Visual Computing, Department of Computer Science, Sand 14 Tuebingen 72076, Germany
| | - Thomas Kuestner
- University Hospital Tuebingen, Department of Radiology, University of Tuebingen, Hoppe-Seyler-Straße 3 Tuebingen 72076, Germany
| | - Marcel Nachbar
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tuebingen, Hoppe-Seyler-Straße 3 Tuebingen 72076, Germany
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tuebingen, Hoppe-Seyler-Straße 3 Tuebingen 72076, Germany
| | - Andreas Schilling
- University of Tuebingen, Institute for Visual Computing, Department of Computer Science, Sand 14 Tuebingen 72076, Germany
| | - Sergios Gatidis
- University Hospital Tuebingen, Department of Radiology, University of Tuebingen, Hoppe-Seyler-Straße 3 Tuebingen 72076, Germany; Max Planck Institute for Intelligent Systems, Empirical Inference Department, Max-Planck-Ring 4 Tuebingen 72076, Germany.
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Vergalasova I, Cai J. A modern review of the uncertainties in volumetric imaging of respiratory-induced target motion in lung radiotherapy. Med Phys 2020; 47:e988-e1008. [PMID: 32506452 DOI: 10.1002/mp.14312] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 05/15/2020] [Accepted: 05/26/2020] [Indexed: 12/25/2022] Open
Abstract
Radiotherapy has become a critical component for the treatment of all stages and types of lung cancer, often times being the primary gateway to a cure. However, given that radiation can cause harmful side effects depending on how much surrounding healthy tissue is exposed, treatment of the lung can be particularly challenging due to the presence of moving targets. Careful implementation of every step in the radiotherapy process is absolutely integral for attaining optimal clinical outcomes. With the advent and now widespread use of stereotactic body radiation therapy (SBRT), where extremely large doses are delivered, accurate, and precise dose targeting is especially vital to achieve an optimal risk to benefit ratio. This has largely become possible due to the rapid development of image-guided technology. Although imaging is critical to the success of radiotherapy, it can often be plagued with uncertainties due to respiratory-induced target motion. There has and continues to be an immense research effort aimed at acknowledging and addressing these uncertainties to further our abilities to more precisely target radiation treatment. Thus, the goal of this article is to provide a detailed review of the prevailing uncertainties that remain to be investigated across the different imaging modalities, as well as to highlight the more modern solutions to imaging motion and their role in addressing the current challenges.
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Affiliation(s)
- Irina Vergalasova
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, USA
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
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Kurz C, Buizza G, Landry G, Kamp F, Rabe M, Paganelli C, Baroni G, Reiner M, Keall PJ, van den Berg CAT, Riboldi M. Medical physics challenges in clinical MR-guided radiotherapy. Radiat Oncol 2020; 15:93. [PMID: 32370788 PMCID: PMC7201982 DOI: 10.1186/s13014-020-01524-4] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 03/24/2020] [Indexed: 12/18/2022] Open
Abstract
The integration of magnetic resonance imaging (MRI) for guidance in external beam radiotherapy has faced significant research and development efforts in recent years. The current availability of linear accelerators with an embedded MRI unit, providing volumetric imaging at excellent soft tissue contrast, is expected to provide novel possibilities in the implementation of image-guided adaptive radiotherapy (IGART) protocols. This study reviews open medical physics issues in MR-guided radiotherapy (MRgRT) implementation, with a focus on current approaches and on the potential for innovation in IGART.Daily imaging in MRgRT provides the ability to visualize the static anatomy, to capture internal tumor motion and to extract quantitative image features for treatment verification and monitoring. Those capabilities enable the use of treatment adaptation, with potential benefits in terms of personalized medicine. The use of online MRI requires dedicated efforts to perform accurate dose measurements and calculations, due to the presence of magnetic fields. Likewise, MRgRT requires dedicated quality assurance (QA) protocols for safe clinical implementation.Reaction to anatomical changes in MRgRT, as visualized on daily images, demands for treatment adaptation concepts, with stringent requirements in terms of fast and accurate validation before the treatment fraction can be delivered. This entails specific challenges in terms of treatment workflow optimization, QA, and verification of the expected delivered dose while the patient is in treatment position. Those challenges require specialized medical physics developments towards the aim of fully exploiting MRI capabilities. Conversely, the use of MRgRT allows for higher confidence in tumor targeting and organs-at-risk (OAR) sparing.The systematic use of MRgRT brings the possibility of leveraging IGART methods for the optimization of tumor targeting and quantitative treatment verification. Although several challenges exist, the intrinsic benefits of MRgRT will provide a deeper understanding of dose delivery effects on an individual basis, with the potential for further treatment personalization.
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Affiliation(s)
- Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748, Garching, Germany
| | - Giulia Buizza
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.za Leonardo da Vinci 32, 20133, Milano, Italy
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748, Garching, Germany
- German Cancer Consortium (DKTK), 81377, Munich, Germany
| | - Florian Kamp
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Moritz Rabe
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.za Leonardo da Vinci 32, 20133, Milano, Italy
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.za Leonardo da Vinci 32, 20133, Milano, Italy
- Bioengineering Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Privata Campeggi 53, 27100, Pavia, Italy
| | - Michael Reiner
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Paul J Keall
- ACRF Image X Institute, University of Sydney, Sydney, NSW, 2006, Australia
| | - Cornelis A T van den Berg
- Department of Radiotherapy, University Medical Centre Utrecht, PO box 85500, 3508 GA, Utrecht, The Netherlands
| | - Marco Riboldi
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748, Garching, Germany.
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Knowles BR, Friedrich F, Fischer C, Paech D, Ladd ME. Beyond T2 and 3T: New MRI techniques for clinicians. Clin Transl Radiat Oncol 2019; 18:87-97. [PMID: 31341982 PMCID: PMC6630188 DOI: 10.1016/j.ctro.2019.04.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 04/11/2019] [Accepted: 04/11/2019] [Indexed: 12/12/2022] Open
Abstract
Technological advances in Magnetic Resonance Imaging (MRI) in terms of field strength and hybrid MR systems have led to improvements in tumor imaging in terms of anatomy and functionality. This review paper discusses the applications of such advances in the field of radiation oncology with regards to treatment planning, therapy guidance and monitoring tumor response and predicting outcome.
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Affiliation(s)
- Benjamin R. Knowles
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Florian Friedrich
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany
| | - Carola Fischer
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany
| | - Daniel Paech
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mark E. Ladd
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany
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Bertholet J, Knopf A, Eiben B, McClelland J, Grimwood A, Harris E, Menten M, Poulsen P, Nguyen DT, Keall P, Oelfke U. Real-time intrafraction motion monitoring in external beam radiotherapy. Phys Med Biol 2019; 64:15TR01. [PMID: 31226704 PMCID: PMC7655120 DOI: 10.1088/1361-6560/ab2ba8] [Citation(s) in RCA: 111] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 05/10/2019] [Accepted: 06/21/2019] [Indexed: 12/25/2022]
Abstract
Radiotherapy (RT) aims to deliver a spatially conformal dose of radiation to tumours while maximizing the dose sparing to healthy tissues. However, the internal patient anatomy is constantly moving due to respiratory, cardiac, gastrointestinal and urinary activity. The long term goal of the RT community to 'see what we treat, as we treat' and to act on this information instantaneously has resulted in rapid technological innovation. Specialized treatment machines, such as robotic or gimbal-steered linear accelerators (linac) with in-room imaging suites, have been developed specifically for real-time treatment adaptation. Additional equipment, such as stereoscopic kilovoltage (kV) imaging, ultrasound transducers and electromagnetic transponders, has been developed for intrafraction motion monitoring on conventional linacs. Magnetic resonance imaging (MRI) has been integrated with cobalt treatment units and more recently with linacs. In addition to hardware innovation, software development has played a substantial role in the development of motion monitoring methods based on respiratory motion surrogates and planar kV or Megavoltage (MV) imaging that is available on standard equipped linacs. In this paper, we review and compare the different intrafraction motion monitoring methods proposed in the literature and demonstrated in real-time on clinical data as well as their possible future developments. We then discuss general considerations on validation and quality assurance for clinical implementation. Besides photon RT, particle therapy is increasingly used to treat moving targets. However, transferring motion monitoring technologies from linacs to particle beam lines presents substantial challenges. Lessons learned from the implementation of real-time intrafraction monitoring for photon RT will be used as a basis to discuss the implementation of these methods for particle RT.
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Affiliation(s)
- Jenny Bertholet
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS
Foundation Trust, London, United
Kingdom
- Author to whom any correspondence should be
addressed
| | - Antje Knopf
- Department of Radiation Oncology,
University Medical Center
Groningen, University of Groningen, The
Netherlands
| | - Björn Eiben
- Department of Medical Physics and Biomedical
Engineering, Centre for Medical Image Computing, University College London, London,
United Kingdom
| | - Jamie McClelland
- Department of Medical Physics and Biomedical
Engineering, Centre for Medical Image Computing, University College London, London,
United Kingdom
| | - Alexander Grimwood
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS
Foundation Trust, London, United
Kingdom
| | - Emma Harris
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS
Foundation Trust, London, United
Kingdom
| | - Martin Menten
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS
Foundation Trust, London, United
Kingdom
| | - Per Poulsen
- Department of Oncology, Aarhus University Hospital, Aarhus,
Denmark
| | - Doan Trang Nguyen
- ACRF Image X Institute, University of Sydney, Sydney,
Australia
- School of Biomedical Engineering,
University of Technology
Sydney, Sydney, Australia
| | - Paul Keall
- ACRF Image X Institute, University of Sydney, Sydney,
Australia
| | - Uwe Oelfke
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS
Foundation Trust, London, United
Kingdom
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