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Cheng SH, Lee SY, Lee HH. Harnessing the Power of Radiotherapy for Lung Cancer: A Narrative Review of the Evolving Role of Magnetic Resonance Imaging Guidance. Cancers (Basel) 2024; 16:2710. [PMID: 39123438 PMCID: PMC11311467 DOI: 10.3390/cancers16152710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2024] [Revised: 07/22/2024] [Accepted: 07/29/2024] [Indexed: 08/12/2024] Open
Abstract
Compared with computed tomography (CT), magnetic resonance imaging (MRI) traditionally plays a very limited role in lung cancer management, although there is plenty of room for improvement in the current CT-based workflow, for example, in structures such as the brachial plexus and chest wall invasion, which are difficult to visualize with CT alone. Furthermore, in the treatment of high-risk tumors such as ultracentral lung cancer, treatment-associated toxicity currently still outweighs its benefits. The advent of MR-Linac, an MRI-guided radiotherapy (RT) that combines MRI with a linear accelerator, could potentially address these limitations. Compared with CT-based technologies, MR-Linac could offer superior soft tissue visualization, daily adaptive capability, real-time target tracking, and an early assessment of treatment response. Clinically, it could be especially advantageous in the treatment of central/ultracentral lung cancer, early-stage lung cancer, and locally advanced lung cancer. Increasing demands for stereotactic body radiotherapy (SBRT) for lung cancer have led to MR-Linac adoption in some cancer centers. In this review, a broad overview of the latest research on imaging-guided radiotherapy (IGRT) with MR-Linac for lung cancer management is provided, and development pertaining to artificial intelligence is also highlighted. New avenues of research are also discussed.
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Affiliation(s)
- Sarah Hsin Cheng
- Department of Clinical Education and Training, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan;
| | - Shao-Yun Lee
- Department of Medical Education, Taichung Veterans General Hospital, Taichung 407, Taiwan;
| | - Hsin-Hua Lee
- Department of Radiation Oncology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Ph.D. Program in Environmental and Occupational Medicine, Kaohsiung Medical University and National Health Research Institutes, Kaohsiung 807, Taiwan
- Department of Radiation Oncology, Faculty of Medicine, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Center for Cancer Research, Kaohsiung Medical University, Kaohsiung 807, Taiwan
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2
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Rabe M, Paganelli C, Schmitz H, Meschini G, Riboldi M, Hofmaier J, Nierer-Kohlhase L, Dinkel J, Reiner M, Parodi K, Belka C, Landry G, Kurz C, Kamp F. Continuous time-resolved estimated synthetic 4D-CTs for dose reconstruction of lung tumor treatments at a 0.35 T MR-linac. Phys Med Biol 2023; 68:235008. [PMID: 37669669 DOI: 10.1088/1361-6560/acf6f0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 09/05/2023] [Indexed: 09/07/2023]
Abstract
Objective.To experimentally validate a method to create continuous time-resolved estimated synthetic 4D-computed tomography datasets (tresCTs) based on orthogonal cine MRI data for lung cancer treatments at a magnetic resonance imaging (MRI) guided linear accelerator (MR-linac).Approach.A breathing porcine lung phantom was scanned at a CT scanner and 0.35 T MR-linac. Orthogonal cine MRI series (sagittal/coronal orientation) at 7.3 Hz, intersecting tumor-mimicking gelatin nodules, were deformably registered to mid-exhale 3D-CT and 3D-MRI datasets. The time-resolved deformation vector fields were extrapolated to 3D and applied to a reference synthetic 3D-CT image (sCTref), while accounting for breathing phase-dependent lung density variations, to create 82 s long tresCTs at 3.65 Hz. Ten tresCTs were created for ten tracked nodules with different motion patterns in two lungs. For each dataset, a treatment plan was created on the mid-exhale phase of a measured ground truth (GT) respiratory-correlated 4D-CT dataset with the tracked nodule as gross tumor volume (GTV). Each plan was recalculated on the GT 4D-CT, randomly sampled tresCT, and static sCTrefimages. Dose distributions for corresponding breathing phases were compared in gamma (2%/2 mm) and dose-volume histogram (DVH) parameter analyses.Main results.The mean gamma pass rate between all tresCT and GT 4D-CT dose distributions was 98.6%. The mean absolute relative deviations of the tresCT with respect to GT DVH parameters were 1.9%, 1.0%, and 1.4% for the GTVD98%,D50%, andD2%, respectively, 1.0% for the remaining nodulesD50%, and 1.5% for the lungV20Gy. The gamma pass rate for the tresCTs was significantly larger (p< 0.01), and the GTVD50%deviations with respect to the GT were significantly smaller (p< 0.01) than for the sCTref.Significance.The results suggest that tresCTs could be valuable for time-resolved reconstruction and intrafractional accumulation of the dose to the GTV for lung cancer patients treated at MR-linacs in the future.
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Affiliation(s)
- Moritz Rabe
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Chiara Paganelli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
| | - Henning Schmitz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Giorgia Meschini
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
| | - Marco Riboldi
- Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching (Munich), Germany
| | - Jan Hofmaier
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Lukas Nierer-Kohlhase
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Julien Dinkel
- Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany
- Comprehensive Pneumology Center, German Center for Lung Research (DZL), Munich, Germany
| | - Michael Reiner
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Katia Parodi
- Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching (Munich), Germany
| | - Claus Belka
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and LMU University Hospital Munich, Germany
- Bavarian Cancer Research Center (BZKF), Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Florian Kamp
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- Department of Radiation Oncology, University Hospital Cologne, Cologne, Germany
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Dal Bello R, Lapaeva M, La Greca Saint-Esteven A, Wallimann P, Günther M, Konukoglu E, Andratschke N, Guckenberger M, Tanadini-Lang S. Patient-specific quality assurance strategies for synthetic computed tomography in magnetic resonance-only radiotherapy of the abdomen. Phys Imaging Radiat Oncol 2023; 27:100464. [PMID: 37497188 PMCID: PMC10366576 DOI: 10.1016/j.phro.2023.100464] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 06/21/2023] [Accepted: 06/22/2023] [Indexed: 07/28/2023] Open
Abstract
Background and purpose The superior tissue contrast of magnetic resonance (MR) compared to computed tomography (CT) led to an increasing interest towards MR-only radiotherapy. For the latter, the dose calculation should be performed on a synthetic CT (sCT). Patient-specific quality assurance (PSQA) methods have not been established yet and this study aimed to assess several software-based solutions. Materials and methods A retrospective study was performed on 20 patients treated at an MR-Linac, which were selected to evenly cover four subcategories: (i) standard, (ii) air pockets, (iii) lung and (iv) implant cases. The neural network (NN) CycleGAN was adopted to generate a reference sCT, which was then compared to four PSQA methods: (A) water override of body, (B) five tissue classes with bulk densities, (C) sCT generated by a separate NN (pix2pix) and (D) deformed CT. Results The evaluation of the dose endpoints demonstrated that while all methods A-D provided statistically equivalent results (p = 0.05) within the 2% level for the standard cases (i), only the methods C-D guaranteed the same result over the whole cohort. The bulk densities override was shown to be a valuable method in absence of lung tissue within the beam path. Conclusion The observations of this study suggested that the use of an additional sCT generated by a separate NN was an appropriate tool to perform PSQA of a sCT in an MR-only workflow at an MR-Linac. The time and dose endpoints requirements were respected, namely within 10 min and 2%.
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Affiliation(s)
- Riccardo Dal Bello
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Mariia Lapaeva
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
- Artificial Intelligence and Machine Learning Group, Department of Informatics, University of Zurich, Zurich, Switzerland
- Computer Vision Laboratory, ETH Zurich, Zurich, Switzerland
| | - Agustina La Greca Saint-Esteven
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
- Computer Vision Laboratory, ETH Zurich, Zurich, Switzerland
| | - Philipp Wallimann
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Manuel Günther
- Artificial Intelligence and Machine Learning Group, Department of Informatics, University of Zurich, Zurich, Switzerland
| | | | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
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4
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Wang H, Liu X, Kong L, Huang Y, Chen H, Ma X, Duan Y, Shao Y, Feng A, Shen Z, Gu H, Kong Q, Xu Z, Zhou Y. Improving CBCT image quality to the CT level using RegGAN in esophageal cancer adaptive radiotherapy. Strahlenther Onkol 2023; 199:485-497. [PMID: 36688953 PMCID: PMC10133081 DOI: 10.1007/s00066-022-02039-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/04/2022] [Indexed: 01/24/2023]
Abstract
OBJECTIVE This study aimed to improve the image quality and CT Hounsfield unit accuracy of daily cone-beam computed tomography (CBCT) using registration generative adversarial networks (RegGAN) and apply synthetic CT (sCT) images to dose calculations in radiotherapy. METHODS The CBCT/planning CT images of 150 esophageal cancer patients undergoing radiotherapy were used for training (120 patients) and testing (30 patients). An unsupervised deep-learning method, the 2.5D RegGAN model with an adaptively trained registration network, was proposed, through which sCT images were generated. The quality of deep-learning-generated sCT images was quantitatively compared to the reference deformed CT (dCT) image using mean absolute error (MAE), root mean square error (RMSE) of Hounsfield units (HU), and peak signal-to-noise ratio (PSNR). The dose calculation accuracy was further evaluated for esophageal cancer radiotherapy plans, and the same plans were calculated on dCT, CBCT, and sCT images. RESULTS The quality of sCT images produced by RegGAN was significantly improved compared to the original CBCT images. ReGAN achieved image quality in the testing patients with MAE sCT vs. CBCT: 43.7 ± 4.8 vs. 80.1 ± 9.1; RMSE sCT vs. CBCT: 67.2 ± 12.4 vs. 124.2 ± 21.8; and PSNR sCT vs. CBCT: 27.9 ± 5.6 vs. 21.3 ± 4.2. The sCT images generated by the RegGAN model showed superior accuracy on dose calculation, with higher gamma passing rates (93.3 ± 4.4, 90.4 ± 5.2, and 84.3 ± 6.6) compared to original CBCT images (89.6 ± 5.7, 85.7 ± 6.9, and 72.5 ± 12.5) under the criteria of 3 mm/3%, 2 mm/2%, and 1 mm/1%, respectively. CONCLUSION The proposed deep-learning RegGAN model seems promising for generation of high-quality sCT images from stand-alone thoracic CBCT images in an efficient way and thus has the potential to support CBCT-based esophageal cancer adaptive radiotherapy.
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Affiliation(s)
- Hao Wang
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China.,Institute of Modern Physics, Fudan University, Shanghai, China
| | - Xiao Liu
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | | | - Ying Huang
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Hua Chen
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Xiurui Ma
- Department of Radiation Oncology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yanhua Duan
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Yan Shao
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Aihui Feng
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Zhenjiong Shen
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Hengle Gu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Qing Kong
- Institute of Modern Physics, Fudan University, Shanghai, China
| | - Zhiyong Xu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Yongkang Zhou
- Department of Radiation Oncology, Zhongshan Hospital, Fudan University, Shanghai, China.
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5
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Goodburn RJ, Philippens MEP, Lefebvre TL, Khalifa A, Bruijnen T, Freedman JN, Waddington DEJ, Younus E, Aliotta E, Meliadò G, Stanescu T, Bano W, Fatemi‐Ardekani A, Wetscherek A, Oelfke U, van den Berg N, Mason RP, van Houdt PJ, Balter JM, Gurney‐Champion OJ. The future of MRI in radiation therapy: Challenges and opportunities for the MR community. Magn Reson Med 2022; 88:2592-2608. [PMID: 36128894 PMCID: PMC9529952 DOI: 10.1002/mrm.29450] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 08/17/2022] [Accepted: 08/22/2022] [Indexed: 01/11/2023]
Abstract
Radiation therapy is a major component of cancer treatment pathways worldwide. The main aim of this treatment is to achieve tumor control through the delivery of ionizing radiation while preserving healthy tissues for minimal radiation toxicity. Because radiation therapy relies on accurate localization of the target and surrounding tissues, imaging plays a crucial role throughout the treatment chain. In the treatment planning phase, radiological images are essential for defining target volumes and organs-at-risk, as well as providing elemental composition (e.g., electron density) information for radiation dose calculations. At treatment, onboard imaging informs patient setup and could be used to guide radiation dose placement for sites affected by motion. Imaging is also an important tool for treatment response assessment and treatment plan adaptation. MRI, with its excellent soft tissue contrast and capacity to probe functional tissue properties, holds great untapped potential for transforming treatment paradigms in radiation therapy. The MR in Radiation Therapy ISMRM Study Group was established to provide a forum within the MR community to discuss the unmet needs and fuel opportunities for further advancement of MRI for radiation therapy applications. During the summer of 2021, the study group organized its first virtual workshop, attended by a diverse international group of clinicians, scientists, and clinical physicists, to explore our predictions for the future of MRI in radiation therapy for the next 25 years. This article reviews the main findings from the event and considers the opportunities and challenges of reaching our vision for the future in this expanding field.
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Affiliation(s)
- Rosie J. Goodburn
- Joint Department of PhysicsInstitute of Cancer Research and Royal Marsden NHS Foundation TrustLondonUnited Kingdom
| | | | - Thierry L. Lefebvre
- Department of PhysicsUniversity of CambridgeCambridgeUnited Kingdom
- Cancer Research UK Cambridge Research InstituteUniversity of CambridgeCambridgeUnited Kingdom
| | - Aly Khalifa
- Department of Medical BiophysicsUniversity of TorontoTorontoOntarioCanada
| | - Tom Bruijnen
- Department of RadiotherapyUniversity Medical Center UtrechtUtrechtNetherlands
| | | | - David E. J. Waddington
- Faculty of Medicine and Health, Sydney School of Health Sciences, ACRF Image X InstituteThe University of SydneySydneyNew South WalesAustralia
| | - Eyesha Younus
- Department of Medical Physics, Odette Cancer CentreSunnybrook Health Sciences CentreTorontoOntarioCanada
| | - Eric Aliotta
- Department of Medical PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - Gabriele Meliadò
- Unità Operativa Complessa di Fisica SanitariaAzienda Ospedaliera Universitaria Integrata VeronaVeronaItaly
| | - Teo Stanescu
- Department of Radiation Oncology, University of Toronto and Medical Physics, Princess Margaret Cancer CentreUniversity Health NetworkTorontoOntarioCanada
| | - Wajiha Bano
- Joint Department of PhysicsInstitute of Cancer Research and Royal Marsden NHS Foundation TrustLondonUnited Kingdom
| | - Ali Fatemi‐Ardekani
- Department of PhysicsJackson State University (JSU)JacksonMississippiUSA
- SpinTecxJacksonMississippiUSA
- Department of Radiation OncologyCommunity Health Systems (CHS) Cancer NetworkJacksonMississippiUSA
| | - Andreas Wetscherek
- Joint Department of PhysicsInstitute of Cancer Research and Royal Marsden NHS Foundation TrustLondonUnited Kingdom
| | - Uwe Oelfke
- Joint Department of PhysicsInstitute of Cancer Research and Royal Marsden NHS Foundation TrustLondonUnited Kingdom
| | - Nico van den Berg
- Department of RadiotherapyUniversity Medical Center UtrechtUtrechtNetherlands
| | - Ralph P. Mason
- Department of RadiologyUniversity of Texas Southwestern Medical CenterDallasTexasUSA
| | - Petra J. van Houdt
- Department of Radiation OncologyNetherlands Cancer InstituteAmsterdamNetherlands
| | - James M. Balter
- Department of Radiation OncologyUniversity of MichiganAnn ArborMichiganUSA
| | - Oliver J. Gurney‐Champion
- Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam UMCUniversity of AmsterdamAmsterdamNetherlands
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Gough J, Hall W, Good J, Nash A, Aitken K. Technical Radiotherapy Advances – The Role of Magnetic Resonance Imaging-Guided Radiation in the Delivery of Hypofractionation. Clin Oncol (R Coll Radiol) 2022; 34:301-312. [DOI: 10.1016/j.clon.2022.02.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 02/07/2022] [Accepted: 02/23/2022] [Indexed: 12/30/2022]
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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: 82] [Impact Index Per Article: 27.3] [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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8
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Freedman JN, Gurney-Champion OJ, Nill S, Shiarli AM, Bainbridge HE, Mandeville HC, Koh DM, McDonald F, Kachelrieß M, Oelfke U, Wetscherek A. Rapid 4D-MRI reconstruction using a deep radial convolutional neural network: Dracula. Radiother Oncol 2021; 159:209-217. [PMID: 33812914 PMCID: PMC8216429 DOI: 10.1016/j.radonc.2021.03.034] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 03/07/2021] [Accepted: 03/26/2021] [Indexed: 11/16/2022]
Abstract
BACKGROUND AND PURPOSE 4D and midposition MRI could inform plan adaptation in lung and abdominal MR-guided radiotherapy. We present deep learning-based solutions to overcome long 4D-MRI reconstruction times while maintaining high image quality and short scan times. METHODS Two 3D U-net deep convolutional neural networks were trained to accelerate the 4D joint MoCo-HDTV reconstruction. For the first network, gridded and joint MoCo-HDTV-reconstructed 4D-MRI were used as input and target data, respectively, whereas the second network was trained to directly calculate the midposition image. For both networks, input and target data had dimensions of 256 × 256 voxels (2D) and 16 respiratory phases. Deep learning-based MRI were verified against joint MoCo-HDTV-reconstructed MRI using the structural similarity index (SSIM) and the naturalness image quality evaluator (NIQE). Moreover, two experienced observers contoured the gross tumour volume and scored the images in a blinded study. RESULTS For 12 subjects, previously unseen by the networks, high-quality 4D and midposition MRI (1.25 × 1.25 × 3.3 mm3) were each reconstructed from gridded images in only 28 seconds per subject. Excellent agreement was found between deep-learning-based and joint MoCo-HDTV-reconstructed MRI (average SSIM ≥ 0.96, NIQE scores 7.94 and 5.66). Deep-learning-based 4D-MRI were clinically acceptable for target and organ-at-risk delineation. Tumour positions agreed within 0.7 mm on midposition images. CONCLUSION Our results suggest that the joint MoCo-HDTV and midposition algorithms can each be approximated by a deep convolutional neural network. This rapid reconstruction of 4D and midposition MRI facilitates online treatment adaptation in thoracic or abdominal MR-guided radiotherapy.
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Affiliation(s)
- Joshua N Freedman
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
| | - Oliver J Gurney-Champion
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, The Netherlands.
| | - Simeon Nill
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
| | - Anna-Maria Shiarli
- Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
| | - Hannah E Bainbridge
- Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; Department of Radiotherapy, Portsmouth Hospitals University NHS Trust, Queen Alexandra Hospital, United Kingdom.
| | - Henry C Mandeville
- Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
| | - Dow-Mu Koh
- Department of Radiology, The Royal Marsden NHS Foundation Trust, London, United Kingdom.
| | - Fiona McDonald
- Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
| | - Marc Kachelrieß
- Division of X-Ray Imaging and CT, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Uwe Oelfke
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
| | - Andreas Wetscherek
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
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9
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Groot Koerkamp ML, de Hond YJM, Maspero M, Kontaxis C, Mandija S, Vasmel JE, Charaghvandi RK, Philippens MEP, van Asselen B, van den Bongard HJGD, Hackett SS, Houweling AC. Synthetic CT for single-fraction neoadjuvant partial breast irradiation on an MRI-linac. Phys Med Biol 2021; 66. [PMID: 33761491 DOI: 10.1088/1361-6560/abf1ba] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/24/2021] [Indexed: 01/08/2023]
Abstract
A synthetic computed tomography (sCT) is required for daily plan optimization on an MRI-linac. Yet, only limited information is available on the accuracy of dose calculations on sCT for breast radiotherapy. This work aimed to (1) evaluate dosimetric accuracy of treatment plans for single-fraction neoadjuvant partial breast irradiation (PBI) on a 1.5 T MRI-linac calculated on a) bulk-density sCT mimicking the current MRI-linac workflow and b) deep learning-generated sCT, and (2) investigate the number of bulk-density levels required. For ten breast cancer patients we created three bulk-density sCTs of increasing complexity from the planning-CT, using bulk-density for: (1) body, lungs, and GTV (sCTBD1); (2) volumes for sCTBD1plus chest wall and ipsilateral breast (sCTBD2); (3) volumes for sCTBD2plus ribs (sCTBD3); and a deep learning-generated sCT (sCTDL) from a 1.5 T MRI in supine position. Single-fraction neoadjuvant PBI treatment plans for a 1.5 T MRI-linac were optimized on each sCT and recalculated on the planning-CT. Image evaluation was performed by assessing mean absolute error (MAE) and mean error (ME) in Hounsfield Units (HU) between the sCTs and the planning-CT. Dosimetric evaluation was performed by assessing dose differences, gamma pass rates, and dose-volume histogram (DVH) differences. The following results were obtained (median across patients for sCTBD1/sCTBD2/sCTBD3/sCTDLrespectively): MAE inside the body contour was 106/104/104/75 HU and ME was 8/9/6/28 HU, mean dose difference in the PTVGTVwas 0.15/0.00/0.00/-0.07 Gy, median gamma pass rate (2%/2 mm, 10% dose threshold) was 98.9/98.9/98.7/99.4%, and differences in DVH parameters were well below 2% for all structures except for the skin in the sCTDL. Accurate dose calculations for single-fraction neoadjuvant PBI on an MRI-linac could be performed on both bulk-density and deep learning sCT, facilitating further implementation of MRI-guided radiotherapy for breast cancer. Balancing simplicity and accuracy, sCTBD2showed the optimal number of bulk-density levels for a bulk-density approach.
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Affiliation(s)
- M L Groot Koerkamp
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Y J M de Hond
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - M Maspero
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.,Computational Imaging Group for MR diagnostics & therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - C Kontaxis
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - S Mandija
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.,Computational Imaging Group for MR diagnostics & therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - J E Vasmel
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - R K Charaghvandi
- Department of Radiation Oncology, Radboudumc, Nijmegen, The Netherlands
| | - M E P Philippens
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - B van Asselen
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - S S Hackett
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - A C Houweling
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
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10
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Meschini G, Vai A, Paganelli C, Molinelli S, Maestri D, Fontana G, Pella A, Vitolo V, Valvo F, Ciocca M, Baroni G. Investigating the use of virtual 4DCT from 4DMRI in gated carbon ion radiation therapy of abdominal tumors. Z Med Phys 2020; 32:98-108. [PMID: 33069586 PMCID: PMC9948849 DOI: 10.1016/j.zemedi.2020.08.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 07/27/2020] [Accepted: 08/31/2020] [Indexed: 12/22/2022]
Abstract
PURPOSE To generate virtual 4DCT from 4DMRI with field of view (FOV) extended to the entire involved patient anatomy, in order to evaluate its use in carbon ion radiation therapy (CIRT) of the abdominal site in a clinical scenario. MATERIALS AND METHODS The virtual 4DCT was generated by deforming a reference CT in order to (1) match the anatomy depicted in the 4DMRI within its FOV, by calculating deformation fields with deformable image registration to describe inter-fractional and breathing motion, and (2) obtain physically plausible deformation outside of the 4DMRI FOV, by propagating and modulating the previously obtained deformation fields. The implemented method was validated on a digital anthropomorphic phantom, for which a ground truth (GT) 4DCT was available. A CIRT treatment plan was optimized at the end-exhale reference CT and the RBE-weighted dose distribution was recalculated on both the virtual and GT 4DCTs. The method estimation error was quantified by comparing the virtual and GT 4DCTs and the corresponding recomputed doses. The method was then evaluated on 8 patients with pancreas or liver tumors treated with CIRT using respiratory gating at end-exhale. The clinical treatment plans adopted at the National Center for Oncological Hadrontherapy (CNAO, Pavia, Italy) were considered and the dose distribution was recomputed on all respiratory phases of the planning and virtual 4DCTs. By comparing the two datasets and the corresponding dose distributions, the geometrical and dosimetric impact of organ motion was assessed. RESULTS For the phantom, the error outside of the 4DMRI FOV was up to 4.5mm, but it remained sub-millimetric in correspondence to the target within the 4DMRI FOV. Although the impact of motion on the target D95% resulted in variations ranging from 22% to 90% between the planned dose and the doses recomputed on the GT 4DCT phases, the corresponding estimation error was ≤2.2%. In the patient cases, the variation of the baseline tumor position between the planning and the virtual end-exhale CTs presented a median (interquartile range) value of 6.0 (4.9) mm. For baseline variations larger than 5mm, the tumor D95% variation between the plan and the dose recomputed on the end-exhale virtual CT resulted larger than 10%. Median variations higher than 10% in the target D95% and gastro-intestinal OARs D2% were quantified at the end-inhale, whereas close to the end-exhale phase, limited variations of relevant dose metrics were found for both tumor and OARs. CONCLUSIONS The negligible impact of the geometrical inaccuracy in the estimated anatomy outside of the 4DMRI FOV on the overall dosimetric accuracy suggests the feasibility of virtual 4DCT with extended FOV in CIRT of the abdominal site. In the analyzed patient group, inter-fractional variations such as baseline variation and breathing variability were quantified, demonstrating the method capability to support treatment planning in gated CIRT of the abdominal site.
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Affiliation(s)
- Giorgia Meschini
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano 20133, Italy.
| | - Alessandro Vai
- Centro Nazionale di Adroterapia Oncologica, Pavia 27100, Italy
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano 20133, Italy
| | | | - Davide Maestri
- Centro Nazionale di Adroterapia Oncologica, Pavia 27100, Italy
| | - Giulia Fontana
- Centro Nazionale di Adroterapia Oncologica, Pavia 27100, Italy
| | - Andrea Pella
- Centro Nazionale di Adroterapia Oncologica, Pavia 27100, Italy
| | - Viviana Vitolo
- Centro Nazionale di Adroterapia Oncologica, Pavia 27100, Italy
| | - Francesca Valvo
- Centro Nazionale di Adroterapia Oncologica, Pavia 27100, Italy
| | - Mario Ciocca
- Centro Nazionale di Adroterapia Oncologica, Pavia 27100, Italy
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano 20133, Italy,Centro Nazionale di Adroterapia Oncologica, Pavia 27100, Italy
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11
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Groot Koerkamp ML, Vasmel JE, Russell NS, Shaitelman SF, Anandadas CN, Currey A, Vesprini D, Keller BM, De-Colle C, Han K, Braunstein LZ, Mahmood F, Lorenzen EL, Philippens MEP, Verkooijen HM, Lagendijk JJW, Houweling AC, van den Bongard HJGD, Kirby AM. Optimizing MR-Guided Radiotherapy for Breast Cancer Patients. Front Oncol 2020; 10:1107. [PMID: 32850318 PMCID: PMC7399349 DOI: 10.3389/fonc.2020.01107] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 06/03/2020] [Indexed: 01/01/2023] Open
Abstract
Current research in radiotherapy (RT) for breast cancer is evaluating neoadjuvant as opposed to adjuvant partial breast irradiation (PBI) with the aim of reducing the volume of breast tissue irradiated and therefore the risk of late treatment-related toxicity. The development of magnetic resonance (MR)-guided RT, including dedicated MR-guided RT systems [hybrid machines combining an MR scanner with a linear accelerator (MR-linac) or 60Co sources], could potentially reduce the irradiated volume even further by improving tumour visibility before and during each RT treatment. In this position paper, we discuss MR guidance in relation to each step of the breast RT planning and treatment pathway, focusing on the application of MR-guided RT to neoadjuvant PBI.
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Affiliation(s)
| | - Jeanine E. Vasmel
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Nicola S. Russell
- Department of Radiotherapy, The Netherlands Cancer Institute–Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands
| | - Simona F. Shaitelman
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Carmel N. Anandadas
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Adam Currey
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Danny Vesprini
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Brian M. Keller
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Chiara De-Colle
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Tübingen, Germany
| | - Kathy Han
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON, Canada
| | - Lior Z. Braunstein
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Faisal Mahmood
- Department of Oncology, Odense University Hospital, Odense, Denmark
- Research Unit for Oncology, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Ebbe L. Lorenzen
- Department of Oncology, Odense University Hospital, Odense, Denmark
| | | | | | - Jan J. W. Lagendijk
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Antonetta C. Houweling
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, Netherlands
| | | | - Anna M. Kirby
- Department of Radiotherapy, Royal Marsden NHS Foundation Trust and Institute of Cancer Research, Sutton, United Kingdom
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12
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Chowdhury SR, Dutta J. Higher-order singular value decomposition-based lung parcellation for breathing motion management. J Med Imaging (Bellingham) 2019; 6:024004. [PMID: 31065568 DOI: 10.1117/1.jmi.6.2.024004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 04/04/2019] [Indexed: 11/14/2022] Open
Abstract
Positron emission tomography (PET) imaging of the lungs is confounded by respiratory motion-induced blurring artifacts that degrade quantitative accuracy. Gating and motion-compensated image reconstruction are frequently used to correct these motion artifacts in PET. In the absence of voxel-by-voxel deformation measures, surrogate signals from external markers are used to track internal motion and generate gated PET images. The objective of our work is to develop a group-level parcellation framework for the lungs to guide the placement of markers depending on the location of the internal target region. We present a data-driven framework based on higher-order singular value decomposition (HOSVD) of deformation tensors that enables identification of synchronous areas inside the torso and on the skin surface. Four-dimensional (4-D) magnetic resonance (MR) imaging based on a specialized radial pulse sequence with a one-dimensional slice-projection navigator was used for motion capture under free-breathing conditions. The deformation tensors were computed by nonrigidly registering the gated MR images. Group-level motion signatures obtained via HOSVD were used to cluster the voxels both inside the volume and on the surface. To characterize the parcellation result, we computed correlation measures across the different regions of interest (ROIs). To assess the robustness of the parcellation technique, leave-one-out cross-validation was performed over the subject cohort, and the dependence of the result on varying numbers of gates and singular value thresholds was examined. Overall, the parcellation results were largely consistent across these test cases with Jaccard indices reflecting high degrees of overlap. Finally, a PET simulation study was performed which showed that, depending on the location of the lesion, the selection of a synchronous ROI may lead to noticeable gains in the recovery coefficient. Accurate quantitative interpretation of PET images is important for lung cancer management. Therefore, a guided motion monitoring approach is of utmost importance in the context of pulmonary PET imaging.
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Affiliation(s)
- Samadrita Roy Chowdhury
- University of Massachusetts Lowell, Department of Electrical and Computer Engineering, Lowell, Massachusetts, United States
| | - Joyita Dutta
- University of Massachusetts Lowell, Department of Electrical and Computer Engineering, Lowell, Massachusetts, United States.,Massachusetts General Hospital and Harvard Medical School, Gordon Center for Medical Imaging, Boston, Massachusetts, United States
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