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Dong Y, Yang F, Wen J, Cai J, Zeng F, Liu M, Li S, Wang J, Ford JC, Portelance L, Yang Y. Improvement of 2D cine image quality using 3D priors and cycle generative adversarial network for low field MRI-guided radiation therapy. Med Phys 2024; 51:3495-3509. [PMID: 38043123 DOI: 10.1002/mp.16860] [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: 07/04/2023] [Revised: 10/12/2023] [Accepted: 11/05/2023] [Indexed: 12/05/2023] Open
Abstract
BACKGROUND Cine magnetic resonance (MR) images have been used for real-time MR guided radiation therapy (MRgRT). However, the onboard MR systems with low-field strength face the problem of limited image quality. PURPOSE To improve the quality of cine MR images in MRgRT using prior image information provided by the patient planning and positioning MR images. METHODS This study employed MR images from 18 pancreatic cancer patients who received MR-guided stereotactic body radiation therapy. Planning 3D MR images were acquired during the patient simulation, and positioning 3D MR images and 2D sagittal cine MR images were acquired before and during the beam delivery, respectively. A deep learning-based framework consisting of two cycle generative adversarial networks (CycleGAN), Denoising CycleGAN and Enhancement CycleGAN, was developed to establish the mapping between the 3D and 2D MR images. The Denoising CycleGAN was trained to first denoise the cine images using the time domain cine image series, and the Enhancement CycleGAN was trained to enhance the spatial resolution and contrast by taking advantage of the prior image information from the planning and positioning images. The denoising performance was assessed by signal-to-noise ratio (SNR), structural similarity index measure, peak SNR, blind/reference-less image spatial quality evaluator (BRISQUE), natural image quality evaluator, and perception-based image quality evaluator scores. The quality enhancement performance was assessed by the BRISQUE and physician visual scores. In addition, the target contouring was evaluated on the original and processed images. RESULTS Significant differences were found for all evaluation metrics after Denoising CycleGAN processing. The BRISQUE and visual scores were also significantly improved after sequential Denoising and Enhancement CycleGAN processing. In target contouring evaluation, Dice similarity coefficient, centroid distance, Hausdorff distance, and average surface distance values were significantly improved on the enhanced images. The whole processing time was within 20 ms for a typical input image size of 512 × 512. CONCLUSION Taking advantage of the prior high-quality positioning and planning MR images, the deep learning-based framework enhanced the cine MR image quality significantly, leading to improved accuracy in automatic target contouring. With the merits of both high computational efficiency and considerable image quality enhancement, the proposed method may hold important clinical implication for real-time MRgRT.
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Affiliation(s)
- Yuyan Dong
- Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, China
| | - Fei Yang
- The Miller School of Medicine, University of Miami, Miami, Florida, USA
| | - Jie Wen
- Department of Radiology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
| | - Feiyan Zeng
- Department of Radiology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Mengqiu Liu
- Department of Radiology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Shuang Li
- Department of Radiology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Jiangtao Wang
- Cancer Center, Sichuan Academy of Medical Sciences Sichuan Provincial People's Hospital, Chengdu, Sichuan, China
| | - John Chetley Ford
- The Miller School of Medicine, University of Miami, Miami, Florida, USA
| | | | - Yidong Yang
- Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, China
- Department of Radiation Oncology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
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Lombardo E, Dhont J, Page D, Garibaldi C, Künzel LA, Hurkmans C, Tijssen RHN, Paganelli C, Liu PZY, Keall PJ, Riboldi M, Kurz C, Landry G, Cusumano D, Fusella M, Placidi L. Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects. Radiother Oncol 2024; 190:109970. [PMID: 37898437 DOI: 10.1016/j.radonc.2023.109970] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/19/2023] [Accepted: 10/22/2023] [Indexed: 10/30/2023]
Abstract
MRI-guided radiotherapy (MRIgRT) is a highly complex treatment modality, allowing adaptation to anatomical changes occurring from one treatment day to the other (inter-fractional), but also to motion occurring during a treatment fraction (intra-fractional). In this vision paper, we describe the different steps of intra-fractional motion management during MRIgRT, from imaging to beam adaptation, and the solutions currently available both clinically and at a research level. Furthermore, considering the latest developments in the literature, a workflow is foreseen in which motion-induced over- and/or under-dosage is compensated in 3D, with minimal impact to the radiotherapy treatment time. Considering the time constraints of real-time adaptation, a particular focus is put on artificial intelligence (AI) solutions as a fast and accurate alternative to conventional algorithms.
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Affiliation(s)
- Elia Lombardo
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Jennifer Dhont
- Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B), Institut Jules Bordet, Department of Medical Physics, Brussels, Belgium; Université Libre De Bruxelles (ULB), Radiophysics and MRI Physics Laboratory, Brussels, Belgium
| | - Denis Page
- University of Manchester, Division of Cancer Sciences, Manchester, United Kingdom
| | - Cristina Garibaldi
- IEO, Unit of Radiation Research, European Institute of Oncology IRCCS, Milan, Italy
| | - Luise A Künzel
- National Center for Tumor Diseases (NCT), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; 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
| | - Coen Hurkmans
- Department of Radiation Oncology, Catharina Hospital, Eindhoven, the Netherlands
| | - Rob H N Tijssen
- Department of Radiation Oncology, Catharina Hospital, Eindhoven, the Netherlands
| | - Chiara Paganelli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
| | - Paul Z Y Liu
- Image X Institute, University of Sydney Central Clinical School, Sydney, NSW, Australia; Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, NSW, Australia
| | - Paul J Keall
- Image X Institute, University of Sydney Central Clinical School, Sydney, NSW, Australia; Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, NSW, Australia
| | - Marco Riboldi
- Department of Medical Physics, Faculty of Physics, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Guillaume Landry
- 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), Partner Site Munich, Munich, Germany
| | | | - Marco Fusella
- Department of Radiation Oncology, Abano Terme Hospital, Italy.
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Roma, Italy
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Hunt B, Gill GS, Alexander DA, Streeter SS, Gladstone DJ, Russo GA, Zaki BI, Pogue BW, Zhang R. Fast Deformable Image Registration for Real-Time Target Tracking During Radiation Therapy Using Cine MRI and Deep Learning. Int J Radiat Oncol Biol Phys 2023; 115:983-993. [PMID: 36309075 DOI: 10.1016/j.ijrobp.2022.09.086] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 08/10/2022] [Accepted: 09/07/2022] [Indexed: 11/07/2022]
Abstract
PURPOSE We developed a deep learning (DL) model for fast deformable image registration using 2-dimensional sagittal cine magnetic resonance imaging (MRI) acquired during radiation therapy and evaluated its potential for real-time target tracking compared with conventional image registration methods. METHODS AND MATERIALS Our DL model uses a pair of cine MRI images as input and provides a motion vector field (MVF) as output. The MVF is then applied to align the input images. A retrospective study was conducted to train and evaluate our model using cine MRI data from patients undergoing treatment for abdominal and thoracic tumors. For each treatment fraction, MR-linear accelerator delivery log files, tracking videos, and cine image files were analyzed. Individual MRI frames were temporally sampled to construct a large set of image registration pairs used to evaluate multiple methods. The DL model was optimized using 5-fold cross validation, and model outputs (transformed images and MVFs) using test set images were saved for comparison with 3 conventional registration methods (affine, b-spline, and demons). Evaluation metrics were 3-fold: (1) registration error, (2) MVF stability (both spatial and temporal), and (3) average computation time. RESULTS We analyzed >21 hours of cine MRI (>629,000 frames) acquired during 86 treatment fractions from 21 patients. In a test set of 10,320 image registration pairs, DL registration outperformed conventional methods in both registration error (affine, b-spline, demons, DL; root mean square error: 0.067, 0.040, 0.036, 0.032; paired t test demons vs DL: t[20] = 4.2, P < .001) and computation time per frame (51, 1150, 4583, 8 ms). Among deformable methods, spatial stability of resulting MVFs was comparable; however, the DL model had significantly improved temporal consistency. CONCLUSIONS DL-based image registration can leverage large-scale MR cine data sets to outperform conventional registration methods and is a promising solution for real-time deformable motion estimation in radiation therapy.
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Affiliation(s)
- Brady Hunt
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire; Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire; Dartmouth Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire.
| | - Gobind S Gill
- Dartmouth Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire
| | | | - Samuel S Streeter
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire
| | - David J Gladstone
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire; Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire; Dartmouth Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire
| | - Gregory A Russo
- Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire; Dartmouth Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire
| | - Bassem I Zaki
- Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire; Dartmouth Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire
| | - Brian W Pogue
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Rongxiao Zhang
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire; Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire; Dartmouth Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire
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Gaga R. Editorial for "Super-Resolution Cine Image Enhancement for Fetal Cardiovascular Magnetic Resonance Imaging". J Magn Reson Imaging 2021; 56:232-233. [PMID: 34738688 DOI: 10.1002/jmri.27985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 10/25/2021] [Indexed: 11/06/2022] Open
Affiliation(s)
- Remus Gaga
- 2 Pediatric Clinic, Mother and Child Department, University of Medicine and Pharmacy Iuliu Haţieganu Cluj-Napoca, Emergency Clinical Hospital for Children, Cluj-Napoca, Romania
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