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Wong YL, Li T, Liu C, Lee HFV, Cheung LYA, Hui ESK, Cao P, Cai J. Reconstruction of multi-phase parametric maps in 4D-magnetic resonance fingerprinting (4D-MRF) by optimization of local T1 and T2 sensitivities. Med Phys 2024; 51:4721-4735. [PMID: 38386904 DOI: 10.1002/mp.17001] [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: 03/21/2023] [Revised: 01/18/2024] [Accepted: 01/23/2024] [Indexed: 02/24/2024] Open
Abstract
BACKGROUND Time-resolved magnetic resonance fingerprinting (MRF), or 4D-MRF, has been demonstrated its feasibility in motion management in radiotherapy (RT). However, the prohibitive long acquisition time is one of challenges of the clinical implementation of 4D-MRF. The shortening of acquisition time causes data insufficiency in each respiratory phase, leading to poor accuracies and consistencies of the predicted tissues' properties of each phase. PURPOSE To develop a technique for the reconstruction of multi-phase parametric maps in four-dimensional magnetic resonance fingerprinting (4D-MRF) through the optimization of local T1 and T2 sensitivities. METHODS The proposed technique employed an iterative optimization to tailor the data arrangement of each phase by manipulation of inter-phase frames, such that the T1 and T2 sensitivities, which were quantified by the modified Minkowski distance, of the truncated signal evolution curve was maximized. The multi-phase signal evolution curves were modified by sliding window reconstruction and inter-phase frame sharing (SWIFS). Motion correction (MC) and dot product matching were sequentially performed on the modified signal evolution and dictionary to reconstruct the multi-parametric maps. The proposed technique was evaluated by numerical simulations using the extended cardiac-torso (XCAT) phantom with regular and irregular breathing patterns, and by in vivo MRF data of three health volunteers and six liver cancer patients acquired at a 3.0 T scanner. RESULTS In simulation study, the proposed SWIFS approach achieved the overall mean absolute percentage error (MAPE) of 8.62% ± 1.59% and 16.2% ± 3.88% for the eight-phases T1 and T2 maps, respectively, in the sagittal view with irregular breathing patterns. In contrast, the overall MAPE of T1 and T2 maps generated by the conventional approach with multiple MRF repetitions were 22.1% ± 11.0% and 30.8% ± 14.9%, respectively. For in-vivo study, the predicted mean T1 and T2 of liver by the proposed SWIFS approach were 795 ms ± 38.9 ms and 58.3 ms ± 11.7 ms, respectively. CONCLUSIONS Both simulation and in vivo results showed that the approach empowered by T1 and T2 sensitivities optimization and sliding window under the shortened acquisition of MRF had superior performance in the estimation of multi-phase T1 and T2 maps as compared to the conventional approach with oversampling of MRF data.
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Affiliation(s)
- Yat Lam Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, China
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Chenyang Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Ho-Fun Victor Lee
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China
| | - Lai-Yin Andy Cheung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
- Department of Clinical Oncology, Oncology Center, St. Paul's Hospital, Hong Kong, China
| | - Edward Sai Kam Hui
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China
| | - Peng Cao
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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Warren M, Barrett A, Bhalla N, Brada M, Chuter R, Cobben D, Eccles CL, Hart C, Ibrahim E, McClelland J, Rea M, Turtle L, Fenwick JD. Sorting lung tumor volumes from 4D-MRI data using an automatic tumor-based signal reduces stitching artifacts. J Appl Clin Med Phys 2024; 25:e14262. [PMID: 38234116 PMCID: PMC11005973 DOI: 10.1002/acm2.14262] [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: 08/31/2023] [Revised: 10/30/2023] [Accepted: 12/18/2023] [Indexed: 01/19/2024] Open
Abstract
PURPOSE To investigate whether a novel signal derived from tumor motion allows more precise sorting of 4D-magnetic resonance (4D-MR) image data than do signals based on normal anatomy, reducing levels of stitching artifacts within sorted lung tumor volumes. METHODS (4D-MRI) scans were collected for 10 lung cancer patients using a 2D T2-weighted single-shot turbo spin echo sequence, obtaining 25 repeat frames per image slice. For each slice, a tumor-motion signal was generated using the first principal component of movement in the tumor neighborhood (TumorPC1). Signals were also generated from displacements of the diaphragm (DIA) and upper and lower chest wall (UCW/LCW) and from slice body area changes (BA). Pearson r coefficients of correlations between observed tumor movement and respiratory signals were determined. TumorPC1, DIA, and UCW signals were used to compile image stacks showing each patient's tumor volume in a respiratory phase. Unsorted image stacks were also built for comparison. For each image stack, the presence of stitching artifacts was assessed by measuring the roughness of the compiled tumor surface according to a roughness metric (Rg). Statistical differences in weighted means of Rg between any two signals were determined using an exact permutation test. RESULTS The TumorPC1 signal was most strongly correlated with superior-inferior tumor motion, and had significantly higher Pearson r values (median 0.86) than those determined for correlations of UCW, LCW, and BA with superior-inferior tumor motion (p < 0.05). Weighted means of ratios of Rg values in TumorPC1 image stacks to those in unsorted, UCW, and DIA stacks were 0.67, 0.69, and 0.71, all significantly favoring TumorPC1 (p = 0.02-0.05). For other pairs of signals, weighted mean ratios did not differ significantly from one. CONCLUSION Tumor volumes were smoother in 3D image stacks compiled using the first principal component of tumor motion than in stacks compiled with signals based on normal anatomy.
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Affiliation(s)
- Mark Warren
- School of Health Sciences, Institute of Population HealthUniversity of LiverpoolLiverpoolUK
| | | | - Neeraj Bhalla
- The Clatterbridge Cancer Centre NHS Foundation TrustLiverpoolUK
| | - Michael Brada
- Molecular & Clinical Cancer Medicine, Institute of Institute of Systems, Molecular and Integrative BiologyUniversity of LiverpoolLiverpoolUK
| | - Robert Chuter
- Christie Medical Physics and EngineeringThe Christie NHS Foundation TrustManchesterUK
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and HealthUniversity of ManchesterManchesterUK
| | - David Cobben
- The Clatterbridge Cancer Centre NHS Foundation TrustLiverpoolUK
- Department of Health Data Science, Institute of Population HealthUniversity of LiverpoolLiverpoolUK
| | - Cynthia L. Eccles
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and HealthUniversity of ManchesterManchesterUK
- RadiotherapyThe Christie NHS Foundation TrustManchesterUK
| | - Clare Hart
- The Clatterbridge Cancer Centre NHS Foundation TrustLiverpoolUK
| | - Ehab Ibrahim
- The Clatterbridge Cancer Centre NHS Foundation TrustLiverpoolUK
| | - Jamie McClelland
- Department of Medical Physics and BioengineeringUniversity College LondonLondonUK
| | - Marc Rea
- The Clatterbridge Cancer Centre NHS Foundation TrustLiverpoolUK
| | - Louise Turtle
- The Clatterbridge Cancer Centre NHS Foundation TrustLiverpoolUK
| | - John D. Fenwick
- Department of Medical Physics and BioengineeringUniversity College LondonLondonUK
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Kadalie E, Trotier AJ, Corbin N, Miraux S, Ribot EJ. Rapid whole brain 3D T 2 mapping respiratory-resolved Double-Echo Steady State (DESS) sequence with improved repeatability. Magn Reson Med 2024; 91:221-236. [PMID: 37794821 DOI: 10.1002/mrm.29847] [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: 03/03/2023] [Revised: 08/11/2023] [Accepted: 08/11/2023] [Indexed: 10/06/2023]
Abstract
PURPOSE To propose a quantitative 3D double-echo steady-state (DESS) sequence that offers rapid and repeatable T2 mapping of the human brain using different encoding schemes that account for respiratory B0 variation. METHODS A retrospective self-gating module was firstly implemented into the standard DESS sequence in order to suppress the respiratory artifact via data binning. A compressed-sensing trajectory (CS-DESS) was then optimized to accelerate the acquisition. Finally, a spiral Cartesian encoding (SPICCS-DESS) was incorporated to further disrupt the coherent respiratory artifact. These different versions were compared to a standard DESS sequence (fully DESS) by assessing the T2 distribution and repeatability in different brain regions of eight volunteers at 3 T. RESULTS The respiratory artifact correction was determined to be optimal when the data was binned into seven respiratory phases. Compared to the fully DESS, T2 distribution was improved for the CS-DESS and SPICCS-DESS with interquartile ranges reduced significantly by a factor ranging from 2 to 12 in the caudate, putamen, and thalamus regions. In the gray and white matter areas, average absolute test-retest T2 differences across all volunteers were respectively 3.5 ± 2% and 3.1 ± 2.1% for the SPICCS-DESS, 4.6 ± 4.6% and 4.9 ± 5.1% for the CS-DESS, and 15% ± 13% and 7.3 ± 5.6% for the fully DESS. The SPICCS-DESS sequence's acquisition time could be reduced by half (<4 min) while maintaining its efficient T2 mapping. CONCLUSION The respiratory-resolved SPICCS-DESS sequence offers rapid, robust, and repeatable 3D T2 mapping of the human brain, which can be especially effective for longitudinal monitoring of cerebral pathologies.
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Affiliation(s)
- Emile Kadalie
- Univ. Bordeaux, CNRS, Centre de Résonance Magnétique des Systèmes Biologiques (CRMSB), UMR 5536, F-33000, Bordeaux, France
| | - Aurélien J Trotier
- Univ. Bordeaux, CNRS, Centre de Résonance Magnétique des Systèmes Biologiques (CRMSB), UMR 5536, F-33000, Bordeaux, France
| | - Nadège Corbin
- Univ. Bordeaux, CNRS, Centre de Résonance Magnétique des Systèmes Biologiques (CRMSB), UMR 5536, F-33000, Bordeaux, France
| | - Sylvain Miraux
- Univ. Bordeaux, CNRS, Centre de Résonance Magnétique des Systèmes Biologiques (CRMSB), UMR 5536, F-33000, Bordeaux, France
| | - Emeline J Ribot
- Univ. Bordeaux, CNRS, Centre de Résonance Magnétique des Systèmes Biologiques (CRMSB), UMR 5536, F-33000, Bordeaux, France
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Xiao H, Han X, Zhi S, Wong YL, Liu C, Li W, Liu W, Wang W, Zhang Y, Wu H, Lee HFV, Cheung LYA, Chang HC, Liao YP, Deng J, Li T, Cai J. Ultra-fast multi-parametric 4D-MRI image reconstruction for real-time applications using a downsampling-invariant deformable registration (D2R) model. Radiother Oncol 2023; 189:109948. [PMID: 37832790 DOI: 10.1016/j.radonc.2023.109948] [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: 02/28/2023] [Revised: 09/12/2023] [Accepted: 10/09/2023] [Indexed: 10/15/2023]
Abstract
BACKGROUND AND PURPOSE Motion estimation from severely downsampled 4D-MRI is essential for real-time imaging and tumor tracking. This simulation study developed a novel deep learning model for simultaneous MR image reconstruction and motion estimation, named the Downsampling-Invariant Deformable Registration (D2R) model. MATERIALS AND METHODS Forty-three patients undergoing radiotherapy for liver tumors were recruited for model training and internal validation. Five prospective patients from another center were recruited for external validation. Patients received 4D-MRI scans and 3D MRI scans. The 4D-MRI was retrospectively down-sampled to simulate real-time acquisition. Motion estimation was performed using the proposed D2R model. The accuracy and robustness of the proposed D2R model and baseline methods, including Demons, Elastix, the parametric total variation (pTV) algorithm, and VoxelMorph, were compared. High-quality (HQ) 4D-MR images were also constructed using the D2R model for real-time imaging feasibility verification. The image quality and motion accuracy of the constructed HQ 4D-MRI were evaluated. RESULTS The D2R model showed significantly superior and robust registration performance than all the baseline methods at downsampling factors up to 500. HQ T1-weighted and T2-weighted 4D-MR images were also successfully constructed with significantly improved image quality, sub-voxel level motion error, and real-time efficiency. External validation demonstrated the robustness and generalizability of the technique. CONCLUSION In this study, we developed a novel D2R model for deformation estimation of downsampled 4D-MR images. HQ 4D-MR images were successfully constructed using the D2R model. This model may expand the clinical implementation of 4D-MRI for real-time motion management during liver cancer treatment.
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Affiliation(s)
- Haonan Xiao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China 999077; Department of Radiation Oncology and Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, China.
| | - Xinyang Han
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China 999077
| | - Shaohua Zhi
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China 999077
| | - Yat-Lam Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China 999077
| | - Chenyang Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China 999077
| | - Wen Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China 999077
| | - Weiwei Liu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing 100000, China
| | - Weihu Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing 100000, China
| | - Yibao Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing 100000, China
| | - Hao Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing 100000, China
| | - Ho-Fun Victor Lee
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China 999077
| | - Lai-Yin Andy Cheung
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China 999077
| | - Hing-Chiu Chang
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China 999077
| | - Yen-Peng Liao
- Department of Radiation Oncology's Division of Medical Physics & Engineering, University of Texas Southwestern Medical Center, Texas 75390, USA
| | - Jie Deng
- Department of Radiation Oncology's Division of Medical Physics & Engineering, University of Texas Southwestern Medical Center, Texas 75390, USA
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China 999077.
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China 999077.
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Wang L, Li T, Cai J, Chang HC. Motion-resolved four-dimensional abdominal diffusion-weighted imaging using PROPELLER EPI (4D-DW-PROPELLER-EPI). Magn Reson Med 2023; 90:2454-2471. [PMID: 37486854 DOI: 10.1002/mrm.29802] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 06/22/2023] [Accepted: 07/03/2023] [Indexed: 07/26/2023]
Abstract
PURPOSE To develop a distortion-free motion-resolved four-dimensional diffusion-weighted PROPELLER EPI (4D-DW-PROPELLER-EPI) technique for benefiting clinical abdominal radiotherapy (RT). METHODS An improved abdominal 4D-DWI technique based on 2D diffusion-weighted PROPELLER-EPI (2D-DW-PROPELLER-EPI), termed 4D-DW-PROPELLER-EPI, was proposed to improve the frame rate of repeated data acquisition and produce distortion-free 4D-DWI images. Since the radial or PROPELLER sampling with golden-angle rotation can achieve an efficient k-space coverage with a flexible time-resolved acquisition, the golden-angle multi-blade acquisition was used in the proposed 4D-DW-PROPELLER-EPI to improve the performance of data sorting. A new k-space and blade (K-B) amplitude binning method was developed for the proposed 4D-DW-PROPELLER-EPI to optimize the number of blades and the k-space uniformity before performing conventional PROPELLER-EPI reconstruction, by using two metrics to evaluate the adequacy of the acquired data. The proposed 4D-DW-PROPELLER-EPI was preliminarily evaluated in both simulation experiments and in vivo experiments with varying frame rates and different numbers of repeated acquisition. RESULTS The feasibility of achieving distortion-free 4D-DWI images by using the proposed 4D-DW-PROPELLER-EPI technique was demonstrated in both digital phantom and healthy subjects. Evaluation of the 4D completeness metrics shows that the K-B amplitude binning method could simultaneously improve the acquisition efficiency and data reconstruction performance for 4D-DW-PROPELLER-EPI. CONCLUSION 4D-DW-PROPELLER-EPI with K-B amplitude binning is an advanced technique that can provide distortion-free 4D-DWI images for resolving respiratory motion, and may benefit the application of image-guided abdominal RT.
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Affiliation(s)
- Lu Wang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Hing-Chiu Chang
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong
- Multi-Scale Medical Robotics Center, The Chinese University of Hong Kong, Hong Kong, Hong Kong
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Liu C, Li T, Cao P, Hui ES, Wong YL, Wang Z, Xiao H, Zhi S, Zhou T, Li W, Lam SK, Cheung ALY, Lee VHF, Ying M, Cai J. Respiratory-Correlated 4-Dimensional Magnetic Resonance Fingerprinting for Liver Cancer Radiation Therapy Motion Management. Int J Radiat Oncol Biol Phys 2023; 117:493-504. [PMID: 37116591 DOI: 10.1016/j.ijrobp.2023.04.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 04/04/2023] [Accepted: 04/18/2023] [Indexed: 04/30/2023]
Abstract
PURPOSE The objective of this study was to develop a respiratory-correlated (RC) 4-dimensional (4D) imaging technique based on magnetic resonance fingerprinting (MRF) (RC-4DMRF) for liver tumor motion management in radiation therapy. METHODS AND MATERIALS Thirteen patients with liver cancer were prospectively enrolled in this study. k-space MRF signals of the liver were acquired during free-breathing using the fast acquisition with steady-state precession sequence on a 3T scanner. The signals were binned into 8 respiratory phases based on respiratory surrogates, and interphase displacement vector fields were estimated using a phase-specific low-rank optimization method. Hereafter, the tissue property maps, including T1 and T2 relaxation times, and proton density, were reconstructed using a pyramid motion-compensated method that alternatively optimized interphase displacement vector fields and subspace images. To evaluate the efficacy of RC-4DMRF, amplitude motion differences and Pearson correlation coefficients were determined to assess measurement agreement in tumor motion between RC-4DMRF and cine magnetic resonance imaging (MRI); mean absolute percentage errors of the RC-4DMRF-derived tissue maps were calculated to reveal tissue quantification accuracy using digital human phantom; and tumor-to-liver contrast-to-noise ratio of RC-4DMRF images was compared with that of planning CT and contrast-enhanced MRI (CE-MRI) images. A paired Student t test was used for statistical significance analysis with a P value threshold of .05. RESULTS RC-4DMRF achieved excellent agreement in motion measurement with cine MRI, yielding the mean (± standard deviation) Pearson correlation coefficients of 0.95 ± 0.05 and 0.93 ± 0.09 and amplitude motion differences of 1.48 ± 1.06 mm and 0.81 ± 0.64 mm in the superior-inferior and anterior-posterior directions, respectively. Moreover, RC-4DMRF achieved high accuracy in tissue property quantification, with mean absolute percentage errors of 8.8%, 9.6%, and 5.0% for T1, T2, and proton density, respectively. Notably, the tumor contrast-to-noise ratio in RC-4DMRI-derived T1 maps (6.41 ± 3.37) was found to be the highest among all tissue property maps, approximately equal to that of CE-MRI (6.96 ± 1.01, P = .862), and substantially higher than that of planning CT (2.91 ± 1.97, P = .048). CONCLUSIONS RC-4DMRF demonstrated high accuracy in respiratory motion measurement and tissue properties quantification, potentially facilitating tumor motion management in liver radiation therapy.
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Affiliation(s)
- Chenyang Liu
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Tian Li
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Peng Cao
- Department of Diagnostic Radiology, University of Hong Kong, Hong Kong SAR, China
| | - Edward S Hui
- Department of Imaging and Interventional Radiology, Chinese University of Hong Kong, Hong Kong SAR, China; Department of Psychiatry, Chinese University of Hong Kong, Hong Kong SAR, China
| | - Yat-Lam Wong
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Zuojun Wang
- Department of Diagnostic Radiology, University of Hong Kong, Hong Kong SAR, China
| | - Haonan Xiao
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Shaohua Zhi
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Ta Zhou
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Wen Li
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Sai Kit Lam
- Department of Biomedical Engineering, Hong Kong Polytechnic University, Hong Kong SAR, China; Research Institute for Smart Ageing, Hong Kong Polytechnic University, Hong Kong SAR, China
| | | | - Victor Ho-Fun Lee
- Department of Clinical Oncology, University of Hong Kong, Hong Kong SAR, China
| | - Michael Ying
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China.
| | - Jing Cai
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China; Research Institute for Smart Ageing, Hong Kong Polytechnic University, Hong Kong SAR, China.
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Li T, Wang J, Yang Y, Glide-Hurst CK, Wen N, Cai J. Multi-parametric MRI for radiotherapy simulation. Med Phys 2023; 50:5273-5293. [PMID: 36710376 PMCID: PMC10382603 DOI: 10.1002/mp.16256] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 09/10/2022] [Accepted: 12/06/2022] [Indexed: 01/31/2023] Open
Abstract
Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image-guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi-parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi-contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.
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Affiliation(s)
- Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jihong Wang
- Department of Radiation Physics, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Yingli Yang
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Carri K Glide-Hurst
- Department of Radiation Oncology, University of Wisconsin, Madison, Wisconsin, USA
| | - Ning Wen
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- The Global Institute of Future Technology, Shanghai Jiaotong University, Shanghai, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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Shao HC, Li T, Dohopolski MJ, Wang J, Cai J, Tan J, Wang K, Zhang Y. Real-time MRI motion estimation through an unsupervised k-space-driven deformable registration network (KS-RegNet). Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac762c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 06/06/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Purpose. Real-time three-dimensional (3D) magnetic resonance (MR) imaging is challenging because of slow MR signal acquisition, leading to highly under-sampled k-space data. Here, we proposed a deep learning-based, k-space-driven deformable registration network (KS-RegNet) for real-time 3D MR imaging. By incorporating prior information, KS-RegNet performs a deformable image registration between a fully-sampled prior image and on-board images acquired from highly-under-sampled k-space data, to generate high-quality on-board images for real-time motion tracking. Methods. KS-RegNet is an end-to-end, unsupervised network consisting of an input data generation block, a subsequent U-Net core block, and following operations to compute data fidelity and regularization losses. The input data involved a fully-sampled, complex-valued prior image, and the k-space data of an on-board, real-time MR image (MRI). From the k-space data, under-sampled real-time MRI was reconstructed by the data generation block to input into the U-Net core. In addition, to train the U-Net core to learn the under-sampling artifacts, the k-space data of the prior image was intentionally under-sampled using the same readout trajectory as the real-time MRI, and reconstructed to serve an additional input. The U-Net core predicted a deformation vector field that deforms the prior MRI to on-board real-time MRI. To avoid adverse effects of quantifying image similarity on the artifacts-ridden images, the data fidelity loss of deformation was evaluated directly in k-space. Results. Compared with Elastix and other deep learning network architectures, KS-RegNet demonstrated better and more stable performance. The average (±s.d.) DICE coefficients of KS-RegNet on a cardiac dataset for the 5- , 9- , and 13-spoke k-space acquisitions were 0.884 ± 0.025, 0.889 ± 0.024, and 0.894 ± 0.022, respectively; and the corresponding average (±s.d.) center-of-mass errors (COMEs) were 1.21 ± 1.09, 1.29 ± 1.22, and 1.01 ± 0.86 mm, respectively. KS-RegNet also provided the best performance on an abdominal dataset. Conclusion. KS-RegNet allows real-time MRI generation with sub-second latency. It enables potential real-time MR-guided soft tissue tracking, tumor localization, and radiotherapy plan adaptation.
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Keijnemans K, Borman PTS, Uijtewaal P, Woodhead PL, Raaymakers BW, Fast MF. A hybrid 2D/4D-MRI methodology using simultaneous multislice imaging for radiotherapy guidance. Med Phys 2022; 49:6068-6081. [PMID: 35694905 PMCID: PMC9545880 DOI: 10.1002/mp.15802] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 04/18/2022] [Accepted: 05/27/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose Respiratory motion management is important in abdominothoracic radiotherapy. Fast imaging of the tumor can facilitate multileaf collimator (MLC) tracking that allows for smaller treatment margins, while repeatedly imaging the full field‐of‐view is necessary for 4D dose accumulation. This study introduces a hybrid 2D/4D‐MRI methodology that can be used for simultaneous MLC tracking and dose accumulation on a 1.5 T Unity MR‐linac (Elekta AB, Stockholm, Sweden). Methods We developed a hybrid 2D/4D‐MRI methodology that uses a simultaneous multislice (SMS) accelerated MRI sequence, which acquires two coronal slices simultaneously and repeatedly cycles through slice positions over the image volume. As a result, the fast 2D imaging can be used prospectively for MLC tracking and the SMS slices can be sorted retrospectively into respiratory‐correlated 4D‐MRIs for dose accumulation. Data were acquired in five healthy volunteers with an SMS‐bTFE and SMS‐TSE MRI sequence. For each sequence, a prebeam dataset and a beam‐on dataset were acquired simulating the two phases of MR‐linac treatments. Prebeam data were used to generate a 4D‐based motion model and a reference mid‐position volume, while beam‐on data were used for real‐time motion extraction and reconstruction of beam‐on 4D‐MRIs. In addition, an in‐silico computational phantom was used for validation of the hybrid 2D/4D‐MRI methodology. MLC tracking experiments were performed with the developed methodology, for which real‐time SMS data reconstruction was enabled on the scanner. A 15‐beam 8× 7.5 Gy intensity‐modulated radiotherapy plan for lung stereotactic body radiotherapy with isotropic 3 mm GTV‐to‐PTV margins was created. Dosimetry experiments were performed using a 4D motion phantom. The latency between target motion and updating the radiation beam was determined and compensated. Local gamma analyses were performed to quantify dose differences compared to a static reference delivery, and dose area histograms (DAHs) were used to quantify the GTV and PTV coverage. Results In‐vivo data acquisition and MLC tracking experiments were successfully performed with the developed hybrid 2D/4D‐MRI methodology. Real‐time liver–lung interface motion estimation had a Pearson's correlation of 0.996 (in‐vivo) and 0.998 (in‐silico). A median (5th–95th percentile) error of 0.0 (−0.9 to 0.7) mm and 0.0 (−0.2 to 0.2) mm was found for real‐time motion estimation for in‐vivo and in‐silico, respectively. Target motion prediction beyond the liver–lung interface had a median root mean square error of 1.6 mm (in‐vivo) and 0.5 mm (in‐silico). Beam‐on 4D MRI reconstruction required a median amount of data equal to an acquisition time of 2:21–3:17 min, which was 20% less data compared to the prebeam‐derived 4D‐MRI. System latency was reduced from 501 ± 12 ms to −1 ± 3 ms (SMS‐TSE) and from 398 ± 10 ms to −10 ± 4 ms (SMS‐bTFE) by a linear regression prediction filter. The local gamma analysis agreed within −3.8% to 3.3% (SMS‐bTFE) and −5.3% to 10% (SMS‐TSE) with a reference MRI sequence. The DAHs revealed a relative D98% GTV coverage between 97% and 100% (SMS‐bTFE) and 100% and 101% (SMS‐TSE) compared to the static reference. Conclusions The presented 2D/4D‐MRI methodology demonstrated the potential for accurately extracting real‐time motion for MLC tracking in abdominothoracic radiotherapy, while simultaneously reconstructing contiguous respiratory‐correlated 4D‐MRIs for dose accumulation.
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Affiliation(s)
- Katrinus Keijnemans
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Pim T S Borman
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Prescilla Uijtewaal
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Peter L Woodhead
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.,Elekta AB, kungstensgatan 18, 113 57 Stockholm, Sweden
| | - Bas W Raaymakers
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Martin F Fast
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
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10
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Cao P, Wang Z, Liu C, Li T, Hui E, Cai J. Motion-resolved and free-breathing liver MRF. Magn Reson Imaging 2022; 91:69-80. [DOI: 10.1016/j.mri.2022.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 05/01/2022] [Accepted: 05/22/2022] [Indexed: 11/28/2022]
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11
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Cheung ALY, Zhang L, Liu C, Li T, Cheung AHY, Leung C, Leung AKC, Lam SK, Lee VHF, Cai J. Evaluation of Multisource Adaptive MRI Fusion for Gross Tumor Volume Delineation of Hepatocellular Carcinoma. Front Oncol 2022; 12:816678. [PMID: 35280780 PMCID: PMC8913492 DOI: 10.3389/fonc.2022.816678] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 01/27/2022] [Indexed: 12/22/2022] Open
Abstract
Purpose Tumor delineation plays a critical role in radiotherapy for hepatocellular carcinoma (HCC) patients. The incorporation of MRI might improve the ability to correctly identify tumor boundaries and delineation consistency. In this study, we evaluated a novel Multisource Adaptive MRI Fusion (MAMF) method in HCC patients for tumor delineation. Methods Ten patients with HCC were included in this study retrospectively. Contrast-enhanced T1-weighted MRI at portal-venous phase (T1WPP), contrast-enhanced T1-weighted MRI at 19-min delayed phase (T1WDP), T2-weighted (T2W), and diffusion-weighted MRI (DWI) were acquired on a 3T MRI scanner and imported to in-house-developed MAMF software to generate synthetic MR fusion images. The original multi-contrast MR image sets were registered to planning CT by deformable image registration (DIR) using MIM. Four observers independently delineated gross tumor volumes (GTVs) on the planning CT, four original MR image sets, and the fused MRI for all patients. Tumor contrast-to-noise ratio (CNR) and Dice similarity coefficient (DSC) of the GTVs between each observer and a reference observer were measured on the six image sets. Inter-observer and inter-patient mean, SD, and coefficient of variation (CV) of the DSC were evaluated. Results Fused MRI showed the highest tumor CNR compared to planning CT and original MR sets in the ten patients. The mean ± SD tumor CNR was 0.72 ± 0.73, 3.66 ± 2.96, 4.13 ± 3.98, 4.10 ± 3.17, 5.25 ± 2.44, and 9.82 ± 4.19 for CT, T1WPP, T2W, DWI, T1WDP, and fused MRI, respectively. Fused MRI has the minimum inter-observer and inter-patient variations as compared to original MR sets and planning CT sets. GTV delineation inter-observer mean DSC across the ten patients was 0.81 ± 0.09, 0.85 ± 0.08, 0.88 ± 0.04, 0.89 ± 0.08, 0.90 ± 0.04, and 0.95 ± 0.02 for planning CT, T1WPP, T2W, DWI, T1WDP, and fused MRI, respectively. The patient mean inter-observer CV of DSC was 3.3%, 3.2%, 1.7%, 2.6%, 1.5%, and 0.9% for planning CT, T1WPP, T2W, DWI, T1WDP, and fused MRI, respectively. Conclusion The results demonstrated that the fused MRI generated using the MAMF method can enhance tumor CNR and improve inter-observer consistency of GTV delineation in HCC as compared to planning CT and four commonly used MR image sets (T1WPP, T1WDP, T2W, and DWI). The MAMF method holds great promise in MRI applications in HCC radiotherapy treatment planning.
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Affiliation(s)
- Andy Lai-Yin Cheung
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China.,Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Lei Zhang
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States.,Medical Physics Graduate Program, Duke University, Durham, NC, United States.,Medical Physics Graduate Program, Duke Kunshan University, Kunshan, China
| | - Chenyang Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Anson Ho-Yin Cheung
- Radiotherapy and Oncology Centre, Hong Kong Baptist Hospital, Hong Kong, Hong Kong SAR, China
| | - Chun Leung
- Radiotherapy and Oncology Centre, Hong Kong Baptist Hospital, Hong Kong, Hong Kong SAR, China
| | | | - Sai-Kit Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Victor Ho-Fun Lee
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China.,Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
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12
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Xiao H, Ni R, Zhi S, Li W, Liu C, Ren G, Teng X, Liu W, Wang W, Zhang Y, Wu H, Lee HFV, Cheung LYA, Chang HCC, Li T, Cai J. A Dual-supervised Deformation Estimation Model (DDEM) for constructing ultra-quality 4D-MRI based on a commercial low-quality 4D-MRI for liver cancer radiation therapy. Med Phys 2022; 49:3159-3170. [PMID: 35171511 DOI: 10.1002/mp.15542] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 01/09/2022] [Accepted: 02/09/2022] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND Most available 4D-MRI techniques are limited by insufficient image quality and long acquisition times or require specially designed sequences or hardware that are not available in the clinic. These limitations have greatly hindered the clinical implementation of 4D-MRI. PURPOSE This study aims to develop a fast ultra-quality (UQ) 4D-MRI reconstruction method using a commercially available 4D-MRI sequence and dual-supervised deformation estimation model (DDEM). METHODS Thirty-nine patients receiving radiotherapy for liver tumors were included. Each patient was scanned using a TWIST-VIBE MRI sequence to acquire 4D-MR images. They also received 3D T1-/T2-weighted MRI scans as prior images and UQ 4D-MRI at any instant was considered a deformation of them. A DDEM was developed to obtain a 4D deformable vector field (DVF) from 4D-MRI data, and the prior images were deformed using this 4D-DVF to generate UQ 4D-MR images. The registration accuracies of the DDEM, VoxelMorph (normalized cross-correlation (NCC) supervised), VoxelMorph (end-to-end point error (EPE) supervised), and the parametric total variation (pTV) algorithm were compared. Tumor motion on UQ 4D-MRI was evaluated quantitatively using region-of-interest (ROI) tracking errors, while image quality was evaluated using the contrast-to-noise ratio (CNR), lung-liver edge sharpness, and perceptual blur metric (PBM). RESULTS The registration accuracy of the DDEM was significantly better than those of VoxelMorph (NCC supervised), VoxelMorph (EPE supervised) and the pTV algorithm (all, p < 0.001), with an inference time of 69.3 ± 5.9 ms. UQ 4D-MRI yielded ROI tracking errors of 0.79 ± 0.65, 0.50 ± 0.55, and 0.51 ± 0.58 mm in the superior-inferior, anterior-posterior, and mid-lateral directions, respectively. From the original 4D-MRI to UQ 4D-MRI, the CNR increased from 7.25 ± 4.89 to 18.86 ± 15.81; the lung-liver edge full-width-at-half-maximum decreased from 8.22 ± 3.17 to 3.65 ± 1.66 mm in the in-plane direction and from 8.79 ± 2.78 to 5.04 ± 1.67 mm in the cross-plane direction, and the PBM decreased from 0.68 ± 0.07 to 0.38 ± 0.01. CONCLUSION This novel DDEM method successfully generated UQ 4D-MR images based on a commercial 4D-MRI sequence. It shows great promise for improving liver tumor motion management during radiation therapy. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Haonan Xiao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Ruiyan Ni
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Shaohua Zhi
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Wen Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Chenyang Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Weiwei Liu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing, 100000, China
| | - Weihu Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing, 100000, China
| | - Yibao Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing, 100000, China
| | - Hao Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing, 100000, China
| | - Ho-Fun Victor Lee
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong SAR, 999077, China
| | - Lai-Yin Andy Cheung
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong SAR, 999077, China
| | | | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
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13
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Liu C, Li M, Xiao H, Li T, Li W, Zhang J, Teng X, Cai J. Advances in MRI‐guided precision radiotherapy. PRECISION RADIATION ONCOLOGY 2022. [DOI: 10.1002/pro6.1143] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Affiliation(s)
- Chenyang Liu
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR China
| | - Mao Li
- Department of Radiation Oncology Philips Healthcare Chengdu China
| | - Haonan Xiao
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR China
| | - Tian Li
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR China
| | - Wen Li
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR China
| | - Jiang Zhang
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR China
| | - Xinzhi Teng
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR China
| | - Jing Cai
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR China
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14
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Zhang L, Yin FF, Li T, Teng X, Xiao H, Harris W, Ren L, Kong FMS, Ge H, Mao R, Cai J. Multi-contrast four-dimensional magnetic resonance imaging (MC-4D-MRI): Development and initial evaluation in liver tumor patients. Med Phys 2021; 48:7984-7997. [PMID: 34706072 DOI: 10.1002/mp.15314] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 07/15/2021] [Accepted: 10/06/2021] [Indexed: 12/25/2022] Open
Abstract
PURPOSE To develop a novel multi-contrast four-dimensional magnetic resonance imaging (MC-4D-MRI) technique that expands single image contrast 4D-MRI to a spectrum of native and synthetic image contrasts and to evaluate its feasibility in liver tumor patients. METHODS AND MATERIALS The MC-4D-MRI technique integrates multi-parametric MRI fusion, 4D-MRI, and deformable image registration (DIR) techniques. The fusion technique consists of native MRI as input, image pre-processing, fusion algorithm, adaptation, and fused multi-contrast MRI as output. Four-dimensional deformation vector fields (4D-DVF) were generated from an original T2/T1-w 4D-MRI by deforming end-of-inhalation (EOI) to nine other phase volumes via DIR. The 4D-DVF were applied to multi-contrast MRI to generate a spectrum of 4D-MRI in different image contrasts. The MC-4D-MRI technique was evaluated in five liver tumor patients on tumor contrast-to-noise ratio (CNR), internal target volume (ITV) contouring consistency, diaphragm motion range, and tumor motion trajectory; and in digital anthropomorphic phantoms on 4D-DIR introduced errors in tumor motion range, centroid location, extent, and volume. RESULTS MC-4D-MRI consisting of 4D-MRIs in native image contrasts (T1-w, T2-w, and T2/T1-w) and synthetic image contrasts, such as tumor-enhanced contrast (TEC) were generated in five liver tumor patients. Patient tumor CNR increased from 2.6 ± 1.8 in the T2/T1-w MRI, to -4.4 ± 2.4, 6.6 ± 3.0, and 9.6 ± 3.9 in the T1-w, T2-w, and TEC MRI, respectively. Patient ITV inter-observer mean Dice similarity coefficient (mDSC) increased from 0.65 ± 0.10 in the original T2/T1-w 4D-MRI, to 0.76 ± 0.14, 0.77 ± 0.12, and 0.86 ± 0.05 in the T1-w, T2-w, and TEC 4D-MRI, respectively. Patient diaphragm motion range absolute differences between the three new 4D-MRIs and original T2/T1-w 4D-MRI were 1.2 ± 1.3, 0.3 ± 0.7, and 0.5 ± 0.5 mm, respectively. Patient tumor displacement phase-averaged absolute differences between the three 4D-MRIs and the original 4D-MRI were 0.72 ± 0.33, 0.62 ± 0.54, and 0.74 ± 0.43 mm in the superior-inferior (SI) direction, and 0.59 ± 0.36, 0.51 ± 0.30, and 0.50 ± 0.24 mm in the anterior-posterior (AP) direction, respectively. In the digital phantoms, phase-averaged absolute tumor centroid shift caused by the 4D-DIR were at or below 0.5 mm in SI, AP, and left-right (LR) directions. CONCLUSION We developed an MC-4D-MRI technique capable of expanding single image contrast 4D-MRI along a new dimension of image contrast. Initial evaluations in liver tumor patients showed enhancements in image contrast variety, tumor contrast, and ITV contouring consistencies using MC-4D-MRI. The technique might offer new perspectives on the image contrast of MRI and 4D-MRI in MR-guided radiotherapy.
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Affiliation(s)
- Lei Zhang
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA.,Medical Physics Graduate Program, Duke University, Durham, North Carolina, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA.,Medical Physics Graduate Program, Duke University, Durham, North Carolina, USA
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Haonan Xiao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Wendy Harris
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA.,Medical Physics Graduate Program, Duke University, Durham, North Carolina, USA
| | - Lei Ren
- Department of Radiation Oncology, University of Maryland, Baltimore, Maryland, USA
| | | | - Hong Ge
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Ronghu Mao
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Jing Cai
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA.,Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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15
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Han S, Liang X, Li T, Yin FF, Cai J. Slice-stacking T2-weighted MRI for fast determination of internal target volume for liver tumor. Quant Imaging Med Surg 2021; 11:32-42. [PMID: 33392009 DOI: 10.21037/qims-20-41] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Background To investigate the feasibility of generating maximum intensity projection (MIP) images to determine internal target volume (ITV) using slice-stacking MRI (SS-MRI) technique. Methods Slice-stacking is a technique which applies a multi-slice MRI acquisition to generate a 3D MIP for ITV contouring, without reconstructing 4D-MRI. 4D digital extended cardiac-torso (XCAT) phantom was used to generate MIP images with sequential 2D HASTE sequence, with different tumor diameters (10, 30 and 50 mm) and with simulated regular and irregular (patient) breathing motions. A reference MIP was generated using all acquisition images. Consecutive repetitions were then used to generate MIP to analyze the relationship between Dice's similarity coefficient (DSC) and the number of repetitions, and the relationship between the relative ITV volume difference and the number of repetitions. Images from XCAT phantom and from three hepatic carcinoma patients were collected in this study to demonstrate the feasibility of this technique. Results For both regular and irregular breathing motion, the average DSC of ITV is >0.94 and the average relative ITV volume difference is <10% (approximately 0.15 cm3) when using 5 repeated scanning images to reconstruct MIP for tumor diameter of 10 mm. As tumor diameter increases, the DSC of ITV is >0.97 and the relative ITV volume difference is <5% for regular breathing motion, and the DSC of ITV is >0.97 and the relative ITV volume difference is <5.5% for irregular breathing motion when using 5 repeated scanning images to reconstruct MIP. In patient image study, the mean relative ITV volume difference is <3% and the mean DSC is 0.99 when using 5 repeated scanning images to reconstruct MIP. Conclusions The number of scans required to generate tumor ITV for slice-stacking method (5-7 repetition) is 3-4 times less than that of 4D-MRI (15-20 repetitions). It is feasible to generate a fast clinically acceptable ITV using slice-stacking method with sequential 2D MR images.
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Affiliation(s)
- Silu Han
- Medical Physics Graduate Program, Duke University Medical Center, Durham, NNC, USA.,Department of Biomedical Engineering, The University of Arizona, Tucson, AZ, USA
| | - Xiao Liang
- Medical Physics Graduate Program, Duke University Medical Center, Durham, NNC, USA
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Fang-Fang Yin
- Medical Physics Graduate Program, Duke University Medical Center, Durham, NNC, USA.,Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.,Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
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Kavaluus H, Seppälä T, Koivula L, Salli E, Collan J, Saarilahti K, Tenhunen M. Retrospective four-dimensional magnetic resonance imaging of liver: Method development. J Appl Clin Med Phys 2020; 21:304-313. [PMID: 33270997 PMCID: PMC7769409 DOI: 10.1002/acm2.13108] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 10/29/2020] [Accepted: 11/06/2020] [Indexed: 11/16/2022] Open
Abstract
Purpose of our research was to develop a four‐dimensional (4D) magnetic resonance imaging (MRI) method of liver. Requirements of the method were to create a clinical procedure with acceptable imaging time and sufficient temporal and spatial accuracy. The method should produce useful planning image sets for stereotactic body radiation therapy delivery both during breath‐hold and in free breathing. The purpose of the method was to improve the localization of liver metastasis. The method was validated with phantom tests. Imaging parameters were optimized to create a 4D dataset compressed to one respiratory cycle of the whole liver with clinically reasonable level of image contrast and artifacts. Five healthy volunteers were imaged with T2‐weighted SSFSE research sequence. The respiratory surrogate signal was observed by the linear navigator interleaved with the anatomical liver images. The navigator was set on head‐feet — direction on the superior surface of the liver to detect the edge of diaphragm. The navigator signal and 2D liver image data were retrospectively processed with a self‐developed MATLAB algorithm. A deformable phantom for 4D imaging tests was constructed by combining deformable tissue‐equivalent material and a commercial programmable motor unit of the 4D phantom with a clinically relevant range of deformation patterns. 4D Computed Tomography images were used as reference to validate the MRI protocol. The best compromise of reasonable accuracy and imaging time was found with 2D T2‐weighted SSFSE imaging sequence using parameters: TR = 500–550 ms, images/slices = 20, slice thickness = 3 mm. Then, image processing with number of respiratory phases = 8 constructed accurate 4D images of liver. We have developed the 4D‐MRI method visualizing liver motions three‐dimensionally in one representative respiratory cycle. From phantom tests it was found that the spatial agreement to 4D‐CT is within 2 mm that is considered sufficient for clinical applications.
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Affiliation(s)
- Henna Kavaluus
- Comprehensive Cancer Center, University of Helsinki and Helsinki University Hospital, Radiotherapy, Finland.,Department of Physics, MATRENA doctoral programme, University of Helsinki, Helsinki, Finland
| | - Tiina Seppälä
- Comprehensive Cancer Center, University of Helsinki and Helsinki University Hospital, Radiotherapy, Finland
| | - Lauri Koivula
- Comprehensive Cancer Center, University of Helsinki and Helsinki University Hospital, Radiotherapy, Finland.,Department of Physics, MATRENA doctoral programme, University of Helsinki, Helsinki, Finland.,Medical imaging center, Radiology, University of Helsinki and Helsinki University Hospital, Finland
| | - Eero Salli
- Medical imaging center, Radiology, University of Helsinki and Helsinki University Hospital, Finland
| | - Juhani Collan
- Comprehensive Cancer Center, University of Helsinki and Helsinki University Hospital, Radiotherapy, Finland
| | - Kauko Saarilahti
- Comprehensive Cancer Center, University of Helsinki and Helsinki University Hospital, Radiotherapy, Finland
| | - Mikko Tenhunen
- Comprehensive Cancer Center, University of Helsinki and Helsinki University Hospital, Radiotherapy, Finland
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Time‐resolved magnetic resonance fingerprinting for radiotherapy motion management. Med Phys 2020; 47:6286-6293. [DOI: 10.1002/mp.14513] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 08/20/2020] [Accepted: 09/21/2020] [Indexed: 01/13/2023] Open
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18
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Nie X, Saleh Z, Kadbi M, Zakian K, Deasy J, Rimner A, Li G. A super-resolution framework for the reconstruction of T2-weighted (T2w) time-resolved (TR) 4DMRI using T1w TR-4DMRI as the guidance. Med Phys 2020; 47:3091-3102. [PMID: 32166757 DOI: 10.1002/mp.14136] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 01/30/2020] [Accepted: 03/05/2020] [Indexed: 12/25/2022] Open
Abstract
PURPOSE The purpose of this study was to develop T2-weighted (T2w) time-resolved (TR) four-dimensional magnetic resonance imaging (4DMRI) reconstruction technique with higher soft-tissue contrast for multiple breathing cycle motion assessment by building a super-resolution (SR) framework using the T1w TR-4DMRI reconstruction as guidance. METHODS The multi-breath T1w TR-4DMRI was reconstructed by deforming a high-resolution (HR: 2 × 2 × 2 mm3 ) volumetric breath-hold (BH, 20s) three-dimensional magnetic resonance imaging (3DMRI) image to a series of low-resolution (LR: 5 × 5 × 5 mm3 ) 3D cine images at a 2Hz frame rate in free-breathing (FB, 40 s) using an enhanced Demons algorithm, namely [T1BH →FB] reconstruction. Within the same imaging session, respiratory-correlated (RC) T2w 4DMRI (2 × 2 × 2 mm3 ) was acquired based on an internal navigator to gain HR T2w (T2HR ) in three states (full exhalation and mid and full inhalation) in ~5 min. Minor binning artifacts in the RC-4DMRI were automatically identified based on voxel intensity correlation (VIC) between consecutive slices as outliers (VIC < VICmean -σ) and corrected by deforming the artifact slices to interpolated slices from the adjacent slices iteratively until no outliers were identified. A T2HR image with minimal deformation (<1 cm at the diaphragm) from the T1BH image was selected for multi-modal B-Spline deformable image registration (DIR) to establish the T2HR -T1BH voxel correspondence. Two approaches to reconstruct T2w TR-4DMRI were investigated: (A) T2HR →[T1BH →FB]: to deform T2w HR to T1w BH only as T1w TR-4DMRI was reconstructed, and combine the two displacement vector fields (DVFs) to reconstruct T2w TR-4DMRI, and (B) [T2HR ←T1BH ]→FB: to deform T1w BH to T2w HR first and apply the deformed T1w BH to reconstruct T2w TR-4DMRI. The reconstruction times were similar, 8-12 min per volume. To validate the two methods, T2w- and T1w-mapped 4D XCAT digital phantoms were utilized with three synthetic spherical tumors (ϕ = 2.0, 3.0, and 4.0 cm) in the lower or mid lobes as the ground truth to evaluate the tumor location (the center of mass, COM), size (volume ratio, %V), and shape (Dice index). Six lung cancer patients were scanned under an IRB-approved protocol and the T2w TR-4DMRI images reconstructed from the two methods were compared based on the preservation of the three tumor characteristics. The local tumor-contained image quality was also characterized using the VIC and structure similarity (SSIM) indexes. RESULTS In the 4D digital phantom, excellent tumor alignment after T2HR -T1HR DIR is achieved: ∆COM = 0.8 ± 0.5 mm, %V = 1.06 ± 0.02, and Dice = 0.91 ± 0.03, in both deformation directions using the DIR-target image as the reference. In patients, binning artifacts are corrected with improved image quality: average VIC increases from 0.92 ± 0.03 to 0.95 ± 0.01. Both T2w TR-4DMRI reconstruction methods produce similar tumor alignment errors ∆COM = 2.9 ± 0.6 mm. However, method B ([T2HR ←T1BH ]→FB) produces superior results in preserving more T2w tumor features with a higher %V = 0.99 ± 0.03, Dice = 0.81 ± 0.06, VIC = 0.85 ± 0.06, and SSIM = 0.65 ± 0.10 in the T2w TR-4DMRI images. CONCLUSIONS This study has demonstrated the feasibility of T2w TR-4DMRI reconstruction with high soft-tissue contrast and adequately-preserved tumor position, size, and shape in multiple breathing cycles. The T2w-centric DIR (method B) produces a superior solution for the SR-based framework of T2w TR-4DMRI reconstruction with highly preserved tumor characteristics and local image features, which are useful for tumor delineation and motion management in radiation therapy.
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Affiliation(s)
- Xingyu Nie
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Ziad Saleh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Mo Kadbi
- Philips Healthcare, MR Therapy, Cleveland, OH, USA
| | - Kristen Zakian
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Joseph Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Andreas Rimner
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Guang Li
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
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Yang Z, Ren L, Yin FF, Liang X, Cai J. Motion robust 4D-MRI sorting based on anatomic feature matching: A digital phantom simulation study. RADIATION MEDICINE AND PROTECTION 2020; 1:41-47. [DOI: 10.1016/j.radmp.2020.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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de Senneville BD, Cardiet CR, Trotier AJ, Ribot EJ, Lafitte L, Facq L, Miraux S. Optimizing 4D abdominal MRI: image denoising using an iterative back-projection approach. Phys Med Biol 2020; 65:015003. [PMID: 31714255 DOI: 10.1088/1361-6560/ab563e] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
4D-MRI is a promising tool for organ exploration, target delineation and treatment planning. Intra-scan motion artifacts may be greatly reduced by increasing the imaging frame rate. However, poor signal-to-noise ratios (SNR) are observed when increasing spatial and/or frame number per physiological cycle, in particular in the abdomen. In the current work, the proposed 4D-MRI method favored spatial resolution, frame number, isotropic voxels and large field-of-view (FOV) during MR-acquisition. The consequential SNR penalty in the reconstructed data is addressed retrospectively using an iterative back-projection (IBP) algorithm. Practically, after computing individual spatial 3D deformations present in the images using a deformable image registration (DIR) algorithm, each 3D image is individually enhanced by fusing several successive frames in its local temporal neighborood, these latter being likely to cover common independent informations. A tuning parameter allows one to freely readjust the balance between temporal resolution and precision of the 4D-MRI. The benefit of the method was quantitatively evaluated on the thorax of 6 mice under free breathing using a clinically acceptable duration. Improved 4D cardiac imaging was also shown in the heart of 1 mice. Obtained results are compared to theoretical expectations and discussed. The proposed implementation is easily parallelizable and optimized 4D-MRI could thereby be obtained with a clinically acceptable duration.
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Affiliation(s)
- B Denis de Senneville
- 'Institut de Mathématiques de Bordeaux', University of Bordeaux/CNRS UMR 5251, 351 Cours de la Libération, 33405 Talence Cedex, France. Author to whom any correspondence should be addressed
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Sun D, Liang X, Yin F, Cai J. Probability-based 3D k-space sorting for motion robust 4D-MRI. Quant Imaging Med Surg 2019; 9:1326-1336. [PMID: 31448217 DOI: 10.21037/qims.2019.07.06] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Current 4D-MRI techniques are prone to breathing-variation-induced motion artifacts. This study developed a novel method for motion-robust multi-cycle 4D-MRI using probability-based multi-cycle sorting to overcome this deficiency. METHODS The main cycles were first extracted from the breathing signal. 3D k-space data were then sorted using a result-driven method for each main cycle. The new method was tested on a 4D-extended cardiac-torso (XCAT) phantom with a patient and an artificially generated breathing curve. For comparison, the k-space data were sorted using conventional phase sorting to generate single-cycle 4D-MRI images. Signal-to-noise ratio (SNR) of tumor and liver, tumor volume consistency, and average intensity projection (AIP) accuracy were compared between the two methods. The original phantom images were used as references for the evaluation. RESULTS The new method showed improved tumor-to-liver SNR and tumor volume consistency as compared to 3D k-space phase sorting in both the simulated artificial and real patient breathing signals. For the artificial breathing cycles, the average tumor-to-liver SNR and standard deviation (SD) of tumor volume were 2.53 and 3.80% for cycle 1, 2.24 and 6.16% for cycle 2 of probability-based sorting as compared to 1.47 and 21.83% obtained using the phase sorting method; for the patient breathing curve, values of 1.99 and 2.71%, 1.97 and 3.29%, 1.88 and 4.16% were observed for cycle 1, cycle 2 and cycle 3 of probability-based sorting, versus 1.44 and 7.20% for phase sorting method. Furthermore, the AIP accuracy was improved in the probability-based sorting approach when compared to phase sorting, with the average intensity difference per voxel reduced from 0.39 to 0.15 for the artificial curve, and from 0.46 to 0.21 for the patient curve. CONCLUSIONS We demonstrated the feasibility of probability-based 3D k-space sorting for motion-robust multi-cycle 4D-MRI reconstruction with breathing variation induced motion artifact reduction compared with conventional 2D image sorting and 3D phase sorting methods. This new technique can potentially improve the accuracy of radiation treatment guidance for mobile targets.
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Affiliation(s)
- Duohua Sun
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan 215316, China
| | - Xiao Liang
- Medical Physics Graduate Program, Duke University, Durham, NC, USA
| | - Fangfang Yin
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan 215316, China.,Medical Physics Graduate Program, Duke University, Durham, NC, USA.,Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Jing Cai
- Medical Physics Graduate Program, Duke University, Durham, NC, USA.,Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.,Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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22
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Freedman JN, Collins DJ, Gurney-Champion OJ, McClelland JR, Nill S, Oelfke U, Leach MO, Wetscherek A. Super-resolution T2-weighted 4D MRI for image guided radiotherapy. Radiother Oncol 2018; 129:486-493. [PMID: 29871813 PMCID: PMC6294732 DOI: 10.1016/j.radonc.2018.05.015] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2017] [Revised: 05/02/2018] [Accepted: 05/14/2018] [Indexed: 11/28/2022]
Abstract
BACKGROUND AND PURPOSE The superior soft-tissue contrast of 4D-T2w MRI motivates its use for delineation in radiotherapy treatment planning. We address current limitations of slice-selective implementations, including thick slices and artefacts originating from data incompleteness and variable breathing. MATERIALS AND METHODS A method was developed to calculate midposition and 4D-T2w images of the whole thorax from continuously acquired axial and sagittal 2D-T2w MRI (1.5 × 1.5 × 5.0 mm3). The method employed image-derived respiratory surrogates, deformable image registration and super-resolution reconstruction. Volunteer imaging and a respiratory motion phantom were used for validation. The minimum number of dynamic acquisitions needed to calculate a representative midposition image was investigated by retrospectively subsampling the data (10-30 dynamic acquisitions). RESULTS Super-resolution 4D-T2w MRI (1.0 × 1.0 × 1.0 mm3, 8 respiratory phases) did not suffer from data incompleteness and exhibited reduced stitching artefacts compared to sorted multi-slice MRI. Experiments using a respiratory motion phantom and colour-intensity projection images demonstrated a minor underestimation of the motion range. Midposition diaphragm differences in retrospectively subsampled acquisitions were <1.1 mm compared to the full dataset. 10 dynamic acquisitions were found sufficient to generate midposition MRI. CONCLUSIONS A motion-modelling and super-resolution method was developed to calculate high quality 4D/midposition T2w MRI from orthogonal 2D-T2w MRI.
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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, UK; CR UK Cancer Imaging Centre, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | - David J Collins
- CR UK Cancer Imaging Centre, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | - Oliver J Gurney-Champion
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | - Jamie R McClelland
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Simeon Nill
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | - Uwe Oelfke
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | - Martin O Leach
- CR UK Cancer Imaging Centre, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK.
| | - Andreas Wetscherek
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
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Paganelli C, Whelan B, Peroni M, Summers P, Fast M, van de Lindt T, McClelland J, Eiben B, Keall P, Lomax T, Riboldi M, Baroni G. MRI-guidance for motion management in external beam radiotherapy: current status and future challenges. Phys Med Biol 2018; 63:22TR03. [PMID: 30457121 DOI: 10.1088/1361-6560/aaebcf] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
High precision conformal radiotherapy requires sophisticated imaging techniques to aid in target localisation for planning and treatment, particularly when organ motion due to respiration is involved. X-ray based imaging is a well-established standard for radiotherapy treatments. Over the last few years, the ability of magnetic resonance imaging (MRI) to provide radiation-free images with high-resolution and superb soft tissue contrast has highlighted the potential of this imaging modality for radiotherapy treatment planning and motion management. In addition, these advantageous properties motivated several recent developments towards combined MRI radiation therapy treatment units, enabling in-room MRI-guidance and treatment adaptation. The aim of this review is to provide an overview of the state-of-the-art in MRI-based image guidance for organ motion management in external beam radiotherapy. Methodological aspects of MRI for organ motion management are reviewed and their application in treatment planning, in-room guidance and adaptive radiotherapy described. Finally, a roadmap for an optimal use of MRI-guidance is highlighted and future challenges are discussed.
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Affiliation(s)
- C Paganelli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy. Author to whom any correspondence should be addressed. www.cartcas.polimi.it
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Ginn JS, O'Connell D, Thomas DH, Low DA, Lamb JM. Model-Interpolated Gating for Magnetic Resonance Image-Guided Radiation Therapy. Int J Radiat Oncol Biol Phys 2018; 102:885-894. [PMID: 29970314 PMCID: PMC6542358 DOI: 10.1016/j.ijrobp.2018.05.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 04/03/2018] [Accepted: 05/02/2018] [Indexed: 12/25/2022]
Abstract
PURPOSE To develop and validate a technique for radiation therapy gating using slow (≤1 frame per second) magnetic resonance imaging (MRI) and a motion model. Proposed uses of the technique include radiation therapy gating using T2-weighted images and conducting additional imaging studies during gated treatments. METHODS AND MATERIALS The technique uses a physiologically guided breathing motion model to interpolate deformed target position between 2-dimensional (2D) MRI images acquired every 1 to 3 seconds. The model is parameterized by a 1-dimensional respiratory bellows surrogate and is continuously updated with the most recently acquired 2D images. A phantom and 8 volunteers were imaged with a 0.35T MRI-guided radiation therapy system. A balanced steady-state free precession sequence with a 2D frame rate of 3 frames per second was used to evaluate the technique. The accuracy and beam-on positive predictive value (PPV) of the model-based gating decisions were evaluated using the gating decisions derived from imaging as a ground truth. A T2-weighted gating offline proof-of-concept study using a half-Fourier, single-shot, turbo-spin echo sequence is reported. RESULTS Model-interpolated gating accuracy, beam-on PPV, and median absolute distances between model and image-tracked target centroids were, on average, 98.3%, 98.4%, and 0.33 mm, respectively, in the balanced steady-state free precession phantom studies and 93.7%, 92.1%, and 0.86 mm, respectively, in the volunteer studies. T2 model-interpolated gating in 6 volunteers yielded an average accuracy and PPV of 94.3% and 92.5%, respectively, and the mean absolute median distance between modeled and imaged target centroids was 0.86 mm. CONCLUSIONS This work demonstrates the concept of model-interpolated gating for MRI-guided radiation therapy. The technique was found to be potentially sufficiently accurate for clinical use. Further development is needed to accommodate out-of-plane motion and the use of an internal MR-based respiratory surrogate.
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Affiliation(s)
- John S Ginn
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California.
| | - Dylan O'Connell
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - David H Thomas
- Department of Radiation Oncology, University of Colorado School of Medicine, University of Colorado, Aurora, Colorado
| | - Daniel A Low
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - James M Lamb
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
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Stemkens B, Paulson ES, Tijssen RHN. Nuts and bolts of 4D-MRI for radiotherapy. ACTA ACUST UNITED AC 2018; 63:21TR01. [DOI: 10.1088/1361-6560/aae56d] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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26
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Comparison of four dimensional computed tomography and magnetic resonance imaging in abdominal radiotherapy planning. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2018; 7:70-75. [PMID: 33458408 PMCID: PMC7807635 DOI: 10.1016/j.phro.2018.09.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2018] [Revised: 09/21/2018] [Accepted: 09/21/2018] [Indexed: 12/25/2022]
Abstract
Background and Purpose Four-dimensional (4D) computed tomography (CT) is widely used in radiotherapy (RT) planning and remains the current standard for motion evaluation. We assess a 4D magnetic resonance imaging (MRI) sequence in terms of motion and image quality in a phantom, healthy volunteers and patients undergoing RT. Materials and Methods The 4D-MRI sequence is a prototype T1-weighted 3D gradient echo with radial acquisition with self-gating. The accuracy of the 4D-MRI respiratory sorting based method was assessed using a MRI-CT compatible respiratory simulation phantom. In volunteers, abdominal viscera were evaluated for artefact, noise, structure delineation and overall image quality using a previously published four-point scoring system. In patients undergoing abdominal RT, the tumour (or a surrogate) was utilized to assess the range of motion on both 4D-CT and 4D-MRI. Furthermore, imaging quality was evaluated for both 4D-CT and 4D-MRI. Results In phantom studies 4D-MRI demonstrated amplitude of motion error of less than 0.2 mm for five, seven and ten bins. 4D-MRI provided excellent image quality for liver, kidney and pancreas. In patients, the median amplitude of motion seen on 4D-CT and 4D-MRI was 11.2 mm (range 2.8–20.3 mm) and 10.1 mm (range 0.7–20.7 mm) respectively. The median difference in amplitude between 4D-CT and 4D-MRI was −0.6 mm (range −3.4–5.2 mm). 4D-MRI demonstrated superior edge detection (median score 3 versus 1) and overall image quality (median score 2 versus 1) compared to 4D-CT. Conclusions The prototype 4D-MRI sequence demonstrated promising results and may be used in abdominal targeting, motion gating, and towards implementing MRI-based adaptive RT.
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van de Lindt TN, Fast MF, van der Heide UA, Sonke JJ. Retrospective self-sorted 4D-MRI for the liver. Radiother Oncol 2018; 127:474-480. [DOI: 10.1016/j.radonc.2018.05.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Revised: 05/04/2018] [Accepted: 05/04/2018] [Indexed: 10/16/2022]
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Paganelli C, Kipritidis J, Lee D, Baroni G, Keall P, Riboldi M. Image‐based retrospective 4D
MRI
in external beam radiotherapy: A comparative study with a digital phantom. Med Phys 2018; 45:3161-3172. [DOI: 10.1002/mp.12965] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Revised: 04/30/2018] [Accepted: 05/03/2018] [Indexed: 12/16/2022] Open
Affiliation(s)
- Chiara Paganelli
- Dipartimento di Elettronica, Informazione e Bioingegneria Politecnico di Milano Milano 20133 Italy
| | - John Kipritidis
- Northern Sydney Cancer Centre Royal North Shore Hospital Sydney NSW 2065 Australia
- ACRF Image X Institute Sydney Medical School University of Sydney Sydney NSW 2015 Australia
| | - Danny Lee
- Department of Radiation Oncology Calvary Mater Newcastle Newcastle NSW 2298 Australia
| | - Guido Baroni
- Dipartimento di Elettronica, Informazione e Bioingegneria Politecnico di Milano Milano 20133 Italy
- Centro Nazionale di Adroterapia Oncologica Pavia 27100 Italy
| | - Paul Keall
- ACRF Image X Institute Sydney Medical School University of Sydney Sydney NSW 2015 Australia
| | - Marco Riboldi
- Department of Medical Physics Ludwig‐Maximilians‐Universitat Munchen Munich 80539 Germany
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van de Lindt T, Sonke JJ, Nowee M, Jansen E, van Pelt V, van der Heide U, Fast M. A Self-Sorting Coronal 4D-MRI Method for Daily Image Guidance of Liver Lesions on an MR-LINAC. Int J Radiat Oncol Biol Phys 2018; 102:875-884. [PMID: 30054104 DOI: 10.1016/j.ijrobp.2018.05.029] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 05/04/2018] [Accepted: 05/10/2018] [Indexed: 12/19/2022]
Abstract
PURPOSE Novel hybrid MR-LINAC devices provide MRI's superior soft-tissue contrast in the treatment room and thus have the potential to increase accuracy of liver stereotactic body radiation therapy (SBRT). Requirements for daily position verification using 4-dimensional MRI include tumor visibility and short acquisition-reconstruction time (preferably <5 min). The proposed method provides fast acquisition-reconstruction time and the flexibility to vary T1- and T2-weighting, using standard imaging sequences for straightforward implementation on an MR-LINAC. METHODS AND MATERIALS Images were acquired using a coronal 2-dimensional, multislice, single-shot turbo spin-echo (TSE) and turbo field-echo (TFE) sequence, which were repeated 30 times. An image-based self-sorting signal (ImS) was extracted from the data, and rigid registration of the diaphragm per slice position was performed and corrected for amplitude variation in the anteroposterior direction. Data were sorted into 10 bins according to amplitude and phase. ImS was validated in 4 healthy volunteers against a navigator signal. Positional variations within bins, missing data, and smoothness of the liver dome were compared between amplitude and phase binning in 10 volunteers. Tumor contrast and registration were investigated in 3 patients. RESULTS Each ImS was found to be in excellent agreement with the navigator signal with a correlation coefficient of >0.95 and binning differences of <1 bin. Better liver dome smoothness per bin in case of amplitude binning compared with that in phase binning (2.0-2.6 mm vs 2.4-3.7 mm, respectively) is a tradeoff for more missing data (3.5%-17.5% vs 3.5%-4.7%, respectively). Liver lesions were visible in almost all coronal TSE and TFE images, but the lesion boundary was better defined in the TSE images. Rigid registrations could be performed on the tumor area. CONCLUSIONS An efficient self-sorted 4-dimensional MRI method was developed and validated using standard sequences and fast reconstruction on a LINAC-integrated MRI scanner providing good tumor visibility for daily image-guided liver stereotactic body radiation therapy.
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Affiliation(s)
- Tessa van de Lindt
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jan-Jakob Sonke
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
| | - Marlies Nowee
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Edwin Jansen
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Vivian van Pelt
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Uulke van der Heide
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Martin Fast
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
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Du D, Mutic S, Li HH, Hu Y. An efficient model to guide prospective T2-weighted 4D magnetic resonance imaging acquisition. Med Phys 2018; 45:2453-2462. [PMID: 29663412 DOI: 10.1002/mp.12923] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Revised: 03/07/2018] [Accepted: 04/03/2018] [Indexed: 11/06/2022] Open
Abstract
PURPOSE To establish a mathematical model to guide prospective T2-weighted four-dimensional magnetic resonance imaging (4DMRI) acquisition and to propose an efficient solution to expedite prospective T2-weighted 4DMRI acquisition. METHODS Prospective T2-weighted 4DMRI acquisition was characterized by a mathematical model with 4DMRI acquisition time as the objective function and completeness of the image set, acquisition timing, image contrast, and image artifacts as constraints. Given the irregular nature of human respiration, an efficient solution based on the greedy strategy (ESGS) was proposed. The efficiency of the ESGS method was validated using healthy human subjects. Comparisons were made with the previous 4DMRI method incorporating the prefixed-order respiratory state splitting (PO-RSS) technique. RESULTS 4DMRI image sets acquired using the ESGS and PO-RSS methods had similar image quality. The average time to acquire a 4DMRI image set covering 60 slices at 10 respiratory states was reduced by 30%, from 13.1 min using the PO-RSS method to 9.0 min using the ESGS method. It was demonstrated that high-quality T2-weighted 4DMRI could be obtained within a reasonable amount of time and all slices within each of the three-dimensional volumes were indeed acquired at the same respiratory state. CONCLUSIONS The ESGS method substantially reduces the acquisition time for T2-weighted 4DMRI, making it ready for clinical evaluation to obtain abdominal tumor motion for radiotherapy treatment planning.
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Affiliation(s)
- Dongsu Du
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, 48202, USA
| | - Sasa Mutic
- Department of Radiation Oncology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - H Harold Li
- Department of Radiation Oncology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Yanle Hu
- Department of Radiation Oncology, Mayo Clinic in Arizona, Phoenix, AZ, 85054, USA
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Harris W, Yin FF, Wang C, Zhang Y, Cai J, Ren L. Accelerating volumetric cine MRI (VC-MRI) using undersampling for real-time 3D target localization/tracking in radiation therapy: a feasibility study. Phys Med Biol 2017; 63:01NT01. [PMID: 29087963 PMCID: PMC5756137 DOI: 10.1088/1361-6560/aa9746] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
PURPOSE To accelerate volumetric cine MRI (VC-MRI) using undersampled 2D-cine MRI to provide real-time 3D guidance for gating/target tracking in radiotherapy. METHODS 4D-MRI is acquired during patient simulation. One phase of the prior 4D-MRI is selected as the prior images, designated as MRIprior. The on-board VC-MRI at each time-step is considered a deformation of the MRIprior. The deformation field map is represented as a linear combination of the motion components extracted by principal component analysis from the prior 4D-MRI. The weighting coefficients of the motion components are solved by matching the corresponding 2D-slice of the VC-MRI with the on-board undersampled 2D-cine MRI acquired. Undersampled Cartesian and radial k-space acquisition strategies were investigated. The effects of k-space sampling percentage (SP) and distribution, tumor sizes and noise on the VC-MRI estimation were studied. The VC-MRI estimation was evaluated using XCAT simulation of lung cancer patients and data from liver cancer patients. Volume percent difference (VPD) and Center of Mass Shift (COMS) of the tumor volumes and tumor tracking errors were calculated. RESULTS For XCAT, VPD/COMS were 11.93 ± 2.37%/0.90 ± 0.27 mm and 11.53 ± 1.47%/0.85 ± 0.20 mm among all scenarios with Cartesian sampling (SP = 10%) and radial sampling (21 spokes, SP = 5.2%), respectively. When tumor size decreased, higher sampling rate achieved more accurate VC-MRI than lower sampling rate. VC-MRI was robust against noise levels up to SNR = 20. For patient data, the tumor tracking errors in superior-inferior, anterior-posterior and lateral (LAT) directions were 0.46 ± 0.20 mm, 0.56 ± 0.17 mm and 0.23 ± 0.16 mm, respectively, for Cartesian-based sampling with SP = 20% and 0.60 ± 0.19 mm, 0.56 ± 0.22 mm and 0.42 ± 0.15 mm, respectively, for radial-based sampling with SP = 8% (32 spokes). CONCLUSIONS It is feasible to estimate VC-MRI from a single undersampled on-board 2D cine MRI. Phantom and patient studies showed that the temporal resolution of VC-MRI can potentially be improved by 5-10 times using a 2D cine image acquired with 10-20% k-space sampling.
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Affiliation(s)
- Wendy Harris
- Medical Physics Graduate Program, Duke University, 2424 Erwin Road Suite 101, Durham, NC 27705, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University Medical Center, DUMC Box 3295, Durham, North Carolina, 27710, USA
- Medical Physics Graduate Program, Duke University, 2424 Erwin Road Suite 101, Durham, NC 27705, USA
- Medical Physics Graduate Program, Duke Kunshan University, 8 Duke Avenue, Kunshan, Jiangsu, 215316, China
| | - Chunhao Wang
- Department of Radiation Oncology, Duke University Medical Center, DUMC Box 3295, Durham, North Carolina, 27710, USA
- Medical Physics Graduate Program, Duke University, 2424 Erwin Road Suite 101, Durham, NC 27705, USA
| | - You Zhang
- Medical Physics Graduate Program, Duke University, 2424 Erwin Road Suite 101, Durham, NC 27705, USA
| | - Jing Cai
- Department of Radiation Oncology, Duke University Medical Center, DUMC Box 3295, Durham, North Carolina, 27710, USA
- Medical Physics Graduate Program, Duke University, 2424 Erwin Road Suite 101, Durham, NC 27705, USA
| | - Lei Ren
- Department of Radiation Oncology, Duke University Medical Center, DUMC Box 3295, Durham, North Carolina, 27710, USA
- Medical Physics Graduate Program, Duke University, 2424 Erwin Road Suite 101, Durham, NC 27705, USA
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Menten MJ, Wetscherek A, Fast MF. MRI-guided lung SBRT: Present and future developments. Phys Med 2017; 44:139-149. [PMID: 28242140 DOI: 10.1016/j.ejmp.2017.02.003] [Citation(s) in RCA: 80] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Revised: 01/25/2017] [Accepted: 02/07/2017] [Indexed: 12/25/2022] Open
Abstract
Stereotactic body radiotherapy (SBRT) is rapidly becoming an alternative to surgery for the treatment of early-stage non-small cell lung cancer patients. Lung SBRT is administered in a hypo-fractionated, conformal manner, delivering high doses to the target. To avoid normal-tissue toxicity, it is crucial to limit the exposure of nearby healthy organs-at-risk (OAR). Current image-guided radiotherapy strategies for lung SBRT are mostly based on X-ray imaging modalities. Although still in its infancy, magnetic resonance imaging (MRI) guidance for lung SBRT is not exposure-limited and MRI promises to improve crucial soft-tissue contrast. Looking beyond anatomical imaging, functional MRI is expected to inform treatment decisions and adaptations in the future. This review summarises and discusses how MRI could be advantageous to the different links of the radiotherapy treatment chain for lung SBRT: diagnosis and staging, tumour and OAR delineation, treatment planning, and inter- or intrafractional motion management. Special emphasis is placed on a new generation of hybrid MRI treatment devices and their potential for real-time adaptive radiotherapy.
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Affiliation(s)
- Martin J Menten
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK.
| | - Andreas Wetscherek
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | - Martin F Fast
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK.
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Freedman JN, Collins DJ, Bainbridge H, Rank CM, Nill S, Kachelrieß M, Oelfke U, Leach MO, Wetscherek A. T2-Weighted 4D Magnetic Resonance Imaging for Application in Magnetic Resonance-Guided Radiotherapy Treatment Planning. Invest Radiol 2017; 52:563-573. [PMID: 28459800 PMCID: PMC5581953 DOI: 10.1097/rli.0000000000000381] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Accepted: 03/20/2017] [Indexed: 12/13/2022]
Abstract
OBJECTIVES The aim of this study was to develop and verify a method to obtain good temporal resolution T2-weighted 4-dimensional (4D-T2w) magnetic resonance imaging (MRI) by using motion information from T1-weighted 4D (4D-T1w) MRI, to support treatment planning in MR-guided radiotherapy. MATERIALS AND METHODS Ten patients with primary non-small cell lung cancer were scanned at 1.5 T axially with a volumetric T2-weighted turbo spin echo sequence gated to exhalation and a volumetric T1-weighted stack-of-stars spoiled gradient echo sequence with golden angle spacing acquired in free breathing. From the latter, 20 respiratory phases were reconstructed using the recently developed 4D joint MoCo-HDTV algorithm based on the self-gating signal obtained from the k-space center. Motion vector fields describing the respiratory cycle were obtained by deformable image registration between the respiratory phases and projected onto the T2-weighted image volume. The resulting 4D-T2w volumes were verified against the 4D-T1w volumes: an edge-detection method was used to measure the diaphragm positions; the locations of anatomical landmarks delineated by a radiation oncologist were compared and normalized mutual information was calculated to evaluate volumetric image similarity. RESULTS High-resolution 4D-T2w MRI was obtained. Respiratory motion was preserved on calculated 4D-T2w MRI, with median diaphragm positions being consistent with less than 6.6 mm (2 voxels) for all patients and less than 3.3 mm (1 voxel) for 9 of 10 patients. Geometrical positions were coherent between 4D-T1w and 4D-T2w MRI as Euclidean distances between all corresponding anatomical landmarks agreed to within 7.6 mm (Euclidean distance of 2 voxels) and were below 3.8 mm (Euclidean distance of 1 voxel) for 355 of 470 pairs of anatomical landmarks. Volumetric image similarity was commensurate between 4D-T1w and 4D-T2w MRI, as mean percentage differences in normalized mutual information (calculated over all respiratory phases and patients), between corresponding respiratory phases of 4D-T1w and 4D-T2w MRI and the tie-phase of 4D-T1w and 3-dimensional T2w MRI, were consistent to 0.41% ± 0.37%. Four-dimensional T2w MRI displayed tumor extent, structure, and position more clearly than corresponding 4D-T1w MRI, especially when mobile tumor sites were adjacent to organs at risk. CONCLUSIONS A methodology to obtain 4D-T2w MRI that retrospectively applies the motion information from 4D-T1w MRI to 3-dimensional T2w MRI was developed and verified. Four-dimensional T2w MRI can assist clinicians in delineating mobile lesions that are difficult to define on 4D-T1w MRI, because of poor tumor-tissue contrast.
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Affiliation(s)
- Joshua N. Freedman
- From the *Joint Department of Physics, †CR UK Cancer Imaging Centre, and ‡Joint Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; and §Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - David J. Collins
- From the *Joint Department of Physics, †CR UK Cancer Imaging Centre, and ‡Joint Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; and §Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hannah Bainbridge
- From the *Joint Department of Physics, †CR UK Cancer Imaging Centre, and ‡Joint Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; and §Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christopher M. Rank
- From the *Joint Department of Physics, †CR UK Cancer Imaging Centre, and ‡Joint Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; and §Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Simeon Nill
- From the *Joint Department of Physics, †CR UK Cancer Imaging Centre, and ‡Joint Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; and §Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Marc Kachelrieß
- From the *Joint Department of Physics, †CR UK Cancer Imaging Centre, and ‡Joint Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; and §Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Uwe Oelfke
- From the *Joint Department of Physics, †CR UK Cancer Imaging Centre, and ‡Joint Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; and §Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Martin O. Leach
- From the *Joint Department of Physics, †CR UK Cancer Imaging Centre, and ‡Joint Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; and §Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Andreas Wetscherek
- From the *Joint Department of Physics, †CR UK Cancer Imaging Centre, and ‡Joint Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; and §Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Liu Y, Yin FF, Czito BG, Bashir MR, Palta M, Cai J. Retrospective four-dimensional magnetic resonance imaging with image-based respiratory surrogate: a sagittal-coronal-diaphragm point of intersection motion tracking method. J Med Imaging (Bellingham) 2017; 4:024007. [PMID: 28653014 DOI: 10.1117/1.jmi.4.2.024007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Accepted: 06/01/2017] [Indexed: 11/14/2022] Open
Abstract
A four-dimensional magnetic resonance imaging (4-D-MRI) technique with Sagittal-Coronal-Diaphragm Point-of-Intersection (SCD-PoI) as a respiratory surrogate is proposed. To develop an image-based respiratory surrogate, the SCD-PoI motion tracking method is used for retrospective 4-D-MRI reconstruction. Single-slice sagittal MR cine was acquired at a location near the center of the diaphragmatic dome. Multiple-slice coronal MR cines were acquired for 4-D-MRI reconstruction. As a motion surrogate, the diaphragm motion was measured from the PoI among the sagittal MRI cine plane, coronal MRI cine planes, and the diaphragm surface. These points were defined as the SCD-PoI. This point is used as a one-dimensional diaphragmatic navigator in our study. The 4-D-MRI technique was evaluated on a 4-D digital extended cardiac-torso (XCAT) human phantom, a motion phantom, and seven human subjects (five healthy volunteers and two cancer patients). Motion trajectories of a selected region of interest were measured on 4-D-MRI and compared with the known XCAT motion that served as references. The mean absolute amplitude difference ([Formula: see text]) and the cross-correlation coefficient (CC) of the comparisons were determined. 4-D-MRI of the XCAT phantom demonstrated highly accurate motion information ([Formula: see text], [Formula: see text]). Motion trajectories of the motion phantom measured on 4-D-MRI matched well with the references ([Formula: see text], [Formula: see text]). 4-D-MRI of human subjects showed minimal artifacts and clearly revealed the respiratory motion of organs and tumor (mean [Formula: see text]; mean [Formula: see text]). A 4-D-MRI technique with image-based respiratory surrogate has been developed and tested on phantoms and human subjects.
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Affiliation(s)
- Yilin Liu
- Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States.,Duke University Medical Center, Department of Radiation Oncology, Durham, North Carolina, United States
| | - Fang-Fang Yin
- Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States.,Duke University Medical Center, Department of Radiation Oncology, Durham, North Carolina, United States
| | - Brian G Czito
- Duke University Medical Center, Department of Radiation Oncology, Durham, North Carolina, United States
| | - Mustafa R Bashir
- Duke University Medical Center, Department of Radiation Oncology, Durham, North Carolina, United States.,Duke University Medical Center, Center for Advanced Magnetic Resonance Development, Durham, North Carolina, United States
| | - Manisha Palta
- Duke University Medical Center, Department of Radiation Oncology, Durham, North Carolina, United States
| | - Jing Cai
- Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States.,Duke University Medical Center, Department of Radiation Oncology, Durham, North Carolina, United States
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Liang X, Yin FF, Liu Y, Cai J. A probability-based multi-cycle sorting method for 4D-MRI: A simulation study. Med Phys 2017; 43:6375. [PMID: 27908178 DOI: 10.1118/1.4966705] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To develop a novel probability-based sorting method capable of generating multiple breathing cycles of 4D-MRI images and to evaluate performance of this new method by comparing with conventional phase-based methods in terms of image quality and tumor motion measurement. METHODS Based on previous findings that breathing motion probability density function (PDF) of a single breathing cycle is dramatically different from true stabilized PDF that resulted from many breathing cycles, it is expected that a probability-based sorting method capable of generating multiple breathing cycles of 4D images may capture breathing variation information missing from conventional single-cycle sorting methods. The overall idea is to identify a few main breathing cycles (and their corresponding weightings) that can best represent the main breathing patterns of the patient and then reconstruct a set of 4D images for each of the identified main breathing cycles. This method is implemented in three steps: (1) The breathing signal is decomposed into individual breathing cycles, characterized by amplitude, and period; (2) individual breathing cycles are grouped based on amplitude and period to determine the main breathing cycles. If a group contains more than 10% of all breathing cycles in a breathing signal, it is determined as a main breathing pattern group and is represented by the average of individual breathing cycles in the group; (3) for each main breathing cycle, a set of 4D images is reconstructed using a result-driven sorting method adapted from our previous study. The probability-based sorting method was first tested on 26 patients' breathing signals to evaluate its feasibility of improving target motion PDF. The new method was subsequently tested for a sequential image acquisition scheme on the 4D digital extended cardiac torso (XCAT) phantom. Performance of the probability-based and conventional sorting methods was evaluated in terms of target volume precision and accuracy as measured by the 4D images, and also the accuracy of average intensity projection (AIP) of 4D images. RESULTS Probability-based sorting showed improved similarity of breathing motion PDF from 4D images to reference PDF compared to single cycle sorting, indicated by the significant increase in Dice similarity coefficient (DSC) (probability-based sorting, DSC = 0.89 ± 0.03, and single cycle sorting, DSC = 0.83 ± 0.05, p-value <0.001). Based on the simulation study on XCAT, the probability-based method outperforms the conventional phase-based methods in qualitative evaluation on motion artifacts and quantitative evaluation on tumor volume precision and accuracy and accuracy of AIP of the 4D images. CONCLUSIONS In this paper the authors demonstrated the feasibility of a novel probability-based multicycle 4D image sorting method. The authors' preliminary results showed that the new method can improve the accuracy of tumor motion PDF and the AIP of 4D images, presenting potential advantages over the conventional phase-based sorting method for radiation therapy motion management.
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Affiliation(s)
- Xiao Liang
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 and Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710
| | - Fang-Fang Yin
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 and Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710
| | - Yilin Liu
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 and Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710
| | - Jing Cai
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 and Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710
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Arai TJ, Nofiele J, Madhuranthakam AJ, Yuan Q, Pedrosa I, Chopra R, Sawant A. Characterizing spatiotemporal information loss in sparse-sampling-based dynamic MRI for monitoring respiration-induced tumor motion in radiotherapy. Med Phys 2017; 43:2807-2820. [PMID: 27277029 DOI: 10.1118/1.4948684] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
PURPOSE Sparse-sampling and reconstruction techniques represent an attractive strategy to achieve faster image acquisition speeds, while maintaining adequate spatial resolution and signal-to-noise ratio in rapid magnetic resonance imaging (MRI). The authors investigate the use of one such sequence, broad-use linear acquisition speed-up technique (k-t BLAST) in monitoring tumor motion for thoracic and abdominal radiotherapy and examine the potential trade-off between increased sparsification (to increase imaging speed) and the potential loss of "true" information due to greater reliance on a priori information. METHODS Lung tumor motion trajectories in the superior-inferior direction, previously recorded from ten lung cancer patients, were replayed using a motion phantom module driven by an MRI-compatible motion platform. Eppendorf test tubes filled with water which serve as fiducial markers were placed in the phantom. The modeled rigid and deformable motions were collected in a coronal image slice using balanced fast field echo in conjunction with k-t BLAST. Root mean square (RMS) error was used as a metric of spatial accuracy as measured trajectories were compared to input data. The loss of spatial information was characterized for progressively increasing acceleration factor from 1 to 16; the resultant sampling frequency was increased approximately from 2.5 to 19 Hz when the principal direction of the motion was set along frequency encoding direction. In addition to the phantom study, respiration-induced tumor motions were captured from two patients (kidney tumor and lung tumor) at 13 Hz over 49 s to demonstrate the impact of high speed motion monitoring over multiple breathing cycles. For each subject, the authors compared the tumor centroid trajectory as well as the deformable motion during free breathing. RESULTS In the rigid and deformable phantom studies, the RMS error of target tracking at the acquisition speed of 19 Hz was approximately 0.3-0.4 mm, which was smaller than the reconstructed pixel resolution of 0.67 mm. In the patient study, the dynamic 2D MRI enabled the monitoring of cycle-to-cycle respiratory variability present in the tumor position. It was seen that the range of centroid motion as well as the area covered due to target motion during each individual respiratory cycle was underestimated compared to the entire motion range observed over multiple breathing cycles. CONCLUSIONS The authors' initial results demonstrate that sparse-sampling- and reconstruction-based dynamic MRI can be used to achieve adequate image acquisition speeds without significant information loss for the task of radiotherapy guidance. Such monitoring can yield spatial and temporal information superior to conventional offline and online motion capture methods used in thoracic and abdominal radiotherapy.
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Affiliation(s)
- Tatsuya J Arai
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas 75390
| | - Joris Nofiele
- Department of Radiology, UT Southwestern Medical Center, Dallas, Texas 75390
| | - Ananth J Madhuranthakam
- Department of Radiology, UT Southwestern Medical Center, Dallas, Texas 75390 and Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, Texas 75390
| | - Qing Yuan
- Department of Radiology, UT Southwestern Medical Center, Dallas, Texas 75390
| | - Ivan Pedrosa
- Department of Radiology, UT Southwestern Medical Center, Dallas, Texas 75390 and Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, Texas 75390
| | - Rajiv Chopra
- Department of Radiology, UT Southwestern Medical Center, Dallas, Texas 75390 and Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, Texas 75390
| | - Amit Sawant
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas 75390; Department of Radiology, UT Southwestern Medical Center, Dallas, Texas 75390; and Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, 21201
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Liu Y, Zhong X, Czito BG, Palta M, Bashir MR, Dale BM, Yin FF, Cai J. Four-dimensional diffusion-weighted MR imaging (4D-DWI): a feasibility study. Med Phys 2017; 44:397-406. [PMID: 28121369 DOI: 10.1002/mp.12037] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2016] [Revised: 10/04/2016] [Accepted: 11/23/2016] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Diffusion-weighted Magnetic Resonance Imaging (DWI) has been shown to be a powerful tool for cancer detection with high tumor-to-tissue contrast. This study aims to investigate the feasibility of developing a four-dimensional DWI technique (4D-DWI) for imaging respiratory motion for radiation therapy applications. MATERIALS/METHODS Image acquisition was performed by repeatedly imaging a volume of interest (VOI) using an interleaved multislice single-shot echo-planar imaging (EPI) 2D-DWI sequence in the axial plane. Each 2D-DWI image was acquired with an intermediately low b-value (b = 500 s/mm2 ) and with diffusion-encoding gradients in x, y, and z diffusion directions. Respiratory motion was simultaneously recorded using a respiratory bellow, and the synchronized respiratory signal was used to retrospectively sort the 2D images to generate 4D-DWI. Cine MRI using steady-state free precession was also acquired as a motion reference. As a preliminary feasibility study, this technique was implemented on a 4D digital human phantom (XCAT) with a simulated pancreas tumor. The respiratory motion of the phantom was controlled by regular sinusoidal motion profile. 4D-DWI tumor motion trajectories were extracted and compared with the input breathing curve. The mean absolute amplitude differences (D) were calculated in superior-inferior (SI) direction and anterior-posterior (AP) direction. The technique was then evaluated on two healthy volunteers. Finally, the effects of 4D-DWI on apparent diffusion coefficient (ADC) measurements were investigated for hypothetical heterogeneous tumors via simulations. RESULTS Tumor trajectories extracted from XCAT 4D-DWI were consistent with the input signal: the average D value was 1.9 mm (SI) and 0.4 mm (AP). The average D value was 2.6 mm (SI) and 1.7 mm (AP) for the two healthy volunteers. CONCLUSION A 4D-DWI technique has been developed and evaluated on digital phantom and human subjects. 4D-DWI can lead to more accurate respiratory motion measurement. This has a great potential to improve the visualization and delineation of cancer tumors for radiotherapy.
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Affiliation(s)
- Yilin Liu
- Medical Physics Graduate Program, Duke University, Durham, NC, 27710, USA
| | - Xiaodong Zhong
- MR R&D Collaborations, Siemens Healthcare, Atlanta, GA, 30354, USA
| | - Brian G Czito
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, 27710, USA
| | - Manisha Palta
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, 27710, USA
| | - Mustafa R Bashir
- Department of Radiology, Duke University Medical Center, Durham, NC, 27710, USA.,Center for Advanced Magnetic Resonance Development, Duke University Medical Center, Durham, NC, 27710, USA
| | - Brian M Dale
- MR R&D Collaborations, Siemens Healthcare, Cary, NC, 27511, USA
| | - Fang-Fang Yin
- Medical Physics Graduate Program, Duke University, Durham, NC, 27710, USA.,Department of Radiation Oncology, Duke University Medical Center, Durham, NC, 27710, USA
| | - Jing Cai
- Medical Physics Graduate Program, Duke University, Durham, NC, 27710, USA.,Department of Radiation Oncology, Duke University Medical Center, Durham, NC, 27710, USA
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Uh J, Ayaz Khan M, Hua C. Four-dimensional MRI using an internal respiratory surrogate derived by dimensionality reduction. Phys Med Biol 2016; 61:7812-7832. [DOI: 10.1088/0031-9155/61/21/7812] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Liu Y, Yin FF, Rhee D, Cai J. Accuracy of respiratory motion measurement of 4D-MRI: A comparison between cine and sequential acquisition. Med Phys 2016; 43:179. [PMID: 26745910 DOI: 10.1118/1.4938066] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE The authors have recently developed a cine-mode T2*/T1-weighted 4D-MRI technique and a sequential-mode T2-weighted 4D-MRI technique for imaging respiratory motion. This study aims at investigating which 4D-MRI image acquisition mode, cine or sequential, provides more accurate measurement of organ motion during respiration. METHODS A 4D digital extended cardiac-torso (XCAT) human phantom with a hypothesized tumor was used to simulate the image acquisition and the 4D-MRI reconstruction. The respiratory motion was controlled by the given breathing signal profiles. The tumor was manipulated to move continuously with the surrounding tissue. The motion trajectories were measured from both sequential- and cine-mode 4D-MRI images. The measured trajectories were compared with the average trajectory calculated from the input profiles, which was used as references. The error in 4D-MRI tumor motion trajectory (E) was determined. In addition, the corresponding respiratory motion amplitudes of all the selected 2D images for 4D reconstruction were recorded. Each of the amplitude was compared with the amplitude of its associated bin on the average breathing curve. The mean differences from the average breathing curve across all slice positions (D) were calculated. A total of 500 simulated respiratory profiles with a wide range of irregularity (Ir) were used to investigate the relationship between D and Ir. Furthermore, statistical analysis of E and D using XCAT controlled by 20 cancer patients' breathing profiles was conducted. Wilcoxon Signed Rank test was conducted to compare two modes. RESULTS D increased faster for cine-mode (D = 1.17 × Ir + 0.23) than sequential-mode (D = 0.47 × Ir + 0.23) as irregularity increased. For the XCAT study using 20 cancer patients' breathing profiles, the median E values were significantly different: 0.12 and 0.10 cm for cine- and sequential-modes, respectively, with a p-value of 0.02. The median D values were significantly different: 0.47 and 0.24 cm for cine- and sequential-modes, respectively, with a p-value < 0.001. CONCLUSIONS Respiratory motion measurement may be more accurate and less susceptible to breathing irregularity in sequential-mode 4D-MRI than that in cine-mode 4D-MRI.
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Affiliation(s)
- Yilin Liu
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27710 and Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710
| | - Fang-Fang Yin
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27710 and Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710
| | - DongJoo Rhee
- Dongnam Institute of Radiological and Medical Sciences, Gijang-gun, Busan 619-953, South Korea
| | - Jing Cai
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27710 and Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710
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