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Zhang L, Yin FF, Lu K, Moore B, Han S, Cai J. Improving liver tumor image contrast and synthesizing novel tissue contrasts by adaptive multiparametric MRI fusion. PRECISION RADIATION ONCOLOGY 2022; 6:190-198. [PMID: 36590077 PMCID: PMC9797133 DOI: 10.1002/pro6.1167] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/23/2022] [Indexed: 01/05/2023] Open
Abstract
Purpose Multiparametric MRI contains rich and complementary anatomical and functional information, which is often utilized separately. This study aims to propose an adaptive multiparametric MRI (mpMRI) fusion method and examine its capability in improving tumor contrast and synthesizing novel tissue contrasts among liver cancer patients. Methods An adaptive mpMRI fusion method was developed with five components: image pre-processing, fusion algorithm, database, adaptation rules, and fused MRI. Linear-weighted summation algorithm was used for fusion. Weight-driven and feature-driven adaptations were designed for different applications. A clinical-friendly graphic-user-interface (GUI) was developed in Matlab and used for mpMRI fusion. Twelve liver cancer patients and a digital human phantom were included in the study. Synthesis of novel image contrast and enhancement of image signal and contrast were examined in patient cases. Tumor contrast-to-noise ratio (CNR) and liver signal-to-noise ratio (SNR) were evaluated and compared before and after mpMRI fusion. Results The fusion platform was applicable in both XCAT phantom and patient cases. Novel image contrasts, including enhancement of soft-tissue boundary, vertebral body, tumor, and composition of multiple image features in a single image were achieved. Tumor CNR improved from -1.70 ± 2.57 to 4.88 ± 2.28 (p < 0.0001) for T1-w, from 3.39 ± 1.89 to 7.87 ± 3.47 (p < 0.01) for T2-w, and from 1.42 ± 1.66 to 7.69 ± 3.54 (p < 0.001) for T2/T1-w MRI. Liver SNR improved from 2.92 ± 2.39 to 9.96 ± 8.60 (p < 0.05) for DWI. The coefficient of variation (CV) of tumor CNR lowered from 1.57, 0.56, and 1.17 to 0.47, 0.44, and 0.46 for T1-w, T2-w and T2/T1-w MRI, respectively. Conclusion A multiparametric MRI fusion method was proposed and a prototype was developed. The method showed potential in improving clinically relevant features such as tumor contrast and liver signal. Synthesis of novel image contrasts including the composition of multiple image features into single image set was achieved.
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Affiliation(s)
- Lei Zhang
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705, USA
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, 215316 China
| | - Fang-Fang Yin
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705, USA
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, 215316 China
| | - Ke Lu
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705, USA
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA
| | - Brittany Moore
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705, USA
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA
| | - Silu Han
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705, USA
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
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Han P, Chen J, Xiao J, Han F, Hu Z, Yang W, Cao M, Ling DC, Li D, Christodoulou AG, Fan Z. Single projection driven real-time multi-contrast (SPIDERM) MR imaging using pre-learned spatial subspace and linear transformation. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac783e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 06/13/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Objective. To develop and test the feasibility of a novel Single ProjectIon DrivEn Real-time Multi-contrast (SPIDERM) MR imaging technique that can generate real-time 3D images on-the-fly with flexible contrast weightings and a low latency. Approach. In SPIDERM, a ‘prep’ scan is first performed, with sparse k-space sampling periodically interleaved with the central k-space line (navigator data), to learn a subject-specific model, incorporating a spatial subspace and a linear transformation between navigator data and subspace coordinates. A ‘live’ scan is then performed by repeatedly acquiring the central k-space line only to dynamically determine subspace coordinates. With the ‘prep’-learned subspace and ‘live’ coordinates, real-time 3D images are generated on-the-fly with computationally efficient matrix multiplication. When implemented based on a multi-contrast pulse sequence, SPIDERM further allows for data-driven image contrast regeneration to convert real-time contrast-varying images into contrast-frozen images at user’s discretion while maintaining motion states. Both digital phantom and in-vivo experiments were performed to evaluate the technical feasibility of SPIDERM. Main results. The elapsed time from the input of the central k-space line to the generation of real-time contrast-frozen 3D images was approximately 45 ms, permitting a latency of 55 ms or less. Motion displacement measured from SPIDERM and reference images showed excellent correlation (
R
2
≥
0.983
). Geometric variation from the ground truth in the digital phantom was acceptable as demonstrated by pancreas contour analysis (Dice ≥ 0.84, mean surface distance ≤ 0.95 mm). Quantitative image quality metrics showed good consistency between reference images and contrast-varying SPIDREM images in in-vivo studies (mean
NMRSE
=
0.141
,
PSNR
=
3
0.12
,
SSIM
=
0.88
). Significance. SPIDERM is capable of generating real-time multi-contrast 3D images with a low latency. An imaging framework based on SPIDERM has the potential to serve as a standalone package for MR-guided radiation therapy by offering adaptive simulation through a ‘prep’ scan and real-time image guidance through a ‘live’ scan.
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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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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: 3] [Impact Index Per Article: 1.5] [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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5
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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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