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Liao C, Cao X, Iyer SS, Schauman S, Zhou Z, Yan X, Chen Q, Li Z, Wang N, Gong T, Wu Z, He H, Zhong J, Yang Y, Kerr A, Grill-Spector K, Setsompop K. High-resolution myelin-water fraction and quantitative relaxation mapping using 3D ViSTa-MR fingerprinting. Magn Reson Med 2024; 91:2278-2293. [PMID: 38156945 PMCID: PMC10997479 DOI: 10.1002/mrm.29990] [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: 08/11/2023] [Revised: 12/11/2023] [Accepted: 12/11/2023] [Indexed: 01/03/2024]
Abstract
PURPOSE This study aims to develop a high-resolution whole-brain multi-parametric quantitative MRI approach for simultaneous mapping of myelin-water fraction (MWF), T1, T2, and proton-density (PD), all within a clinically feasible scan time. METHODS We developed 3D visualization of short transverse relaxation time component (ViSTa)-MRF, which combined ViSTa technique with MR fingerprinting (MRF), to achieve high-fidelity whole-brain MWF and T1/T2/PD mapping on a clinical 3T scanner. To achieve fast acquisition and memory-efficient reconstruction, the ViSTa-MRF sequence leverages an optimized 3D tiny-golden-angle-shuffling spiral-projection acquisition and joint spatial-temporal subspace reconstruction with optimized preconditioning algorithm. With the proposed ViSTa-MRF approach, high-fidelity direct MWF mapping was achieved without a need for multicompartment fitting that could introduce bias and/or noise from additional assumptions or priors. RESULTS The in vivo results demonstrate the effectiveness of the proposed acquisition and reconstruction framework to provide fast multi-parametric mapping with high SNR and good quality. The in vivo results of 1 mm- and 0.66 mm-isotropic resolution datasets indicate that the MWF values measured by the proposed method are consistent with standard ViSTa results that are 30× slower with lower SNR. Furthermore, we applied the proposed method to enable 5-min whole-brain 1 mm-iso assessment of MWF and T1/T2/PD mappings for infant brain development and for post-mortem brain samples. CONCLUSIONS In this work, we have developed a 3D ViSTa-MRF technique that enables the acquisition of whole-brain MWF, quantitative T1, T2, and PD maps at 1 and 0.66 mm isotropic resolution in 5 and 15 min, respectively. This advancement allows for quantitative investigations of myelination changes in the brain.
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Affiliation(s)
- Congyu Liao
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Xiaozhi Cao
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Siddharth Srinivasan Iyer
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sophie Schauman
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Zihan Zhou
- Department of Radiology, Stanford University, Stanford, CA, USA
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaoqian Yan
- Department of Psychology, Stanford University, Stanford, CA, USA
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Quan Chen
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Zhitao Li
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Nan Wang
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Ting Gong
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Zhe Wu
- Techna Institute, University Health Network, Toronto, ON, Canada
| | - Hongjian He
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
- School of Physics, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
- Department of Imaging Sciences, University of Rochester, Rochester, NY, USA
| | - Yang Yang
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Adam Kerr
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Stanford Center for Cognitive and Neurobiological Imaging, Stanford University, Stanford, CA, USA
| | | | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
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Liu C, Cui ZX, Jia S, Cheng J, Liu Y, Lin L, Hu Z, Xie T, Zhou Y, Zhu Y, Liang D, Zeng H, Wang H. DPP: deep phase prior for parallel imaging with wave encoding. Phys Med Biol 2024; 69:105013. [PMID: 38608645 DOI: 10.1088/1361-6560/ad3e5d] [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: 10/20/2023] [Accepted: 04/12/2024] [Indexed: 04/14/2024]
Abstract
Objective.In Magnetic Resonance (MR) parallel imaging with virtual channel-expanded Wave encoding, limitations are imposed on the ability to comprehensively and accurately characterize the background phase. These limitations are primarily attributed to the calibration process relying solely on center low-frequency Auto-Calibration Signals (ACS) data for calibration.Approach.To tackle the challenge of accurately estimating the background phase in wave encoding, a novel deep neural network model guided by deep phase priors is proposed with integrated virtual conjugate coil (VCC) extension. Concretely, within the proposed framework, the background phase is implicitly characterized by employing a carefully designed decoder convolutional neural network, leveraging the inherent characteristics of phase smoothness and compact support in the transformed domain. Furthermore, the proposed model with wave encoding benefits from additional priors, which incorporate transmission sparsity of the latent image and coil sensitivity smoothness.Main results.Ablation experiments were conducted to ascertain the proposed method's capability to implicitly represent CSM and the background phase. Subsequently, the superiority of the proposed method is demonstrated through confidence comparisons with competing methods, employing 4-fold and 5-fold acceleration experiments. In achieving 4-fold and 5-fold acceleration, the optimal quantitative metrics (PSNR/SSIM/NMSE) are 44.1359 dB/0.9863/0.0008 (4-fold) and 41.2074/0.9846/0.0017 (5-fold), respectively. Furthermore, the generalizability of the proposed method is further validated by conducting acceleration experiments with T1, T2, T2*, and various undersampling patterns. In addition, the DPP delivered much better performance than the conventional methods by exploring accelerated phase-sensitive SWI imaging. In SWI accelerated imaging, it also surpasses the optimal competing method in terms of (PSNR/SSIM/NMSE) with 0.096%/0.009%/0.0017%.Significance.The proposed method enables precise characterization of the background phase in the integrated VCC and wave encoding framework, supported via theoretical analysis and empirical findings. Our code is available at:https://github.com/sober235/DPP.
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Affiliation(s)
- Congcong Liu
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
| | - Zhuo-Xu Cui
- Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
| | - Sen Jia
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
| | - Jing Cheng
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
| | - Yuanyuan Liu
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
| | - Ling Lin
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
| | - Zhanqi Hu
- Department of Neurology, Shenzhen Children's Hospital, Shenzhen, Guangdong, People's Republic of China
| | - Taofeng Xie
- Inner Mongolia University, Hohhot, Inner Mongolia, People's Republic of China
- Inner Mongolia Medical University, Hohhot, Inner Mongolia, People's Republic of China
| | - Yihang Zhou
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
- Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, People's Republic of China
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen, People's Republic of China
| | - Yanjie Zhu
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
- Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, People's Republic of China
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen, People's Republic of China
| | - Dong Liang
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
- Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
- Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, People's Republic of China
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen, People's Republic of China
| | - Hongwu Zeng
- Department of Radiology, Shenzhen Children's Hospital, Shenzhen, Guangdong, People's Republic of China
| | - Haifeng Wang
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
- Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, People's Republic of China
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen, People's Republic of China
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Zhou Z, Li Q, Liao C, Cao X, Liang H, Chen Q, Pu R, Ye H, Tong Q, He H, Zhong J. Optimized three-dimensional ultrashort echo time: Magnetic resonance fingerprinting for myelin tissue fraction mapping. Hum Brain Mapp 2023; 44:2209-2223. [PMID: 36629336 PMCID: PMC10028641 DOI: 10.1002/hbm.26203] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 12/12/2022] [Accepted: 01/01/2023] [Indexed: 01/12/2023] Open
Abstract
Quantitative assessment of brain myelination has gained attention for both research and diagnosis of neurological diseases. However, conventional pulse sequences cannot directly acquire the myelin-proton signals due to its extremely short T2 and T2* values. To obtain the myelin-proton signals, dedicated short T2 acquisition techniques, such as ultrashort echo time (UTE) imaging, have been introduced. However, it remains challenging to isolate the myelin-proton signals from tissues with longer T2. In this article, we extended our previous two-dimensional ultrashort echo time magnetic resonance fingerprinting (UTE-MRF) with dual-echo acquisition to three dimensional (3D). Given a relatively low proton density (PD) of myelin-proton, we utilized Cramér-Rao Lower Bound to encode myelin-proton with the maximal SNR efficiency for optimizing the MR fingerprinting design, in order to improve the sensitivity of the sequence to myelin-proton. In addition, with a second echo of approximately 3 ms, myelin-water component can be also captured. A myelin-tissue (myelin-proton and myelin-water) fraction mapping can be thus calculated. The optimized 3D UTE-MRF with dual-echo acquisition is tested in simulations, physical phantom and in vivo studies of both healthy subjects and multiple sclerosis patients. The results suggest that the rapidly decayed myelin-proton and myelin-water signal can be depicted with UTE signals of our method at clinically relevant resolution (1.8 mm isotropic) in 15 min. With its good sensitivity to myelin loss in multiple sclerosis patients demonstrated, our method for the whole brain myelin-tissue fraction mapping in clinical friendly scan time has the potential for routine clinical imaging.
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Affiliation(s)
- Zihan Zhou
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China
| | - Qing Li
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
- MR Collaborations, Siemens Healthineers Ltd, Shanghai, China
| | - Congyu Liao
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Xiaozhi Cao
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Hui Liang
- Department of Neurology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Quan Chen
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Run Pu
- Neusoft Medical Systems, Shanghai, China
| | - Huihui Ye
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Qiqi Tong
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, Zhejiang, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China
- School of Physics, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
- Department of Imaging Sciences, University of Rochester, Rochester, New York, USA
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Lim SH, Lee J, Jung S, Kim B, Rhee HY, Oh SH, Park S, Cho AR, Ryu CW, Jahng GH. Myelin-Weighted Imaging Presents Reduced Apparent Myelin Water in Patients with Alzheimer’s Disease. Diagnostics (Basel) 2022; 12:diagnostics12020446. [PMID: 35204537 PMCID: PMC8871299 DOI: 10.3390/diagnostics12020446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/27/2022] [Accepted: 02/06/2022] [Indexed: 02/04/2023] Open
Abstract
The purpose of this study was to investigate myelin loss in both AD and mild cognitive impairment (MCI) patients with a new myelin water mapping technique within reasonable scan time and evaluate the clinical relevance of the apparent myelin water fraction (MWF) values by assessing the relationship between decreases in myelin water and the degree of memory decline or aging. Twenty-nine individuals were assigned to the cognitively normal (CN) elderly group, 32 participants were assigned to the MCI group, and 31 patients were assigned to the AD group. A 3D visualization of the short transverse relaxation time component (ViSTa)-gradient and spin-echo (GraSE) sequence was developed to map apparent MWF. Then, the MWF values were compared between the three participant groups and was evaluated the relationship with the degree of memory loss. The AD group showed a reduced apparent MWF compared to the CN and MCI groups. The largest AUC (area under the curve) value was in the corpus callosum and used to classify the CN and AD groups using the apparent MWF. The ViSTa-GraSE sequence can be a useful tool to map the MWF in a reasonable scan time. Combining the MWF in the corpus callosum with the detection of atrophy in the hippocampus can be valuable for group classification.
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Affiliation(s)
- Seung-Hyun Lim
- Department of Radiology, Kyung Hee University Hospital at Gangdong, 892 Dongnam-ro, Gangdong-gu, Seoul 05278, Korea; (S.-H.L.); (B.K.); (C.-W.R.)
| | - Jiyoon Lee
- Department of Biomedical Engineering, Undergraduate School, College of Electronics and Information, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin-si 17104, Korea; (J.L.); (S.J.)
| | - Sumin Jung
- Department of Biomedical Engineering, Undergraduate School, College of Electronics and Information, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin-si 17104, Korea; (J.L.); (S.J.)
| | - Bokyung Kim
- Department of Radiology, Kyung Hee University Hospital at Gangdong, 892 Dongnam-ro, Gangdong-gu, Seoul 05278, Korea; (S.-H.L.); (B.K.); (C.-W.R.)
| | - Hak Young Rhee
- Department of Neurology, Kyung Hee University Hospital at Gangdong, 892 Dongnam-ro, Gangdong-gu, Seoul 05278, Korea;
- Department of Medicine, College of Medicine, Kyung Hee University, 26 Kyung Hee Dae-ro, Dongdaemun-gu, Seoul 02447, Korea; (S.P.); (A.R.C.)
| | - Se-Hong Oh
- Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin 17035, Korea;
| | - Soonchan Park
- Department of Medicine, College of Medicine, Kyung Hee University, 26 Kyung Hee Dae-ro, Dongdaemun-gu, Seoul 02447, Korea; (S.P.); (A.R.C.)
| | - Ah Rang Cho
- Department of Medicine, College of Medicine, Kyung Hee University, 26 Kyung Hee Dae-ro, Dongdaemun-gu, Seoul 02447, Korea; (S.P.); (A.R.C.)
- Department of Psychiatry, Kyung Hee University Hospital at Gangdong, 892 Dongnam-ro, Gangdong-gu, Seoul 05278, Korea
| | - Chang-Woo Ryu
- Department of Radiology, Kyung Hee University Hospital at Gangdong, 892 Dongnam-ro, Gangdong-gu, Seoul 05278, Korea; (S.-H.L.); (B.K.); (C.-W.R.)
- Department of Medicine, College of Medicine, Kyung Hee University, 26 Kyung Hee Dae-ro, Dongdaemun-gu, Seoul 02447, Korea; (S.P.); (A.R.C.)
| | - Geon-Ho Jahng
- Department of Radiology, Kyung Hee University Hospital at Gangdong, 892 Dongnam-ro, Gangdong-gu, Seoul 05278, Korea; (S.-H.L.); (B.K.); (C.-W.R.)
- Department of Medicine, College of Medicine, Kyung Hee University, 26 Kyung Hee Dae-ro, Dongdaemun-gu, Seoul 02447, Korea; (S.P.); (A.R.C.)
- Correspondence: ; Tel.: +82-2-440-6187; Fax: +82-2-440-6932
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Li Z, Tian Q, Ngamsombat C, Cartmell S, Conklin J, Filho ALMG, Lo WC, Wang G, Ying K, Setsompop K, Fan Q, Bilgic B, Cauley S, Huang SY. High-fidelity fast volumetric brain MRI using synergistic wave-controlled aliasing in parallel imaging and a hybrid denoising generative adversarial network (HDnGAN). Med Phys 2021; 49:1000-1014. [PMID: 34961944 DOI: 10.1002/mp.15427] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 11/22/2021] [Accepted: 12/12/2021] [Indexed: 11/08/2022] Open
Abstract
PURPOSE The goal of this study is to leverage an advanced fast imaging technique, wave-controlled aliasing in parallel imaging (Wave-CAIPI), and a generative adversarial network (GAN) for denoising to achieve accelerated high-quality high-signal-to-noise-ratio (SNR) volumetric MRI. METHODS Three-dimensional (3D) T2 -weighted fluid-attenuated inversion recovery (FLAIR) image data were acquired on 33 multiple sclerosis (MS) patients using a prototype Wave-CAIPI sequence (acceleration factor R = 3×2, 2.75 minutes) and a standard T2 -SPACE FLAIR sequence (R = 2, 7.25 minutes). A hybrid denoising GAN entitled "HDnGAN" consisting of a 3D generator and a 2D discriminator was proposed to denoise highly accelerated Wave-CAIPI images. HDnGAN benefits from the improved image synthesis performance provided by the 3D generator and increased training samples from a limited number of patients for training the 2D discriminator. HDnGAN was trained and validated on data from 25 MS patients with the standard FLAIR images as the target and evaluated on data from 8 MS patients not seen during training. HDnGAN was compared to other denoising methods including AONLM, BM4D, MU-Net, and 3D GAN in qualitative and quantitative analysis of output images using the mean squared error (MSE) and VGG perceptual loss compared to standard FLAIR images, and a reader assessment by two neuroradiologists regarding sharpness, SNR, lesion conspicuity, and overall quality. Finally, the performance of these denoising methods was compared at higher noise levels using simulated data with added Rician noise. RESULTS HDnGAN effectively denoised low-SNR Wave-CAIPI images with sharpness and rich textural details, which could be adjusted by controlling the contribution of the adversarial loss to the total loss when training the generator. Quantitatively, HDnGAN (λ = 10-3 ) achieved low MSE and the lowest VGG perceptual loss. The reader study showed that HDnGAN (λ = 10-3 ) significantly improved the SNR of Wave-CAIPI images (P<0.001), outperformed AONLM (P = 0.015), BM4D (P<0.001), MU-Net (P<0.001) and 3D GAN (λ = 10-3 ) (P<0.001) regarding image sharpness, and outperformed MU-Net (P<0.001) and 3D GAN (λ = 10-3 ) (P = 0.001) regarding lesion conspicuity. The overall quality score of HDnGAN (λ = 10-3 ) (4.25±0.43) was significantly higher than those from Wave-CAIPI (3.69±0.46, P = 0.003), BM4D (3.50±0.71, P = 0.001), MU-Net (3.25±0.75, P<0.001), and 3D GAN (λ = 10-3 ) (3.50±0.50, P<0.001), with no significant difference compared to standard FLAIR images (4.38±0.48, P = 0.333). The advantages of HDnGAN over other methods were more obvious at higher noise levels. CONCLUSION HDnGAN provides robust and feasible denoising while preserving rich textural detail in empirical volumetric MRI data. Our study using empirical patient data and systematic evaluation supports the use of HDnGAN in combination with modern fast imaging techniques such as Wave-CAIPI to achieve high-fidelity fast volumetric MRI and represents an important step to the clinical translation of GANs. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Ziyu Li
- Department of Biomedical Engineering, Tsinghua University, Beijing, P.R. China
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Chanon Ngamsombat
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Mahidol, Thailand
| | - Samuel Cartmell
- Department of Radiology, Massachusetts General Hospital, Boston, USA
| | - John Conklin
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA.,Department of Radiology, Massachusetts General Hospital, Boston, USA
| | - Augusto Lio M Gonçalves Filho
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Massachusetts General Hospital, Boston, USA
| | | | - Guangzhi Wang
- Department of Biomedical Engineering, Tsinghua University, Beijing, P.R. China
| | - Kui Ying
- Department of Engineering Physics, Tsinghua University, Beijing, P. R. China
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA.,Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA.,Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Stephen Cauley
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA.,Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
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Assessing the differential sensitivities of wave-CAIPI ViSTa myelin water fraction and magnetization transfer saturation for efficiently quantifying tissue damage in MS. Mult Scler Relat Disord 2021; 56:103309. [PMID: 34688179 DOI: 10.1016/j.msard.2021.103309] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 09/21/2021] [Accepted: 10/02/2021] [Indexed: 11/23/2022]
Abstract
BACKGROUND Wave-CAIPI Visualization of Short Transverse relaxation time component (ViSTa) is a recently developed, short-T1-sensitized MRI method for fast quantification of myelin water fraction (MWF) in the human brain. It represents a promising technique for the evaluation of subtle, early signals of demyelination in the cerebral white matter of multiple sclerosis (MS) patients. Currently however, few studies exist that robustly assess the utility of ViSTa MWF measures of myelin compared to more conventional MRI measures of myelin in the brain of MS patients. Moreover, there are no previous studies evaluating the sensitivity of ViSTa MWF for the non-invasive detection of subtle tissue damage in both normal-appearing white matter (NAWM) and white matter lesions of MS patients. As a result, a central purpose of this study was to systematically evaluate the relationship between myelin sensitivity of T1-based ViSTa MWF mapping and a more generally recognized metric, Magnetization Transfer Saturation (MTsat), in healthy control and MS brain white matter. METHODS ViSTa MWF and MTsat values were evaluated in automatically-classified normal appearing white matter (NAWM), white matter (WM) lesion tissue, cortical gray matter, and deep gray matter of 29 MS patients and 10 healthy controls using 3T MRI. MWF and MT sat were also assessed in a tract-specific manner using the Johns Hopkins University WM atlas. MRI-derived measures of cerebral myelin content were uniquely compared by employing non-normal distribution-specific measures of median, interquartile range and skewness. Separate analyses of variance were applied to test tissue-specific differences in MTsat and ViSTa MWF distribution metrics. Non-parametric tests were utilized when appropriate. All tests were corrected for multiple comparisons using the False Discovery Rate method at the level, α=0.05. RESULTS Differences in whole NAWM MS tissue damage were detected with a higher effect size when using ViSTa MWF (q = 0.0008; ƞ2 = 0.34) compared to MTsat (q = 0.02; ƞ2= 0.24). We also observed that, as a possible measure of WM pathology, ViSTa-derived NAWM MWF voxel distributions of MS subjects were consistently skewed towards lower MWF values, while MTsat voxel distributions showed reduced skewness values. We further identified tract-specific reductions in mean ViSTa MWF of MS patients compared to controls that were not observed with MTsat. However, MTsat (q = 1.4 × 10-21; ƞ2 = 0.88) displayed higher effect sizes when differentiating NAWM and MS lesion tissue. Using regression analysis at the group level, we identified a linear relationship between MTsat and ViSTa MWF in NAWM (R2 = 0.46; p = 7.8 × 10-4) lesions (R2 = 0.30; p = 0.004), and with all tissue types combined (R2 = 0.71; p = 8.4 × 10-45). The linear relationship was also observed in most of the WM tracts we investigated. ViSTa MWF in NAWM of MS patients correlated with both disease duration (p = 0.02; R2 = 0.27) and WM lesion volume (p = 0.002; R2 = 0.34). CONCLUSION Because ViSTa MWF and MTsat metrics exhibit differential sensitivities to tissue damage in MS white matter, they can be collected in combination to provide an efficient, comprehensive measure of myelin water and macromolecular pool proton signals. These complementary measures may offer a more sensitive, non-invasive biopsy of early precursor signals in NAWM that occur prior to lesion formation. They may also aid in monitoring the efficacy of remyelination therapies.
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Bontempi P, Rozzanigo U, Amelio D, Scartoni D, Amichetti M, Farace P. Quantitative Multicomponent T2 Relaxation Showed Greater Sensitivity Than Flair Imaging to Detect Subtle Alterations at the Periphery of Lower Grade Gliomas. Front Oncol 2021; 11:651137. [PMID: 33828992 PMCID: PMC8019971 DOI: 10.3389/fonc.2021.651137] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 03/02/2021] [Indexed: 02/02/2023] Open
Abstract
Purpose To demonstrate that quantitative multicomponent T2 relaxation can be more sensitive than conventional FLAIR imaging for detecting cerebral tissue abnormalities. Methods Six patients affected by lower-grade non-enhancing gliomas underwent T2 relaxation and FLAIR imaging before a radiation treatment by proton therapy (PT) and were examined at follow-up. The T2 decay signal obtained by a thirty-two-echo sequence was decomposed into three main components, attributing to each component a different T2 range: water trapped in the lipid bilayer membrane of myelin, intra/extracellular water and cerebrospinal fluid. The T2 quantitative map of the intra/extracellular water was compared with FLAIR images. Results Before PT, in five patients a mismatch was observed between the intra/extracellular water T2 map and FLAIR images, with peri-tumoral areas of high T2 that typically extended outside the area of abnormal FLAIR hyper-intensity. Such mismatch regions evolved into two different types of patterns. The first type, observed in three patients, was a reduced extension of the abnormal regions on T2 map with respect to FLAIR images (T2 decrease pattern). The second type, observed in two patients, was the appearance of new areas of abnormal hyper-intensity on FLAIR images matching the anomalous T2 map extension (FLAIR increase pattern), that was considered as asymptomatic radiation induced damage. Conclusion Our preliminarily results suggest that quantitative T2 mapping of the intra/extracellular water component was more sensitive than conventional FLAIR imaging to subtle cerebral tissue abnormalities, deserving to be further investigated in future clinical studies.
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Affiliation(s)
- Pietro Bontempi
- Proton Therapy Unit, Hospital of Trento, Azienda Provinciale per i Servizi Sanitari (APSS), Trento, Italy
| | - Umberto Rozzanigo
- Radiology Department, Hospital of Trento, Azienda Provinciale per i Servizi Sanitari (APSS), Trento, Italy
| | - Dante Amelio
- Proton Therapy Unit, Hospital of Trento, Azienda Provinciale per i Servizi Sanitari (APSS), Trento, Italy
| | - Daniele Scartoni
- Proton Therapy Unit, Hospital of Trento, Azienda Provinciale per i Servizi Sanitari (APSS), Trento, Italy
| | - Maurizio Amichetti
- Proton Therapy Unit, Hospital of Trento, Azienda Provinciale per i Servizi Sanitari (APSS), Trento, Italy
| | - Paolo Farace
- Proton Therapy Unit, Hospital of Trento, Azienda Provinciale per i Servizi Sanitari (APSS), Trento, Italy
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Su S, Qiu Z, Luo C, Shi C, Wan L, Zhu Y, Li Y, Liu X, Zheng H, Liang D, Wang H. Accelerated 3D bSSFP Using a Modified Wave-CAIPI Technique With Truncated Wave Gradients. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:48-58. [PMID: 32886608 DOI: 10.1109/tmi.2020.3021737] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The Wave Controlled Aliasing In Parallel Imaging (Wave-CAIPI) technique manifests great potential to highly accelerate three-dimensional (3D) balanced steady-state free precession (bSSFP) through substantially reducing the geometric factor (g-factor) and aliasing artifacts of image reconstruction. However, severe banding artifacts appear in bSSFP imaging due to unbalanced gradients with nonzero 0th moment applied by the conventional Wave-CAIPI technique. In this study, we propose a 3D Wave-bSSFP scheme that adopts truncated wave gradients with zero 0th moment to avoid introducing additional banding artifacts and to maintain the advantages of wave encoding. The simulation results indicate that the number of wave cycles that are truncated and different options of applying wave gradients affect both the g-factor reduction and image quality, but the influence is limited. In phantom experiments, the proposed technique shows similar acceleration performance as the conventional Wave-CAIPI technique and effectively eliminates its introduced banding artifacts. Additionally, Wave-bSSFP obtains up to 12× retrospective acceleration at 0.8 mm isotropic resolution in in vivo 3D brain experiments and is superior to the state-of-the-art Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration (CAIPIRINHA) technique, according to both visual validation and quantitative analysis. Moreover, in vivo 3D spine and abdomen imaging demonstrate the potential clinical applications of Wave-bSSFP with fast acquisition speed, improved isotropic resolution and fine image quality.
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Piredda GF, Hilbert T, Thiran JP, Kober T. Probing myelin content of the human brain with MRI: A review. Magn Reson Med 2020; 85:627-652. [PMID: 32936494 DOI: 10.1002/mrm.28509] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 08/12/2020] [Accepted: 08/17/2020] [Indexed: 12/11/2022]
Abstract
Rapid and efficient transmission of electric signals among neurons of vertebrates is ensured by myelin-insulating sheaths surrounding axons. Human cognition, sensation, and motor functions rely on the integrity of these layers, and demyelinating diseases often entail serious cognitive and physical impairments. Magnetic resonance imaging radically transformed the way these disorders are monitored, offering an irreplaceable tool to noninvasively examine the brain structure. Several advanced techniques based on MRI have been developed to provide myelin-specific contrasts and a quantitative estimation of myelin density in vivo. Here, the vast offer of acquisition strategies developed to date for this task is reviewed. Advantages and pitfalls of the different approaches are compared and discussed.
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Affiliation(s)
- Gian Franco Piredda
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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Zhou Z, Tong Q, Zhang L, Ding Q, Lu H, Jonkman LE, Yao J, He H, Zhu K, Zhong J. Evaluation of the diffusion MRI white matter tract integrity model using myelin histology and Monte-Carlo simulations. Neuroimage 2020; 223:117313. [PMID: 32882384 DOI: 10.1016/j.neuroimage.2020.117313] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Revised: 08/21/2020] [Accepted: 08/24/2020] [Indexed: 12/13/2022] Open
Abstract
Quantitative evaluation of brain myelination has drawn considerable attention. Conventional diffusion-based magnetic resonance imaging models, including diffusion tensor imaging and diffusion kurtosis imaging (DKI),1 have been used to infer the microstructure and its changes in neurological diseases. White matter tract integrity (WMTI) was proposed as a biophysical model to relate the DKI-derived metrics to the underlying microstructure. Although the model has been validated on ex vivo animal brains, it was not well evaluated with ex vivo human brains. In this study, histological samples (namely corpus callosum) from postmortem human brains have been investigated based on WMTI analyses on a clinical 3T scanner and comparisons with gold standard myelin staining in proteolipid protein and Luxol fast blue. In addition, Monte Carlo simulations were conducted to link changes from ex vivo to in vivo conditions based on the microscale parameters of water diffusivity and permeability. The results show that WMTI metrics, including axonal water fraction AWF, radial extra-axonal diffusivity De⊥, and intra-axonal diffusivity Dawere needed to characterize myelin content alterations. Thus, WMTI model metrics are shown to be promising candidates as sensitive biomarkers of demyelination.
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Affiliation(s)
- Zihan Zhou
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Zhouyiqing Building, Room 314, Yuquan Campus, Hangzhou 310027, China
| | - Qiqi Tong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Zhouyiqing Building, Room 314, Yuquan Campus, Hangzhou 310027, China
| | - Lei Zhang
- China Brain Bank and Department of Neurology in Second Affiliated Hospital, Key Laboratory of Medical Neurobiology of Zhejiang Province, and Department of Neurobiology, Zhejiang University School of Medicine, Hangzhou 310058, China; Department of Pathology, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Qiuping Ding
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Zhouyiqing Building, Room 314, Yuquan Campus, Hangzhou 310027, China
| | - Hui Lu
- China Brain Bank and Department of Neurology in Second Affiliated Hospital, Key Laboratory of Medical Neurobiology of Zhejiang Province, and Department of Neurobiology, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Laura E Jonkman
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, location VUmc, the Netherlands
| | - Junye Yao
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Zhouyiqing Building, Room 314, Yuquan Campus, Hangzhou 310027, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Zhouyiqing Building, Room 314, Yuquan Campus, Hangzhou 310027, China.
| | - Keqing Zhu
- China Brain Bank and Department of Neurology in Second Affiliated Hospital, Key Laboratory of Medical Neurobiology of Zhejiang Province, and Department of Neurobiology, Zhejiang University School of Medicine, Hangzhou 310058, China; Department of Pathology, Zhejiang University School of Medicine, Hangzhou 310058, China.
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Zhouyiqing Building, Room 314, Yuquan Campus, Hangzhou 310027, China; Department of Imaging Sciences, University of Rochester, United States
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Wang H, Qiu Z, Su S, Jia S, Li Y, Liu X, Zheng H, Liang D. Parameter optimization framework on wave gradients of Wave‐CAIPI imaging. Magn Reson Med 2019; 83:1659-1672. [DOI: 10.1002/mrm.28034] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Revised: 08/30/2019] [Accepted: 09/18/2019] [Indexed: 01/28/2023]
Affiliation(s)
- Haifeng Wang
- Paul C. Lauterbur Research Centre for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China
| | - Zhilang Qiu
- Paul C. Lauterbur Research Centre for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China
- Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen Guangdong China
| | - Shi Su
- Paul C. Lauterbur Research Centre for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China
| | - Sen Jia
- Paul C. Lauterbur Research Centre for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China
- Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen Guangdong China
| | - Ye Li
- Paul C. Lauterbur Research Centre for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China
| | - Xin Liu
- Paul C. Lauterbur Research Centre for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China
| | - Hairong Zheng
- Paul C. Lauterbur Research Centre for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China
| | - Dong Liang
- Paul C. Lauterbur Research Centre for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China
- Research Centre for Medical AI Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China
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