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Zhang HZ, Elsaid NMH, Sun H, Tagare HD, Galiana G. Efficient standardization of clinical T 2-weighted images: Phase-conjugacy e-CAMP with projected gradient descent. Magn Reson Med 2024; 92:2723-2733. [PMID: 38988054 DOI: 10.1002/mrm.30214] [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: 03/07/2024] [Revised: 06/05/2024] [Accepted: 06/21/2024] [Indexed: 07/12/2024]
Abstract
PURPOSE To standardize T 2 $$ {}_2 $$ -weighted images from clinical Turbo Spin Echo (TSE) scans by generating corresponding T 2 $$ {}_2 $$ maps with the goal of removing scanner- and/or protocol-specific heterogeneity. METHODS The T 2 $$ {}_2 $$ map is estimated by minimizing an objective function containing a data fidelity term in a Virtual Conjugate Coils (VCC) framework, where the signal evolution model is expressed as a linear constraint. The objective function is minimized by Projected Gradient Descent (PGD). RESULTS The algorithm achieves accuracy comparable to methods with customized sampling schemes for accelerated T 2 $$ {}_2 $$ mapping. The results are insensitive to the tunable parameters, and the relaxed background phase prior produces better T 2 $$ {}_2 $$ maps compared to the strict real-value enforcement. It is worth noting that the algorithm works well with challenging T 2 $$ {}_2 $$ w-TSE data using typical clinical parameters. The observed normalized root mean square error ranges from 6.8% to 12.3% over grey and white matter, a clinically common level of quantitative map error. CONCLUSION The novel methodological development creates an efficient algorithm that allows for T 2 $$ {}_2 $$ map generated from TSE data with typical clinical parameters, such as high resolution, long echo train length, and low echo spacing. Reconstruction of T 2 $$ {}_2 $$ maps from TSE data with typical clinical parameters has not been previously reported.
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Affiliation(s)
- Horace Z Zhang
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA
| | - Nahla M H Elsaid
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA
| | - Heng Sun
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA
| | - Hemant D Tagare
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA
- Department of Electrical Engineering, Yale University, New Haven, Connecticut, USA
| | - Gigi Galiana
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA
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Chen S, Duan J, Ren X, Wang J, Liu Y. DFUSNN: zero-shot dual-domain fusion unsupervised neural network for parallel MRI reconstruction. Phys Med Biol 2024; 69:105028. [PMID: 38604186 DOI: 10.1088/1361-6560/ad3dbc] [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: 11/23/2023] [Accepted: 04/11/2024] [Indexed: 04/13/2024]
Abstract
Objective. Recently, deep learning models have been used to reconstruct parallel magnetic resonance (MR) images from undersampled k-space data. However, most existing approaches depend on large databases of fully sampled MR data for training, which can be challenging or sometimes infeasible to acquire in certain scenarios. The goal is to develop an effective alternative for improved reconstruction quality that does not rely on external training datasets.Approach. We introduce a novel zero-shot dual-domain fusion unsupervised neural network (DFUSNN) for parallel MR imaging reconstruction without any external training datasets. We employ the Noise2Noise (N2N) network for the reconstruction in the k-space domain, integrate phase and coil sensitivity smoothness priors into the k-space N2N network, and use an early stopping criterion to prevent overfitting. Additionally, we propose a dual-domain fusion method based on Bayesian optimization to enhance reconstruction quality efficiently.Results. Simulation experiments conducted on three datasets with different undersampling patterns showed that the DFUSNN outperforms all other competing unsupervised methods and the one-shot Hankel-k-space generative model (HKGM). The DFUSNN also achieves comparable results to the supervised Deep-SLR method.Significance. The novel DFUSNN model offers a viable solution for reconstructing high-quality MR images without the need for external training datasets, thereby overcoming a major hurdle in scenarios where acquiring fully sampled MR data is difficult.
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Affiliation(s)
- Shengyi Chen
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, People's Republic of China
| | - Jizhong Duan
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, People's Republic of China
| | - Xinmin Ren
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, People's Republic of China
| | - Junfeng Wang
- Department of Hepatobiliary Surgery, First People's Hospital of Yunnan Province, Kunming 650030, People's Republic of China
| | - Yu Liu
- School of Microelectronics, Tianjin University, Tianjin 300072, People's Republic of China
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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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4
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Qiu Z, Hu S, Zhao W, Sakaie K, Sun JE, Griswold MA, Jones DK, Ma D. Self-calibrated subspace reconstruction for multidimensional MR fingerprinting for simultaneous relaxation and diffusion quantification. Magn Reson Med 2024; 91:1978-1993. [PMID: 38102776 PMCID: PMC10950540 DOI: 10.1002/mrm.29969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 11/22/2023] [Accepted: 11/27/2023] [Indexed: 12/17/2023]
Abstract
PURPOSE To propose a new reconstruction method for multidimensional MR fingerprinting (mdMRF) to address shading artifacts caused by physiological motion-induced measurement errors without navigating or gating. METHODS The proposed method comprises two procedures: self-calibration and subspace reconstruction. The first procedure (self-calibration) applies temporally local matrix completion to reconstruct low-resolution images from a subset of under-sampled data extracted from the k-space center. The second procedure (subspace reconstruction) utilizes temporally global subspace reconstruction with pre-estimated temporal subspace from low-resolution images to reconstruct aliasing-free, high-resolution, and time-resolved images. After reconstruction, a customized outlier detection algorithm was employed to automatically detect and remove images corrupted by measurement errors. Feasibility, robustness, and scan efficiency were evaluated through in vivo human brain imaging experiments. RESULTS The proposed method successfully reconstructed aliasing-free, high-resolution, and time-resolved images, where the measurement errors were accurately represented. The corrupted images were automatically and robustly detected and removed. Artifact-free T1, T2, and ADC maps were generated simultaneously. The proposed reconstruction method demonstrated robustness across different scanners, parameter settings, and subjects. A high scan efficiency of less than 20 s per slice has been achieved. CONCLUSION The proposed reconstruction method can effectively alleviate shading artifacts caused by physiological motion-induced measurement errors. It enables simultaneous and artifact-free quantification of T1, T2, and ADC using mdMRF scans without prospective gating, with robustness and high scan efficiency.
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Affiliation(s)
- Zhilang Qiu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States
| | - Siyuan Hu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States
| | - Walter Zhao
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States
| | - Ken Sakaie
- Imaging Institute, Cleveland Clinic, Cleveland, Ohio, United States
| | - Jessie E.P. Sun
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, United States
| | - Mark A. Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, United States
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States
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5
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Liu Y, Liao C, Setsompop K, Haldar JP. The Potential of Phase Constraints for Non-Fourier Radiofrequency-Encoded MRI. IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2024; 10:223-232. [PMID: 39280790 PMCID: PMC11394734 DOI: 10.1109/tci.2024.3361372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/18/2024]
Abstract
In modern magnetic resonance imaging, it is common to use phase constraints to reduce sampling requirements along Fourier-encoded spatial dimensions. In this work, we investigate whether phase constraints might also be beneficial to reduce sampling requirements along spatial dimensions that are measured using non-Fourier encoding techniques, with direct relevance to approaches that use tailored spatially-selective radiofrequency (RF) pulses to perform spatial encoding along the slice dimension in a 3D imaging experiment. In the first part of the paper, we use the Cramér-Rao lower bound to examine the potential estimation theoretic benefits of using phase constraints. The results suggest that phase constraints can be used to improve experimental efficiency and enable acceleration, but only if the RF encoding matrix is complex-valued and appropriately designed. In the second part of the paper, we use simulations of RF-encoded data to test the benefits of phase constraints combined with optimized RF-encodings, and find that the theoretical benefits are indeed borne out empirically. These results provide new insights into the potential benefits of phase constraints for RF-encoded data, and provide a solid theoretical foundation for future practical explorations.
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Affiliation(s)
- Yunsong Liu
- Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089
| | - Congyu Liao
- Departments of Radiology and Electrical Engineering, Stanford University, Stanford, CA 94305, USA
| | - Kawin Setsompop
- Departments of Radiology and Electrical Engineering, Stanford University, Stanford, CA 94305, USA
| | - Justin P Haldar
- Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089
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6
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Dong Y, Koolstra K, Li Z, Riedel M, van Osch MJP, Börnert P. Structured low-rank reconstruction for navigator-free water/fat separated multi-shot diffusion-weighted EPI. Magn Reson Med 2024; 91:205-220. [PMID: 37753595 DOI: 10.1002/mrm.29848] [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: 02/22/2023] [Revised: 07/20/2023] [Accepted: 08/11/2023] [Indexed: 09/28/2023]
Abstract
PURPOSE Multi-shot diffusion-weighted EPI allows an increase in image resolution and reduced geometric distortions and can be combined with chemical-shift encoding (Dixon) to separate water/fat signals. However, such approaches suffer from physiological motion-induced shot-to-shot phase variations. In this work, a structured low-rank-based navigator-free algorithm is proposed to address the challenge of simultaneously separating water/fat signals and correcting for physiological motion-induced shot-to-shot phase variations in multi-shot EPI-based diffusion-weighted MRI. THEORY AND METHODS We propose an iterative, model-based reconstruction pipeline that applies structured low-rank regularization to estimate and eliminate the shot-to-shot phase variations in a data-driven way, while separating water/fat images. The algorithm is tested in different anatomies, including head-neck, knee, brain, and prostate. The performance is validated in simulations and in-vivo experiments in comparison to existing approaches. RESULTS In-vivo experiments and simulations demonstrated the effectiveness of the proposed algorithm compared to extra-navigated and an alternative self-navigation approach. The proposed algorithm demonstrates the capability to reconstruct in the multi-shot/Dixon hybrid space domain under-sampled datasets, using the same number of acquired EPI shots compared to conventional fat-suppression techniques but eliminating fat signals through chemical-shift encoding. In addition, partial Fourier reconstruction can also be achieved by using the concept of virtual conjugate coils in conjunction with the proposed algorithm. CONCLUSION The proposed algorithm effectively eliminates the shot-to-shot phase variations and separates water/fat images, making it a promising solution for future DWI on different anatomies.
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Affiliation(s)
- Yiming Dong
- C.J. Gorter MRI Center, Department of Radiology, LUMC, Leiden, The Netherlands
| | | | - Ziyu Li
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | | | | | - Peter Börnert
- C.J. Gorter MRI Center, Department of Radiology, LUMC, Leiden, The Netherlands
- Philips Research Hamburg, Hamburg, Germany
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7
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Liu C, Cui ZX, Jia S, Cheng J, Cao C, Guo Y, Zhu Y, Liang D, Wang H. Accelerated submillimeter wave-encoded magnetic resonance imaging via deep untrained neural network. Med Phys 2023; 50:7684-7699. [PMID: 37073772 DOI: 10.1002/mp.16425] [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: 11/28/2022] [Revised: 03/20/2023] [Accepted: 03/31/2023] [Indexed: 04/20/2023] Open
Abstract
BACKGROUND Wave gradient encoding can adequately utilize coil sensitivity profiles to facilitate higher accelerations in parallel magnetic resonance imaging (pMRI). However, there are limitations in mainstream pMRI and a few deep learning (DL) methods for recovering missing data under wave encoding framework: the former is prone to introduce errors from the auto-calibration signals (ACS) signal acquisition and is time-consuming, while the latter requires a large amount of training data. PURPOSE To tackle the above issues, an untrained neural network (UNN) model incorporating wave-encoded physical properties and deep generative model, named WDGM, was proposed with additional ACS- and training data-free. METHODS Generally, the proposed method can provide powerful missing data interpolation capability using the wave physical encoding framework and designed UNN to characterize the MR image (k-space data) priors. Specifically, the MRI reconstruction combining physical wave encoding and elaborate UNN is modeled as a generalized minimization problem. The designation of UNN is driven by the coil sensitivity maps (CSM) smoothness and k-space linear predictability. And then, the iterative paradigm to recover the full k-space signal is determined by the projected gradient descent, and the complex computation is unrolled to the network with optimized parameters by the optimizer. Simulated wave encoding and in vivo experiments are exploited to demonstrate the feasibility of the proposed method. The best quantitative metrics RMSE/SSIM/PSNR of 0.0413, 0.9514, and 37.4862 gave competitive results in all experiments with at least six-fold acceleration, respectively. RESULTS In vivo experiments of human brains and knees showed that the proposed method can achieve comparable reconstruction quality and even has superiority relative to the comparison, especially at a high resolution of 0.67 mm and fewer ACS. In addition, the proposed method has a higher computational efficiency achieving a computation time of 9.6 s/per slice. CONCLUSIONS The model proposed in this work addresses two limitations of MRI reconstruction in the wave encoding framework. The first is to eliminate the need for ACS signal acquisition to perform the time-consuming calibration process and to avoid errors such as motion during the acquisition procedure. Furthermore, the proposed method has clinical application friendly without the need to prepare large training datasets, which is difficult in the clinical. All results of the proposed method demonstrate more confidence in both quantitative and qualitative metrics. In addition, the proposed method can achieve higher computational efficiency.
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Affiliation(s)
- Congcong Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhuo-Xu Cui
- Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Sen Jia
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jing Cheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Chentao Cao
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yifan Guo
- Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yanjie Zhu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
- Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Haifeng Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
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Liao C, Yarach U, Cao X, Iyer SS, Wang N, Kim TH, Tian Q, Bilgic B, Kerr AB, Setsompop K. High-fidelity mesoscale in-vivo diffusion MRI through gSlider-BUDA and circular EPI with S-LORAKS reconstruction. Neuroimage 2023; 275:120168. [PMID: 37187364 PMCID: PMC10451786 DOI: 10.1016/j.neuroimage.2023.120168] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/27/2023] [Accepted: 05/12/2023] [Indexed: 05/17/2023] Open
Abstract
PURPOSE To develop a high-fidelity diffusion MRI acquisition and reconstruction framework with reduced echo-train-length for less T2* image blurring compared to typical highly accelerated echo-planar imaging (EPI) acquisitions at sub-millimeter isotropic resolution. METHODS We first proposed a circular-EPI trajectory with partial Fourier sampling on both the readout and phase-encoding directions to minimize the echo-train-length and echo time. We then utilized this trajectory in an interleaved two-shot EPI acquisition with reversed phase-encoding polarity, to aid in the correction of off-resonance-induced image distortions and provide complementary k-space coverage in the missing partial Fourier regions. Using model-based reconstruction with structured low-rank constraint and smooth phase prior, we corrected the shot-to-shot phase variations across the two shots and recover the missing k-space data. Finally, we combined the proposed acquisition/reconstruction framework with an SNR-efficient RF-encoded simultaneous multi-slab technique, termed gSlider, to achieve high-fidelity 720 µm and 500 µm isotropic resolution in-vivo diffusion MRI. RESULTS Both simulation and in-vivo results demonstrate the effectiveness of the proposed acquisition and reconstruction framework to provide distortion-corrected diffusion imaging at the mesoscale with markedly reduced T2*-blurring. The in-vivo results of 720 µm and 500 µm datasets show high-fidelity diffusion images with reduced image blurring and echo time using the proposed approaches. CONCLUSIONS The proposed method provides high-quality distortion-corrected diffusion-weighted images with ∼40% reduction in the echo-train-length and T2* blurring at 500µm-isotropic-resolution compared to standard multi-shot EPI.
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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
| | - Uten Yarach
- Radiologic Technology Department, Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand
| | - Xiaozhi Cao
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
| | - Siddharth Srinivasan Iyer
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nan Wang
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Tae Hyung Kim
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Department of Computer Engineering, Hongik University, Seoul, South Korea
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Adam B 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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Cui ZX, Jia S, Cao C, Zhu Q, Liu C, Qiu Z, Liu Y, Cheng J, Wang H, Zhu Y, Liang D. K-UNN: k-space interpolation with untrained neural network. Med Image Anal 2023; 88:102877. [PMID: 37399681 DOI: 10.1016/j.media.2023.102877] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 05/24/2023] [Accepted: 06/22/2023] [Indexed: 07/05/2023]
Abstract
Recently, untrained neural networks (UNNs) have shown satisfactory performances for MR image reconstruction on random sampling trajectories without using additional full-sampled training data. However, the existing UNN-based approaches lack the modeling of physical priors, resulting in poor performance in some common scenarios (e.g., partial Fourier (PF), regular sampling, etc.) and the lack of theoretical guarantees for reconstruction accuracy. To bridge this gap, we propose a safeguarded k-space interpolation method for MRI using a specially designed UNN with a tripled architecture driven by three physical priors of the MR images (or k-space data), including transform sparsity, coil sensitivity smoothness, and phase smoothness. We also prove that the proposed method guarantees tight bounds for interpolated k-space data accuracy. Finally, ablation experiments show that the proposed method can characterize the physical priors of MR images well. Additionally, experiments show that the proposed method consistently outperforms traditional parallel imaging methods and existing UNNs, and is even competitive against supervised-trained deep learning methods in PF and regular undersampling reconstruction.
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Affiliation(s)
- Zhuo-Xu Cui
- Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Sen Jia
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Chentao Cao
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Qingyong Zhu
- Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Congcong Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhilang Qiu
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Yuanyuan Liu
- National Innovation Center for Advanced Medical Devices, Shenzhen, Guangdong, China
| | - Jing Cheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Haifeng Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yanjie Zhu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Dong Liang
- Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Pazhou Lab, Guangzhou, Guangdong, China.
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10
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Feizollah S, Tardif CL. High-resolution diffusion-weighted imaging at 7 Tesla: single-shot readout trajectories and their impact on signal-to-noise ratio, spatial resolution and accuracy. Neuroimage 2023; 274:120159. [PMID: 37150332 DOI: 10.1016/j.neuroimage.2023.120159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/31/2023] [Accepted: 05/04/2023] [Indexed: 05/09/2023] Open
Abstract
Diffusion MRI (dMRI) is a valuable imaging technique to study the connectivity and microstructure of the brain in vivo. However, the resolution of dMRI is limited by the low signal-to-noise ratio (SNR) of this technique. Various multi-shot acquisition strategies have been developed to achieve sub-millimeter resolution, but they require long scan times which can be restricting for patient scans. Alternatively, the SNR of single-shot acquisitions can be increased by using a spiral readout trajectory to minimize the sequence echo time. Imaging at ultra-high fields (UHF) could further increase the SNR of single-shot dMRI; however, the shorter T2* of brain tissue and the greater field non-uniformities at UHFs will degrade image quality, causing image blurring, distortions, and signal loss. In this study, we investigated the trade-off between the SNR and resolution of different k-space trajectories, including echo planar imaging (EPI), partial Fourier EPI, and spiral trajectories, over a range of dMRI resolutions at 7T. The effective resolution, spatial specificity and sharpening effect were measured from the point spread function (PSF) of the simulated diffusion sequences for a nominal resolution range of 0.6-1.8 mm. In-vivo partial brain scans at a nominal resolution of 1.5 mm isotropic were acquired using the three readout trajectories to validate the simulation results. Field probes were used to measure dynamic magnetic fields offline up to the 3rd order of spherical harmonics. Image reconstruction was performed using static ΔB0 field maps and the measured trajectories to correct image distortions and artifacts, leaving T2* effects as the primary source of blurring. The effective resolution was examined in fractional anisotropy (FA) maps calculated from a multi-shell dataset with b-values of 300, 1000, and 2000 s/mm2 in 5, 16, and 48 directions, respectively. In-vivo scans at nominal resolutions of 1, 1.2, and 1.5 mm were acquired and the SNR of the different trajectories calculated using the multiple replica method to investigate the SNR. Finally, in-vivo whole brain scans with an effective resolution of 1.5 mm isotropic were acquired to explore the SNR and efficiency of different trajectories at a matching effective resolution. FA and intra-cellular volume fraction (ICVF) maps calculated using neurite orientation dispersion and density imaging (NODDI) were used for the comparison. The simulations and in vivo imaging results showed that for matching nominal resolutions, EPI trajectories had the highest specificity and effective resolution with maximum image sharpening effect. However, spirals have a significantly higher SNR, in particular at higher resolutions and even when the effective image resolutions are matched. Overall, this work shows that the higher SNR of single-shot spiral trajectories at 7T allows us to achieve higher effective resolutions compared to EPI and PF-EPI to map the microstructure and connectivity of small brain structures.
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Affiliation(s)
- Sajjad Feizollah
- Department of Neurology and Neurosurgery, Faculty of Medicine and Health Sciences, McGill University, 3801 Rue University, Montreal, QC, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, QC, Canada.
| | - Christine L Tardif
- Department of Neurology and Neurosurgery, Faculty of Medicine and Health Sciences, McGill University, 3801 Rue University, Montreal, QC, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, QC, Canada; Department of Biomedical Engineering, Faculty of Medicine and Health Sciences, McGill University, Duff Medical Building, 3775 Rue University, Suite 316, Montreal, QC, Canada.
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11
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Tao H, Zhang W, Wang H, Wang S, Liang D, Xu X, Liu Q. Multi-weight respecification of scan-specific learning for parallel imaging. Magn Reson Imaging 2023; 97:1-12. [PMID: 36567001 DOI: 10.1016/j.mri.2022.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 12/14/2022] [Accepted: 12/17/2022] [Indexed: 12/24/2022]
Abstract
Parallel imaging is widely used in magnetic resonance imaging as an acceleration technology. Traditional linear reconstruction methods in parallel imaging often suffer from noise amplification. Recently, a non-linear robust artificial-neural-network for k-space interpolation (RAKI) exhibits superior noise resilience over other linear methods. However, RAKI performs poorly at high acceleration rates and needs a large number of autocalibration signals as the training samples. In order to tackle these issues, we propose a multi-weight method that implements multiple weighting matrices on the under-sampled data, named MW-RAKI. Enforcing multiple weighted matrices on the measurements can effectively reduce the influence of noise and increase the data constraints. Furthermore, we incorporate the strategy of multiple weighting matrixes into a residual version of RAKI, and form MW-rRAKI. Experimental comparisons with the alternative methods demonstrated noticeably better reconstruction performances, particularly at high acceleration rates. With only 12.5% of the k-space data is available, the PSNR of MW-RAKI and MW-rRAKI is improved by about 3 dB and 4 dB compared to RAKI and rRAKI, respectively.
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Affiliation(s)
- Hui Tao
- Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China
| | - Wei Zhang
- Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China
| | - Haifeng Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Shanshan Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Medical AI Research Center, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Xiaoling Xu
- Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China
| | - Qiegen Liu
- Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China.
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12
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Dawood P, Breuer F, Stebani J, Burd P, Homolya I, Oberberger J, Jakob PM, Blaimer M. Iterative training of robust k-space interpolation networks for improved image reconstruction with limited scan specific training samples. Magn Reson Med 2023; 89:812-827. [PMID: 36226661 DOI: 10.1002/mrm.29482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 09/12/2022] [Accepted: 09/20/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE To evaluate an iterative learning approach for enhanced performance of robust artificial-neural-networks for k-space interpolation (RAKI), when only a limited amount of training data (auto-calibration signals [ACS]) are available for accelerated standard 2D imaging. METHODS In a first step, the RAKI model was tailored for the case of limited training data amount. In the iterative learning approach (termed iterative RAKI [iRAKI]), the tailored RAKI model is initially trained using original and augmented ACS obtained from a linear parallel imaging reconstruction. Subsequently, the RAKI convolution filters are refined iteratively using original and augmented ACS extracted from the previous RAKI reconstruction. Evaluation was carried out on 200 retrospectively undersampled in vivo datasets from the fastMRI neuro database with different contrast settings. RESULTS For limited training data (18 and 22 ACS lines for R = 4 and R = 5, respectively), iRAKI outperforms standard RAKI by reducing residual artifacts and yields better noise suppression when compared to standard parallel imaging, underlined by quantitative reconstruction quality metrics. Additionally, iRAKI shows better performance than both GRAPPA and standard RAKI in case of pre-scan calibration with varying contrast between training- and undersampled data. CONCLUSION RAKI benefits from the iterative learning approach, which preserves the noise suppression feature, but requires less original training data for the accurate reconstruction of standard 2D images thereby improving net acceleration.
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Affiliation(s)
- Peter Dawood
- Department of Physics, University of Würzburg, Würzburg, Germany
| | - Felix Breuer
- Magnetic Resonance and X-ray Imaging Department, Fraunhofer Institute for Integrated Circuits IIS, Division Development Center X-Ray Technology, Würzburg, Germany
| | - Jannik Stebani
- Magnetic Resonance and X-ray Imaging Department, Fraunhofer Institute for Integrated Circuits IIS, Division Development Center X-Ray Technology, Würzburg, Germany
| | - Paul Burd
- Institute for Theoretical Physics and Astrophysics, University of Würzburg, Würzburg, Germany
| | - István Homolya
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, Hungary
| | - Johannes Oberberger
- Department of Internal Medicine I, University Hospital Würzburg, Würzburg, Germany
| | - Peter M Jakob
- Department of Physics, University of Würzburg, Würzburg, Germany
| | - Martin Blaimer
- Magnetic Resonance and X-ray Imaging Department, Fraunhofer Institute for Integrated Circuits IIS, Division Development Center X-Ray Technology, Würzburg, Germany
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13
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Dai E, Mani M, McNab JA. Multi-band multi-shot diffusion MRI reconstruction with joint usage of structured low-rank constraints and explicit phase mapping. Magn Reson Med 2023; 89:95-111. [PMID: 36063492 PMCID: PMC9887994 DOI: 10.1002/mrm.29422] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 08/01/2022] [Accepted: 08/02/2022] [Indexed: 02/02/2023]
Abstract
PURPOSE To develop a joint reconstruction method for multi-band multi-shot diffusion MRI. THEORY AND METHODS Multi-band multi-shot EPI acquisition is an effective approach for high-resolution diffusion MRI, but requires specific algorithms to correct the inter-shot phase variations. The phase correction can be done by first estimating the explicit phase map and then feeding it into the k-space signal formulation model. Alternatively, the phase information can be used indirectly as structured low-rank constraints in k-space. The 2 methods differ in reconstruction accuracy and efficiency. We aim to combine the 2 different approaches for improved image quality and reconstruction efficiency simultaneously, termed "joint usage of structured low-rank constraints and explicit phase mapping" (JULEP). The proposed JULEP reconstruction is tested on both single-band and multi-band, multi-shot diffusion data, with different resolutions and b values. The results of JULEP are compared with conventional methods with explicit phase mapping (i.e., multiplexed sensitivity-encoding [MUSE]) and structured low-rank constraints (i.e., MUSSELS), and another joint reconstruction method (i.e., network estimated artifacts for tempered reconstruction [NEATR]). RESULTS JULEP improves the quality of the navigator and subsequently facilitates the reconstruction of final diffusion images. Compared with all 3 other methods (MUSE, MUSSELS, and NEATR), JULEP mitigates residual structural bias and improves temporal SNRs in the final diffusion image, particularly at high multi-band factors. Compared with MUSSELS, JULEP also improves computational efficiency. CONCLUSION The proposed JULEP method significantly improves the image quality and reconstruction efficiency of multi-band multi-shot diffusion MRI, which can promote a broader application of high-resolution diffusion MRI.
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Affiliation(s)
- Erpeng Dai
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Merry Mani
- Department of Radiology, University of Iowa, Iowa City, IA, United States
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, United States
| | - Jennifer A McNab
- Department of Radiology, Stanford University, Stanford, CA, United States
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14
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Yang C, Liao X, Zhang L, Zhang M, Liu Q. Virtual coil augmentation for MR coil extrapoltion via deep learning. Magn Reson Imaging 2023; 95:1-11. [PMID: 36241031 DOI: 10.1016/j.mri.2022.10.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 09/29/2022] [Accepted: 10/01/2022] [Indexed: 11/07/2022]
Abstract
Magnetic resonance imaging (MRI) is a widely used medical imaging modality. However, due to the limitations in hardware, scan time, and throughput, it is often clinically challenging to obtain high-quality MR images. In this article, we propose a method of using artificial intelligence to expand the coils to achieve the goal of generating the virtual coils. The main characteristic of our work is utilizing dummy variable technology to expand/extrapolate the receive coils in both image and k-space domains. The high-dimensional information formed by coil expansion is used as the prior information to improve the reconstruction performance of parallel imaging. Two main components are incorporated into the network design, namely variable augmentation technology and sum of squares (SOS) objective function. Variable augmentation provides the network with more high-dimensional prior information, which is helpful for the network to extract the deep feature information of the data. The SOS objective function is employed to solve the deficiency of k-space data training while speeding up convergence. Experimental results demonstrated its great potentials in accelerating parallel imaging reconstruction.
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Affiliation(s)
- Cailian Yang
- Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China
| | - Xianghao Liao
- Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China
| | - Liu Zhang
- Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China
| | - Minghui Zhang
- Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China
| | - Qiegen Liu
- Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China.
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15
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Luo H, Sigona MK, Manuel TJ, Phipps MA, Chen LM, Caskey CF, Grissom WA. Reduced-field of view three-dimensional MR acoustic radiation force imaging with a low-rank reconstruction for targeting transcranial focused ultrasound. Magn Reson Med 2022; 88:2419-2431. [PMID: 35916311 PMCID: PMC9529839 DOI: 10.1002/mrm.29403] [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: 02/28/2022] [Revised: 06/14/2022] [Accepted: 07/12/2022] [Indexed: 11/11/2022]
Abstract
PURPOSE To rapidly image and localize the focus in MR-guided focused ultrasound (FUS) while maintaining a low ultrasound duty cycle to minimize tissue effects. METHODS MR-acoustic radiation force imaging (ARFI) is key to targeting FUS procedures such as neuromodulation, and works by encoding ultrasound-induced displacements into the phase of MR images. However, it can require long scan times to cover a volume of tissue, especially when minimizing the FUS dose during targeting is paramount. To simultaneously minimize scan time and the FUS duty cycle, a 2-min three-dimensional (3D) reduced-FOV spin echo ARFI scan with two-dimensional undersampling was implemented at 3T with a FUS duty cycle of 0.85%. The 3D k-space sampling scheme incorporated uniform undersampling in one phase-encoded axis and partial Fourier (PF) sampling in the other. The scan interleaved FUS-on and FUS-off data collection to improve displacement map quality via a joint low-rank image reconstruction. Experiments in agarose and graphite phantoms and living macaque brains for neuromodulation and blood-brain barrier opening studied the effects of the sampling and reconstruction strategy on the acquisition, and evaluated its repeatability and accuracy. RESULTS In the phantom, the distances between displacement centroids of 10 prospective reconstructions and a fully sampled reference were below 1 mm. In in vivo brain, the distances between centroids ranged from 1.3 to 2.1 mm. Results in phantom and in vivo brain both showed that the proposed method can recover the FUS focus compared to slower fully sampled scans. CONCLUSION The proposed 3D MR-ARFI reduced-FOV method enables rapid imaging of the FUS focus while maintaining a low FUS duty cycle.
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Affiliation(s)
- Huiwen Luo
- Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Michelle K Sigona
- Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Thomas J Manuel
- Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Marshal A Phipps
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Li M Chen
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Charles F Caskey
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - William A Grissom
- Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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16
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Lee PK, Hargreaves BA. A joint linear reconstruction for multishot diffusion weighted non-Carr-Purcell-Meiboom-Gill fast spin echo with full signal. Magn Reson Med 2022; 88:2139-2156. [PMID: 35906924 PMCID: PMC9732866 DOI: 10.1002/mrm.29393] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 06/25/2022] [Accepted: 06/27/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE Diffusion weighted Fast Spin Echo (DW-FSE) is a promising approach for distortionless DW imaging that is robust to system imperfections such as eddy currents and off-resonance. Due to non-Carr-Purcell-Meiboom-Gill (CPMG) magnetization, most DW-FSE sequences discard a large fraction of the signal (2 - 2 × $$ \sqrt{2}-2\times $$ ), reducing signal-to-noise ratio (SNR) efficiency compared to DW-EPI. The full FSE signal can be preserved by quadratically incrementing the transmit phase of the refocusing pulses, but this method of resolving non-CPMG magnetization has only been applied to single-shot DW-FSE due to challenges associated with image reconstruction. We present a joint linear reconstruction for multishot quadratic phase increment data that addresses these challenges and corrects ghosting from both shot-to-shot phase and intrashot signal oscillations. Multishot imaging reduces T2 blur and joint reconstruction of shots improves g-factor performance. A thorough analysis on the condition number of the proposed linear system is described. METHODS A joint multishot reconstruction is derived from the non-CPMG signal model. Multishot quadratic phase increment DW-FSE was tested in a standardized diffusion phantom and compared to single-shot DW-FSE and DW-EPI in vivo in the brain, cervical spine, and prostate. The pseudo multiple replica technique was applied to generate g-factor and SNR maps. RESULTS The proposed joint shot reconstruction eliminates ghosting from shot-to-shot phase and intrashot oscillations. g-factor performance is improved compared to previously proposed reconstructions, permitting efficient multishot imaging. apparent diffusion coefficient estimates in phantom experiments and in vivo are comparable to those obtained with conventional methods. CONCLUSION Multi-shot non-CPMG DW-FSE data with full signal can be jointly reconstructed using a linear model.
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Affiliation(s)
- Philip K. Lee
- Radiology, Stanford University, Stanford, CA, 94305, USA
- Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Brian A. Hargreaves
- Radiology, Stanford University, Stanford, CA, 94305, USA
- Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
- Bioengineering, Stanford University, Stanford, CA, 94305, USA
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Ryu K, Alkan C, Vasanawala SS. Improving high frequency image features of deep learning reconstructions via k-space refinement with null-space kernel. Magn Reson Med 2022; 88:1263-1272. [PMID: 35426470 PMCID: PMC9246879 DOI: 10.1002/mrm.29261] [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: 12/15/2021] [Revised: 03/20/2022] [Accepted: 03/21/2022] [Indexed: 11/12/2022]
Abstract
PURPOSE Deep learning (DL) based reconstruction using unrolled neural networks has shown great potential in accelerating MRI. However, one of the major drawbacks is the loss of high-frequency details and textures in the output. The purpose of the study is to propose a novel refinement method that uses null-space kernel to refine k-space and improve blurred image details and textures. METHODS The proposed method constrains the output of the DL to comply to the linear neighborhood relationship calibrated in the auto-calibration lines. To demonstrate efficacy, we tested our refinement method on the DL reconstruction under a variety of conditions (i.e., dataset, unrolled neural networks, and under-sampling scheme). Specifically, the method was tested on three large-scale public datasets (knee and brain) from fastMRI's multi-coil track. RESULTS The proposed scheme visually reduces the structural error in the k-space domain, enhance the homogeneity of the k-space intensity. Consequently, reconstructed image shows sharper images with enhanced details and textures. The proposed method is also successful in improving high-frequency image details (SSIM, GMSD) without sacrificing overall image error (PSNR). CONCLUSION Our findings imply that refining DL output using the proposed method may generally improve DL reconstruction as tested with various large-scale dataset and networks.
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Affiliation(s)
- Kanghyun Ryu
- Department of Radiology, Stanford University, CA, USA
| | - Cagan Alkan
- Department of Electrical Engineering, Stanford University, CA, USA
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18
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Zhang X, Lu H, Guo D, Lai Z, Ye H, Peng X, Zhao B, Qu X. Accelerated MRI Reconstruction With Separable and Enhanced Low-Rank Hankel Regularization. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2486-2498. [PMID: 35377841 DOI: 10.1109/tmi.2022.3164472] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Magnetic resonance imaging serves as an essential tool for clinical diagnosis, however, suffers from a long acquisition time. Sparse sampling effectively saves this time but images need to be faithfully reconstructed from undersampled data. Among the existing reconstruction methods, the structured low-rank methods have advantages in robustness to the sampling patterns and lower error. However, the structured low-rank methods use the 2D or higher dimension k-space data to build a huge block Hankel matrix, leading to considerable time and memory consumption. To reduce the size of the Hankel matrix, we proposed to separably construct multiple small Hankel matrices from rows and columns of the k-space and then constrain the low-rankness on these small matrices. This separable model can significantly reduce the computational time but ignores the correlation existed in inter- and intra-row or column, resulting in increased reconstruction error. To improve the reconstructed image without obviously increasing the computation, we further introduced the self-consistency of k-space and virtual coil prior. Besides, the proposed separable model can be extended into other imaging scenarios which hold exponential characteristics in the parameter dimension. The in vivo experimental results demonstrated that the proposed method permits the lowest reconstruction error with a fast reconstruction. The proposed approach requires only 4% of the state-of-the-art STDLR-SPIRiT runtime for parallel imaging reconstruction, and achieves the fastest computational speed in parameter imaging reconstruction.
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19
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Chang Y, Zhang J, Pham HA, Li Z, Lyu J. Virtual Conjugate Coil for Improving KerNL Reconstruction. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:599-602. [PMID: 36085691 DOI: 10.1109/embc48229.2022.9871764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Ker NL is a general kernel-based framework for auto calibrated reconstruction method, which does not need any explicit formulas of the kernel function for characterizing nonlinear relationships between acquired and unacquired k-space data. It is non-iterative without requiring a large amount of computational costs. Since the limited autocalibration signals (ACS) are acquired to perform KerNL calibration and the calibration suffers from the overfitting problem, more training data can improve the kernel model accuracy. In this work, virtual conjugate coil data are incorporated into the KerNL calibration and estimation process for enhancing reconstruction performance. Experimental results show that the proposed method can further suppress noise and aliasing artifacts with fewer ACS data and higher acceleration factors. Computation efficiency is still retained to keep fast reconstruction with the random projection.
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20
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Lee JH, Yi J, Kim JH, Ryu K, Han D, Kim S, Lee S, Kim DY, Kim DH. Accelerated 3D myelin water imaging using joint spatio-temporal reconstruction. Med Phys 2022; 49:5929-5942. [PMID: 35678751 DOI: 10.1002/mp.15788] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 03/31/2022] [Accepted: 05/26/2022] [Indexed: 11/08/2022] Open
Abstract
PURPOSE To enable acceleration in 3D multi-echo gradient echo (mGRE) acquisition for myelin water imaging (MWI) by combining joint parallel imaging (JPI) and joint deep learning (JDL). METHODS We implemented a multistep reconstruction process using both advanced parallel imaging and deep learning network which can utilize joint spatiotemporal components between the multi-echo images to further accelerate 3D mGRE acquisition for MWI. In the first step, JPI was performed to estimate missing k-space lines. Next, JDL was implemented to reduce residual artifacts and produce high-fidelity reconstruction by using variable splitting optimization consisting of spatiotemporal denoiser block, data consistency block, and weighted average block. The proposed method was evaluated for MWI with 2D Cartesian uniform under-sampling for each echo, enabling scan times of up to approximately 2 min for 2 mm × 2 mm × 2 mm $2\ {\rm mm} \times 2\ {\rm mm} \times 2\ {\rm mm}$ 3D coverage. RESULTS The proposed method showed acceptable MWI quality with improved quantitative values compared to both JPI and JDL methods individually. The improved performance of the proposed method was demonstrated by the low normalized mean-square error and high-frequency error norm values of the reconstruction with high similarity to the fully sampled MWI. CONCLUSION Joint spatiotemporal reconstruction approach by combining JPI and JDL can achieve high acceleration factors for 3D mGRE-based MWI.
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Affiliation(s)
- Jae-Hun Lee
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Jaeuk Yi
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Jun-Hyeong Kim
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Kanghyun Ryu
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.,Department of Radiology, Stanford University, Stanford, California, USA
| | - Dongyeob Han
- Siemens Healthineers Ltd, Seoul, Republic of Korea
| | - Sewook Kim
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Seul Lee
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Deog Young Kim
- Department of Research Institute of Rehabilitation Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Dong-Hyun Kim
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
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Arefeen Y, Beker O, Cho J, Yu H, Adalsteinsson E, Bilgic B. Scan-specific artifact reduction in k-space (SPARK) neural networks synergize with physics-based reconstruction to accelerate MRI. Magn Reson Med 2022; 87:764-780. [PMID: 34601751 PMCID: PMC8627503 DOI: 10.1002/mrm.29036] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 09/19/2021] [Accepted: 09/20/2021] [Indexed: 02/03/2023]
Abstract
PURPOSE To develop a scan-specific model that estimates and corrects k-space errors made when reconstructing accelerated MRI data. METHODS Scan-specific artifact reduction in k-space (SPARK) trains a convolutional-neural-network to estimate and correct k-space errors made by an input reconstruction technique by back-propagating from the mean-squared-error loss between an auto-calibration signal (ACS) and the input technique's reconstructed ACS. First, SPARK is applied to generalized autocalibrating partially parallel acquisitions (GRAPPA) and demonstrates improved robustness over other scan-specific models, such as robust artificial-neural-networks for k-space interpolation (RAKI) and residual-RAKI. Subsequent experiments demonstrate that SPARK synergizes with residual-RAKI to improve reconstruction performance. SPARK also improves reconstruction quality when applied to advanced acquisition and reconstruction techniques like 2D virtual coil (VC-) GRAPPA, 2D LORAKS, 3D GRAPPA without an integrated ACS region, and 2D/3D wave-encoded imaging. RESULTS SPARK yields SSIM improvement and 1.5 - 2× root mean squared error (RMSE) reduction when applied to GRAPPA and improves robustness to ACS size for various acceleration rates in comparison to other scan-specific techniques. When applied to advanced reconstruction techniques such as residual-RAKI, 2D VC-GRAPPA and LORAKS, SPARK achieves up to 20% RMSE improvement. SPARK with 3D GRAPPA also improves RMSE performance by ~2×, SSIM performance, and perceived image quality without a fully sampled ACS region. Finally, SPARK synergizes with non-Cartesian, 2D and 3D wave-encoding imaging by reducing RMSE between 20% and 25% and providing qualitative improvements. CONCLUSION SPARK synergizes with physics-based acquisition and reconstruction techniques to improve accelerated MRI by training scan-specific models to estimate and correct reconstruction errors in k-space.
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Affiliation(s)
- Yamin Arefeen
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Onur Beker
- Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Jaejin Cho
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Heng Yu
- Department of Automation, Tsinghua University, Beijing, China
| | - Elfar Adalsteinsson
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
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22
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Yu FF, Yi Huang S, Kumar A, Witzel T, Liao C, Duval T, Cohen-Adad J, Bilgic B. Rapid simultaneous acquisition of macromolecular tissue volume, susceptibility, and relaxometry maps. Magn Reson Med 2022; 87:781-790. [PMID: 34480768 PMCID: PMC8627440 DOI: 10.1002/mrm.28995] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Revised: 07/13/2021] [Accepted: 08/10/2021] [Indexed: 02/03/2023]
Abstract
PURPOSE A major obstacle to the clinical implementation of quantitative MR is the lengthy acquisition time required to derive multi-contrast parametric maps. We sought to reduce the acquisition time for QSM and macromolecular tissue volume by acquiring both contrasts simultaneously by leveraging their redundancies. The joint virtual coil concept with GRAPPA (JVC-GRAPPA) was applied to reduce acquisition time further. METHODS Three adult volunteers were imaged on a 3 Tesla scanner using a multi-echo 3D GRE sequence acquired at 3 head orientations. Macromolecular tissue volume, QSM, R2∗ , T1 , and proton density maps were reconstructed. The same sequence (GRAPPA R = 4) was performed in subject 1 with a single head orientation for comparison. Fully sampled data was acquired in subject 2, from which retrospective undersampling was performed (R = 6 GRAPPA and R = 9 JVC-GRAPPA). Prospective undersampling was performed in subject 3 (R = 6 GRAPPA and R = 9 JVC-GRAPPA) using gradient blips to shift k-space sampling in later echoes. RESULTS Subject 1's multi-orientation and single-orientation macromolecular tissue volume maps were not significantly different based on RMSE. For subject 2, the retrospectively undersampled JVC-GRAPPA and GRAPPA generated similar results as fully sampled data. This approach was validated with the prospectively undersampled images in subject 3. Using QSM, R2∗ , and macromolecular tissue volume, the contributions of myelin and iron content to susceptibility were estimated. CONCLUSION We have developed a novel strategy to simultaneously acquire data for the reconstruction of 5 intrinsically coregistered 1-mm isotropic resolution multi-parametric maps, with a scan time of 6 min using JVC-GRAPPA.
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Affiliation(s)
- Fang Frank Yu
- Radiology, University of Texas Southwestern Medical Center, Dallas, TX, United States,,Corresponding author. Fang Frank Yu, MD, UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, Ph: 214-648-7813, Fax: 214-648-3904,
| | - Susie Yi Huang
- Department of Radiology, Harvard Medical School, Boston, MA, United States,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
| | - Ashwin Kumar
- Vanderbilt University, Nashville, TN, United States
| | | | - Congyu Liao
- Radiological Sciences Laboratory, Stanford Medicine, Stanford, CA, United States
| | - Tanguy Duval
- Institute of Biomedical Engineering, Ecole Polytechnique de Montreal, Montreal, QC, Canada
| | - Julien Cohen-Adad
- Institute of Biomedical Engineering, Ecole Polytechnique de Montreal, Montreal, QC, Canada
| | - Berkin Bilgic
- Department of Radiology, Harvard Medical School, Boston, MA, United States,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
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23
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Chang Y, Saritac M. Group feature selection for enhancing information gain in MRI reconstruction. Phys Med Biol 2021; 67. [PMID: 34933300 DOI: 10.1088/1361-6560/ac4561] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 12/21/2021] [Indexed: 11/12/2022]
Abstract
Magnetic resonance imaging (MRI) has revolutionized the radiology. As a leading medical imaging modality, MRI not only visualizes the structures inside body, but also produces functional imaging. However, due to the slow imaging speed constrained by the MR physics, MRI cost is expensive, and patient may feel not comfortable in a scanner for a long time. Parallel MRI has accelerated the imaging speed through the sub-Nyquist sampling strategy and the missing data are interpolated by the multiple coil data acquired. Kernel learning has been used in the parallel MRI reconstruction to learn the interpolation weights and re-construct the undersampled data. However, noise and aliasing artifacts still exist in the reconstructed image and a large number of auto-calibration signal lines are needed. To further improve the kernel learning-based MRI reconstruction and accelerate the speed, this paper proposes a group feature selection strategy to improve the learning performance and enhance the reconstruction quality. An explicit kernel mapping is used for selecting a subset of features which contribute most to estimate the missing k-space data. The experimental results show that the learning behaviours can be better predicted and therefore the reconstructed image quality is improved.
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Affiliation(s)
- Yuchou Chang
- Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth, Massachusetts, 02747, UNITED STATES
| | - Mert Saritac
- Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth, Dartmouth, Massachusetts, 02747, UNITED STATES
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24
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Chang Y. Improving Nonlinear Interpolation of K-Space Data Using Semi-Supervised Learning and Autoregressive Model. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3057-3060. [PMID: 34891888 DOI: 10.1109/embc46164.2021.9630666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Parallel magnetic resonance imaging (pMRI) accelerates data acquisition by undersampling k-space through an array of receiver coils. Finding accurate relationships between acquired and missing k-space data determines the interpolation performance and reconstruction quality. Autocalibration signals (ACS) are generally used to learn the interpolation coefficients for reconstructing the missing k-space data. Based on the estimation-approximation error analysis in machine learning, increasing training data size can reduce estimation error and therefore enhance generalization ability of the interpolator, but scanning time will be longer if more ACS data are acquired. We propose to augment training data using unacquired and acquired data outside of ACS region through semi-supervised learning idea and autoregressive model. Local neighbor unacquired k-space data can be used for training tasks and reducing the generalization error. Experimental results show that the proposed method outperforms the conventional methods by suppressing noise and aliasing artifacts.
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25
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Qiu Z, Jia S, Su S, Zhu Y, Liu X, Zheng H, Liang D, Wang H. Highly accelerated parallel MRI using wave encoding and virtual conjugate coils. Magn Reson Med 2021; 86:1345-1359. [PMID: 33856078 DOI: 10.1002/mrm.28803] [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: 10/24/2020] [Accepted: 03/22/2021] [Indexed: 11/11/2022]
Abstract
PURPOSE To propose a novel model incorporating virtual conjugate coil (VCC) reconstruction and wave encoding (Wave) for improved parallel MRI. THEORY AND METHODS A novel model (VCC-Wave) incorporating VCC and Wave is proposed. The correlation matrix of the encoding operator is introduced to analyze the encoding capability. In addition, simulation experiments are conducted to gain insights into VCC-Wave. In vivo experiments are performed to compare VCC-Wave with alternative methods. RESULTS The correlation matrix and the simulation experiments show that the proposed VCC-Wave can utilize more priors of Wave under the VCC framework. In vivo experiments show that the proposed VCC-Wave can achieve good image quality at a 6-fold acceleration in high-resolution and high-bandwidth cases, indicating an improvement over the original Wave technique. CONCLUSION The proposed VCC-Wave can not only combine the advantages of both the VCC and Wave but also exploit more priors of Wave under the VCC framework. The improvement in VCC-Wave alleviates the limitation of Wave in high-resolution and high-bandwidth cases.
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Affiliation(s)
- Zhilang Qiu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.,Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Sen Jia
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Shi Su
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Yanjie Zhu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Xin Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.,Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Haifeng Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
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26
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Ryu K, Lee JH, Nam Y, Gho SM, Kim HS, Kim DH. Accelerated multicontrast reconstruction for synthetic MRI using joint parallel imaging and variable splitting networks. Med Phys 2021; 48:2939-2950. [PMID: 33733464 DOI: 10.1002/mp.14848] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 03/12/2021] [Accepted: 03/12/2021] [Indexed: 11/10/2022] Open
Abstract
PURPOSE Synthetic magnetic resonance imaging (MRI) requires the acquisition of multicontrast images to estimate quantitative parameter maps, such as T1 , T2 , and proton density (PD). The study aims to develop a multicontrast reconstruction method based on joint parallel imaging (JPI) and joint deep learning (JDL) to enable further acceleration of synthetic MRI. METHODS The JPI and JDL methods are extended and combined to improve reconstruction for better-quality, synthesized images. JPI is performed as a first step to estimate the missing k-space lines, and JDL is then performed to correct and refine the previous estimate with a trained neural network. For the JDL architecture, the original variable splitting network (VS-Net) is modified and extended to form a joint variable splitting network (JVS-Net) to apply to multicontrast reconstructions. The proposed method is designed and tested for multidynamic multiecho (MDME) images with Cartesian uniform under-sampling using acceleration factors between 4 and 8. RESULTS It is demonstrated that the normalized root-mean-square error (nRMSE) is lower and the structural similarity index measure (SSIM) values are higher with the proposed method compared to both the JPI and JDL methods individually. The method also demonstrates the potential to produce a set of synthesized contrast-weighted images that closely resemble those from the fully sampled acquisition without erroneous artifacts. CONCLUSION Combining JPI and JDL enables the reconstruction of highly accelerated synthetic MRIs.
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Affiliation(s)
- Kanghyun Ryu
- Department of Radiology, Stanford University, Stanford, CA, USA.,Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Jae-Hun Lee
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Yoonho Nam
- Department of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea
| | - Sung-Min Gho
- MR Collaboration and Development, GE Healthcare, Seoul, Republic of Korea
| | - Ho-Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Dong-Hyun Kim
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
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27
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Kim D, Cauley SF, Nayak KS, Leahy RM, Haldar JP. Region-optimized virtual (ROVir) coils: Localization and/or suppression of spatial regions using sensor-domain beamforming. Magn Reson Med 2021; 86:197-212. [PMID: 33594732 PMCID: PMC8248187 DOI: 10.1002/mrm.28706] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 01/08/2021] [Accepted: 01/11/2021] [Indexed: 11/11/2022]
Abstract
PURPOSE In many MRI scenarios, magnetization is often excited from spatial regions that are not of immediate interest. Excitation of uninteresting magnetization can complicate the design of efficient imaging methods, leading to either artifacts or acquisitions that are longer than necessary. While there are many hardware- and sequence-based approaches for suppressing unwanted magnetization, this paper approaches this longstanding problem from a different and complementary angle, using beamforming to suppress signals from unwanted regions without modifying the acquisition hardware or pulse sequence. Unlike existing beamforming approaches, we use a spatially invariant sensor-domain approach that can be applied directly to raw data to facilitate image reconstruction. THEORY AND METHODS We use beamforming to linearly mix a set of original coils into a set of region-optimized virtual (ROVir) coils. ROVir coils optimize a signal-to-interference ratio metric, are easily calculated using simple generalized eigenvalue decomposition methods, and provide coil compression. RESULTS ROVir coils were compared against existing coil-compression methods, and were demonstrated to have substantially better signal suppression capabilities. In addition, examples were provided in a variety of different application contexts (including brain MRI, vocal tract MRI, and cardiac MRI; accelerated Cartesian and non-Cartesian imaging; and outer volume suppression) that demonstrate the strong potential of this kind of approach. CONCLUSION The beamforming-based ROVir framework is simple to implement, has promising capabilities to suppress unwanted MRI signal, and is compatible with and complementary to existing signal suppression methods. We believe that this general approach could prove useful across a wide range of different MRI applications.
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Affiliation(s)
- Daeun Kim
- Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA.,Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Stephen F Cauley
- Deparment of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Krishna S Nayak
- Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA.,Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Richard M Leahy
- Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA.,Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Justin P Haldar
- Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA.,Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
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28
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Zhang C, Klein S, Cristobal-Huerta A, Hernandez-Tamames JA, Poot DHJ. APIR4EMC: Autocalibrated parallel imaging reconstruction for extended multi-contrast imaging. Magn Reson Imaging 2021; 78:80-89. [PMID: 33592248 DOI: 10.1016/j.mri.2021.02.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 01/11/2021] [Accepted: 02/03/2021] [Indexed: 11/26/2022]
Abstract
PURPOSE To improve image quality of multi-contrast imaging with the proposed Autocalibrated Parallel Imaging Reconstruction for Extended Multi-Contrast Imaging (APIR4EMC). METHODS APIR4EMC reconstructs multi-contrast images in an autocalibrated parallel imaging reconstruction framework by adding contrasts as virtual coils. Compensation of signal evolution along the echo train of different contrasts is performed to improve signal prediction for missing samples. As a proof of concept, we performed prospectively accelerated phantom and in-vivo brain acquisitions with T1, T1-fat saturated (Fatsat), T2, PD, and FLAIR contrasts. The k-space sampling patterns of these acquisitions were jointly optimized. Images were jointly reconstructed with the proposed APIR4EMC method as well as individually with GRAPPA. Root mean square error (RMSE) to fully sampled reference images and g-factor maps were computed for both methods in the phantom experiment. Visual evaluation was performed in the in-vivo experiment. RESULTS Compared to GRAPPA, APIR4EMC reduced artifacts and improved SNR of the reconstructed images in the phantom acquisitions. Quantitatively, APIR4EMC substantially reduced noise amplification (g-factor) as well as RMSE compared to GRAPPA. Signal evolution compensation reduced artifacts. In the in-vivo experiments, 1 mm3 isotropic 3D images with contrasts of T1, T1-Fatsat, T2, PD, and FLAIR were acquired in as little as 7.5 min with the acceleration factor of 9. Reconstruction quality was consistent with the phantom results. CONCLUSION Compared to single contrast reconstruction with GRAPPA, APIR4EMC reduces artifacts and noise amplification in accelerated multi-contrast imaging.
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Affiliation(s)
- Chaoping Zhang
- Department of Medical Informatics, Erasmus MC, Rotterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands.
| | - Stefan Klein
- Department of Medical Informatics, Erasmus MC, Rotterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | | | | | - Dirk H J Poot
- Department of Medical Informatics, Erasmus MC, Rotterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
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29
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Yi Z, Liu Y, Zhao Y, Xiao L, Leong ATL, Feng Y, Chen F, Wu EX. Joint calibrationless reconstruction of highly undersampled multicontrast MR datasets using a low-rank Hankel tensor completion framework. Magn Reson Med 2021; 85:3256-3271. [PMID: 33533092 DOI: 10.1002/mrm.28674] [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: 07/28/2020] [Revised: 12/18/2020] [Accepted: 12/18/2020] [Indexed: 11/05/2022]
Abstract
PURPOSE To jointly reconstruct highly undersampled multicontrast two-dimensional (2D) datasets through a low-rank Hankel tensor completion framework. METHODS A multicontrast Hankel tensor completion (MC-HTC) framework is proposed to exploit the shareable information in multicontrast datasets with respect to their highly correlated image structure, common spatial support, and shared coil sensitivity for joint reconstruction. This is achieved by first organizing multicontrast k-space datasets into a single block-wise Hankel tensor. Subsequent low-rank tensor approximation via higher-order singular value decomposition (HOSVD) uses the image structural correlation by considering different contrasts as virtual channels. Meanwhile, the HOSVD imposes common spatial support and shared coil sensitivity by treating data from different contrasts as from additional k-space kernels. The missing k-space data are then recovered by iteratively performing such low-rank approximation and enforcing data consistency. This joint reconstruction framework was evaluated using multicontrast multichannel 2D human brain datasets (T1 -weighted, T2 -weighted, fluid-attenuated inversion recovery, and T1 -weighted-inversion recovery) of identical image geometry with random and uniform undersampling schemes. RESULTS The proposed method offered high acceleration, exhibiting significantly less residual errors when compared with both single-contrast SAKE (simultaneous autocalibrating and k-space estimation) and multicontrast J-LORAKS (joint parallel-imaging-low-rank matrix modeling of local k-space neighborhoods) low-rank reconstruction. Furthermore, the MC-HTC framework was applied uniquely to Cartesian uniform undersampling by incorporating a novel complementary k-space sampling strategy where the phase-encoding direction among different contrasts is orthogonally alternated. CONCLUSION The proposed MC-HTC approach presents an effective tensor completion framework to jointly reconstruct highly undersampled multicontrast 2D datasets without coil-sensitivity calibration.
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Affiliation(s)
- Zheyuan Yi
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China.,Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China.,Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, People's Republic of China
| | - Yilong Liu
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China.,Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Yujiao Zhao
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China.,Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Linfang Xiao
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China.,Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Alex T L Leong
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China.,Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Fei Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, People's Republic of China
| | - Ed X Wu
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China.,Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
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30
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Zhu Y, Liu Y, Ying L, Qiu Z, Liu Q, Jia S, Wang H, Peng X, Liu X, Zheng H, Liang D. A 4-minute solution for submillimeter whole-brain T 1ρ quantification. Magn Reson Med 2021; 85:3299-3307. [PMID: 33421224 DOI: 10.1002/mrm.28656] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 12/02/2020] [Accepted: 12/04/2020] [Indexed: 12/12/2022]
Abstract
PURPOSE To develop a robust, accurate, and accelerated T1ρ quantification solution for submillimeter in vivo whole-brain imaging. METHODS A multislice T1ρ mapping solution (MS-T1ρ ) was developed based on a two-acquisition scheme using turbo spin echo with RF cycling to allow for whole-brain coverage with 0.8-mm in-plane resolution. A compressed sensing-based fast imaging method, SCOPE, was used to accelerate the MS-T1ρ acquisition time to a total scan time of 3 minutes 31 seconds. A phantom experiment was conducted to assess the accuracy of MS-T1ρ by comparing the T1ρ value obtained using MS-T1ρ with the reference value obtained using the standard single-slice T1ρ mapping method. In vivo scans of 13 volunteers were acquired prospectively to validate the robustness of MS-T1ρ . RESULTS In the phantom study, the T1ρ values obtained with MS-T1ρ were in good agreement with the reference T1ρ values (R2 = 0.9991) and showed high consistency throughout all slices (coefficient of variation = 2.2 ± 2.43%). In the in vivo experiments, T1ρ maps were successfully acquired for all volunteers with no visually noticeable artifacts. There was no significant difference in T1ρ values between MS-T1ρ acquisitions and fully sampled acquisitions for all brain tissues (p-value > .05). In the intraclass correlation coefficient and Bland-Altman analyses, the accelerated T1ρ measurements show moderate to good agreement to the fully sampled reference values. CONCLUSION The proposed MS-T1ρ solution allows for high-resolution whole-brain T1ρ mapping within 4 minutes and may provide a potential tool for investigating neural diseases.
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Affiliation(s)
- Yanjie Zhu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Yuanyuan Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Leslie Ying
- Department of Biomedical Engineering and Department of Electrical Engineering, University at Buffalo, The State University of New York, Buffalo, New York, 14260, USA
| | - Zhilang Qiu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Qiegen Liu
- Department of Electronic Information Engineering, Nanchang University, Nanchang, Jiangxi, 330031, China
| | - Sen Jia
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Haifeng Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Xi Peng
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, 55905, USA
| | - Xin Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
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31
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Koopmans PJ, Pfaffenrot V. Enhanced POCS reconstruction for partial Fourier imaging in multi-echo and time-series acquisitions. Magn Reson Med 2020; 85:140-151. [PMID: 32710491 DOI: 10.1002/mrm.28417] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 06/16/2020] [Accepted: 06/16/2020] [Indexed: 12/28/2022]
Abstract
PURPOSE To improve partial Fourier (PF) imaging reconstruction in time-series or multi-echo acquisitions. METHODS Many PF methods use a phase estimate to restore Hermitian symmetry before filling missing k-space entries with measured data from the opposite half. This estimate is obtained from the symmetrically sampled, central part of k-space and its low-resolution results in artifacts near high-frequency phase effects (eg, tissue boundaries, vessels), limiting PF undersampling. Enhanced projection onto convex sets (POCS) uses full-resolution phase estimates and relies on alternating the half of k-space that is acquired in time series or multi-echo acquisitions. This enables full-resolution phase estimates to be calculated for each volume/echo, which are fed into the POCS framework. We apply enhanced POCS to high-resolution multi-echo FLASH and 3D-EPI functional MRI time-series data. RESULTS Reconstruction errors and their bias dramatically reduce compared with existing methods, without leading to temporal blurring in time-series acquisitions. This allows for higher PF acceleration factors at virtually no cost. CONCLUSION Enhanced POCS results in superior PF reconstructions. Furthermore, as the resolution of the phase estimate used for symmetry correction no longer depends on the PF factor, enhanced POCS is more robust against larger PF omission.
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Affiliation(s)
- Peter J Koopmans
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, Essen, Germany.,High-Field and Hybrid MR Imaging, University Hospital Essen, Essen, Germany
| | - Viktor Pfaffenrot
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, Essen, Germany.,High-Field and Hybrid MR Imaging, University Hospital Essen, Essen, Germany
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Park S, Chen L, Townsend J, Lee H, Feinberg DA. Simultaneous Multi-VENC and Simultaneous Multi-Slice Phase Contrast Magnetic Resonance Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:742-752. [PMID: 31403409 PMCID: PMC7138512 DOI: 10.1109/tmi.2019.2934422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This work develops a novel, simultaneous multi-VENC and simultaneous multi-slice (SMV+SMS) imaging in a single acquisition for robust phase contrast (PC) MRI. To this end, the pulse sequence was designed to permit concurrent acquisition of multiple VENCs as well as multiple slices on a shared frequency encoding gradient, in which each effective echo time for multiple VENCs was controlled by adjusting net gradient area while multiple slices were simultaneously excited by employing multiband resonance frequency (RF) pulses. For VENC and slice separation, RF phase cycling and gradient blip were applied to create both inter-VENC and inter-slice shifts along phase encoding direction, respectively. With an alternating RF phase cycling that generates oscillating steady-state with low and high signal amplitude, the acquired multi-VENC k-space was reformulated into 3D undersampled k-space by generating a virtual dimension along VENC direction for modulation induced artifact reduction. In vivo studies were conducted to validate the feasibility of the proposed method in comparison with conventional PC MRI. The proposed method shows comparable performance to the conventional method in delineating both low and high flow velocities across cardiac phases with high spatial coverage without apparent artifacts. In the presence of high flow velocity that is above the VENC value, the proposed method exhibits clear depiction of flow signals over conventional method, thereby leading to high VNR image with improved velocity dynamic range.
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Affiliation(s)
| | - Liyong Chen
- Advanced MRI Technologies, Sebastopol, CA, 95472, USA
| | - Jennifer Townsend
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, 94720, USA and Advanced MRI Technologies, Sebastopol, CA, 95472, USA
| | - Hyunyeol Lee
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - David A. Feinberg
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, 94720, USA and Advanced MRI Technologies, Sebastopol, CA, 95472, USA
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Haldar JP, Setsompop K. Linear Predictability in MRI Reconstruction: Leveraging Shift-Invariant Fourier Structure for Faster and Better Imaging. IEEE SIGNAL PROCESSING MAGAZINE 2020; 37:69-82. [PMID: 33746468 PMCID: PMC7971148 DOI: 10.1109/msp.2019.2949570] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
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Mani M, Aggarwal HK, Magnotta V, Jacob M. Improved MUSSELS reconstruction for high-resolution multi-shot diffusion weighted imaging. Magn Reson Med 2019; 83:2253-2263. [PMID: 31789440 DOI: 10.1002/mrm.28090] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Revised: 10/21/2019] [Accepted: 10/30/2019] [Indexed: 12/13/2022]
Abstract
PURPOSE MUSSELS is a one-step iterative reconstruction method for multishot diffusion weighted (msDW) imaging. The current work presents an efficient implementation, termed IRLS MUSSELS, that enables faster reconstruction to enhance its utility for high-resolution diffusion MRI studies. METHODS The recently proposed MUSSELS reconstruction belongs to a new class of parallel imaging-based methods that recover artifact-free DWIs from msDW data without needing phase compensation. The reconstruction is achieved via structured low-rank matrix completion algorithms, which are computationally demanding due to the large size of the Hankel matrices and their associated computations involving singular value decompositions. Because of this, computational demands of the MUSSELS reconstruction scales as the matrix size and the number of shots increases, which hinders its practical utility for high-resolution applications. In this work, we derive a computationally efficient MUSSELS formulation by modifying the iterative reweighted least squares (IRLS) method that were proposed earlier to solve such problems. Using whole-brain in vivo data, we show the utility of the IRLS MUSSELS for routine high-resolution studies with reduced computational burden. RESULTS IRLS MUSSELS provides about five times faster reconstruction for matrix sizes 192 × 192 and 256 × 256 compared to the earlier MUSSELS implementation. The widely employed conjugate symmetry priors can also be incorporated into IRLS MUSSELS to reduce blurring of the partial Fourier acquisitions, without incurring much computational burden. CONCLUSIONS The proposed method is observed to be computationally efficient to enable routine high-resolution studies. The computational complexity matches the traditional msDWI reconstruction methods and provides improved reconstruction results with the additional constraints.
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Affiliation(s)
- Merry Mani
- Department of Radiology, University of Iowa, Iowa City, Iowa, USA
| | - Hemant Kumar Aggarwal
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA
| | - Vincent Magnotta
- Department of Radiology, University of Iowa, Iowa City, Iowa, USA
| | - Mathews Jacob
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA
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Liu X, Hui ES, Chang HC. Elimination of residual aliasing artifact that resembles brain lesion on multi-oblique diffusion-weighted echo-planar imaging with parallel imaging using virtual coil acquisition. J Magn Reson Imaging 2019; 51:1442-1453. [PMID: 31664772 DOI: 10.1002/jmri.26966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Accepted: 09/25/2019] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Single-shot diffusion-weighted echo-planar imaging (ssDW-EPI) acquired with parallel imaging and a multi-oblique scan plane may suffer from residual aliasing artifacts, resembling lesions on the calculated apparent diffusion coefficient (ADC) map. PURPOSE To combine ssDW-EPI and virtual coil acquisition and develop a self-reference reconstruction method to eliminate the residual aliasing artifact on multi-oblique ssDW-EPI sequence with parallel imaging and multiple signal averaging. STUDY TYPE Prospective. SUBJECTS Three healthy subjects and 50 stroke patients. FIELD STRENGTH/SEQUENCE Conventional ssDW-EPI with parallel imaging, and proposed ssDW-EPI with virtual coil acquisition at 1.5T. ASSESSMENT The efficacy of the proposed method was evaluated in 50 stroke patients by comparing the ssDW-EPI with conventional parallel imaging reconstructions. The extent of residual aliasing artifacts were rated on a 5-point Likert scale by three independent raters. Only the data without residual aliasing artifacts on conventional ssDW-EPI were included for the assessment of signal-to-noise ratio (SNR), ghost-to-signal ratio (GSR), and ADC. STATISTICAL TESTS The interobserver agreements for examining residual aliasing artifacts were measured by the intraclass correlation coefficient (ICC). A two-sample t-test was performed for comparing SNR, GSR, and ADC. RESULTS There was a perfect agreement (ICC = 1.00) in the examination of residual aliasing artifacts on images obtained using the proposed method. The incidence rates of the residual aliasing artifact on the ADC maps obtained from the scanner console and proposed method were 60% (ie, 30 out of 50) and 0%, respectively. The proposed method offers significantly lower GSR than conventional parallel imaging reconstruction (P < 0.001). There was no significant difference in SNR (P = 0.20-0.51) and ADC values (P = 0.20-0.94) between conventional parallel imaging reconstructions and the proposed method. DATA CONCLUSION It appears that our method could effectively eliminate artifacts and significantly improve the GSR of b = 0 T2 WI and b > 0 DWI, as well as permit ADC measurement consistent with conventional techniques. Our method may be beneficial to clinical assessment of the brain that utilizes multi-oblique ssDW-EPI. LEVEL OF EVIDENCE 1 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:1442-1453.
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Affiliation(s)
- Xiaoxi Liu
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong
| | - Edward S Hui
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong.,State Key Laboratory of Brain and Cognitive Sciences, University of Hong Kong, Hong Kong
| | - Hing-Chiu Chang
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong
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Bilgic B, Chatnuntawech I, Manhard MK, Tian Q, Liao C, Iyer SS, Cauley SF, Huang SY, Polimeni JR, Wald LL, Setsompop K. Highly accelerated multishot echo planar imaging through synergistic machine learning and joint reconstruction. Magn Reson Med 2019; 82:1343-1358. [PMID: 31106902 PMCID: PMC6626584 DOI: 10.1002/mrm.27813] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Revised: 04/22/2019] [Accepted: 04/22/2019] [Indexed: 12/13/2022]
Abstract
PURPOSE To introduce a combined machine learning (ML)- and physics-based image reconstruction framework that enables navigator-free, highly accelerated multishot echo planar imaging (msEPI) and demonstrate its application in high-resolution structural and diffusion imaging. METHODS Single-shot EPI is an efficient encoding technique, but does not lend itself well to high-resolution imaging because of severe distortion artifacts and blurring. Although msEPI can mitigate these artifacts, high-quality msEPI has been elusive because of phase mismatch arising from shot-to-shot variations which preclude the combination of the multiple-shot data into a single image. We utilize deep learning to obtain an interim image with minimal artifacts, which permits estimation of image phase variations attributed to shot-to-shot changes. These variations are then included in a joint virtual coil sensitivity encoding (JVC-SENSE) reconstruction to utilize data from all shots and improve upon the ML solution. RESULTS Our combined ML + physics approach enabled Rinplane × multiband (MB) = 8- × 2-fold acceleration using 2 EPI shots for multiecho imaging, so that whole-brain T2 and T2 * parameter maps could be derived from an 8.3-second acquisition at 1 × 1 × 3-mm3 resolution. This has also allowed high-resolution diffusion imaging with high geometrical fidelity using 5 shots at Rinplane × MB = 9- × 2-fold acceleration. To make these possible, we extended the state-of-the-art MUSSELS reconstruction technique to simultaneous multislice encoding and used it as an input to our ML network. CONCLUSION Combination of ML and JVC-SENSE enabled navigator-free msEPI at higher accelerations than previously possible while using fewer shots, with reduced vulnerability to poor generalizability and poor acceptance of end-to-end ML approaches.
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Affiliation(s)
- Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Itthi Chatnuntawech
- National Nanotechnology Center, National Science and Technology Development Agency, Pathum Thani, Thailand
| | - Mary Kate Manhard
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Siddharth S. Iyer
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Stephen F. Cauley
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Susie Y. Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Jonathan R. Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Lawrence L. Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
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Dai E, Wu Y, Wu W, Guo R, Liu S, Miller KL, Zhang Z, Guo H. A 3D k-space Fourier encoding and reconstruction framework for simultaneous multi-slab acquisition. Magn Reson Med 2019; 82:1012-1024. [PMID: 31045283 PMCID: PMC6831486 DOI: 10.1002/mrm.27793] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2018] [Revised: 03/22/2019] [Accepted: 04/10/2019] [Indexed: 12/15/2022]
Abstract
PURPOSE To propose a novel 3D k-space Fourier encoding and reconstruction framework for simultaneous multi-slab (SMSlab) acquisition and demonstrate its efficacy in high-resolution imaging. METHODS First, it is illustrated in theory how the inter-slab gap interferes with the formation of the SMSlab 3D k-space. Then, joint RF and gradient encoding are applied to remove the inter-slab gap interference and form a SMSlab 3D k-space. In vivo experiments are performed to validate the proposed theory. Acceleration in the proposed SMSlab 3D k-space is also evaluated. RESULTS High-resolution (1.0 mm isotropic) images can be reconstructed using the proposed SMSlab 3D framework. Controlled aliasing in parallel imaging sampling and 2D GRAPPA reconstruction can also be applied in the SMSlab 3D k-space. Compared with conventional multi-slab acquisition, SMSlab exhibits better SNR maintainability (such as lower g-factors), especially at high acceleration factors. CONCLUSION It is demonstrated that the joint application of RF and gradient encoding enables SMSlab within a 3D Fourier encoding framework. Images with high isotropic resolution can be reconstructed, and further acceleration is also applicable. The proposed SMSlab 3D k-space can be valuable for both high-resolution and high-efficiency diffusion and functional MRI.
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Affiliation(s)
- Erpeng Dai
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, People's Republic of
| | - Yuhsuan Wu
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, People's Republic of
| | - Wenchuan Wu
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Rui Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, People's Republic of
| | - Simin Liu
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, People's Republic of
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Zhe Zhang
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China, People's Republic of
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, People's Republic of
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Manhard MK, Bilgic B, Liao C, Han S, Witzel T, Yen YF, Setsompop K. Accelerated whole-brain perfusion imaging using a simultaneous multislice spin-echo and gradient-echo sequence with joint virtual coil reconstruction. Magn Reson Med 2019; 82:973-983. [PMID: 31069861 PMCID: PMC6692914 DOI: 10.1002/mrm.27784] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 04/03/2019] [Accepted: 04/04/2019] [Indexed: 12/13/2022]
Abstract
PURPOSE Dynamic susceptibility contrast imaging requires high temporal sampling, which poses limits on achievable spatial coverage and resolution. Additionally, more encoding-intensive multi-echo acquisitions for quantitative imaging are desired to mitigate contrast leakage effects, which further limits spatial encoding. We present an accelerated sequence that provides whole-brain coverage at an improved spatio-temporal resolution, to allow for dynamic quantitative R2 and R2 * mapping during contrast-enhanced imaging. METHODS A multi-echo spin and gradient-echo sequence was implemented with simultaneous multislice acquisition. Complementary k-space sampling between repetitions and joint virtual coil reconstruction were used along with a dynamic phase-matching technique to achieve high-quality reconstruction at 9-fold acceleration, which enabled 2 × 2 × 5 mm whole-brain imaging at TR of 1.5 to 1.7 seconds. The multi-echo images from this sequence were fit to achieve quantitative R2 and R2 * maps for each repetition, and subsequently used to find perfusion measures including cerebral blood flow and cerebral blood volume. RESULTS Images reconstructed using joint virtual coil show improved image quality and g-factor compared with conventional reconstruction methods, resulting in improved quantitative maps with a 9-fold acceleration factor and whole-brain coverage during the dynamic perfusion acquisition. CONCLUSION The method presented shows the advantage of using a joint virtual coil-GRAPPA reconstruction to allow for high acceleration factors while maintaining reliable image quality for quantitative perfusion mapping, with the potential to improve tumor diagnostics and monitoring.
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Affiliation(s)
- Mary Kate Manhard
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - SoHyun Han
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Thomas Witzel
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Yi-Fen Yen
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
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Liao C, Stockmann J, Tian Q, Bilgic B, Arango NS, Manhard MK, Huang SY, Grissom WA, Wald LL, Setsompop K. High-fidelity, high-isotropic-resolution diffusion imaging through gSlider acquisition with B 1 + and T 1 corrections and integrated ΔB 0 /Rx shim array. Magn Reson Med 2019; 83:56-67. [PMID: 31373048 DOI: 10.1002/mrm.27899] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 06/10/2019] [Accepted: 06/17/2019] [Indexed: 01/24/2023]
Abstract
PURPOSE B 1 + and T1 corrections and dynamic multicoil shimming approaches were proposed to improve the fidelity of high-isotropic-resolution generalized slice-dithered enhanced resolution (gSlider) diffusion imaging. METHODS An extended reconstruction incorporating B 1 + inhomogeneity and T1 recovery information was developed to mitigate slab-boundary artifacts in short-repetition time (TR) gSlider acquisitions. Slab-by-slab dynamic B0 shimming using a multicoil integrated ΔB0 /Rx shim array and high in-plane acceleration (Rinplane = 4) achieved with virtual-coil GRAPPA were also incorporated into a 1-mm isotropic resolution gSlider acquisition/reconstruction framework to achieve a significant reduction in geometric distortion compared to single-shot echo planar imaging (EPI). RESULTS The slab-boundary artifacts were alleviated by the proposed B 1 + and T1 corrections compared to the standard gSlider reconstruction pipeline for short-TR acquisitions. Dynamic shimming provided >50% reduction in geometric distortion compared to conventional global second-order shimming. One-millimeter isotropic resolution diffusion data show that the typically problematic temporal and frontal lobes of the brain can be imaged with high geometric fidelity using dynamic shimming. CONCLUSIONS The proposed B 1 + and T1 corrections and local-field control substantially improved the fidelity of high-isotropic-resolution diffusion imaging, with reduced slab-boundary artifacts and geometric distortion compared to conventional gSlider acquisition and reconstruction. This enabled high-fidelity whole-brain 1-mm isotropic diffusion imaging with 64 diffusion directions in 20 min using a 3T clinical scanner.
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Affiliation(s)
- Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Jason Stockmann
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Nicolas S Arango
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Mary Kate Manhard
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - William A Grissom
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
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Mullen M, Gutierrez A, Kobayashi N, Haupt J, Garwood M. Accelerated imaging with segmented 2D pulses using parallel imaging and virtual coils. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2019; 305:185-194. [PMID: 31302513 PMCID: PMC6711164 DOI: 10.1016/j.jmr.2019.07.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 07/02/2019] [Accepted: 07/03/2019] [Indexed: 06/10/2023]
Abstract
Large magnetic field inhomogeneity can be a significant cause of spatial flip-angle variation when using ordinary, limited-bandwidth RF pulses. Multidimensional RF pulses are particularly sensitive to inhomogeneity due to their extended pulse length, which decreases their bandwidth. Previously, it was shown that, by breaking a 2D pulse into multiple undersampled k-space segments, the excitation bandwidth can be increased at the expense of increased imaging time. The present study shows how this increased imaging time can be offset by undersampling acquisition k-space in a phase-encoded dimension that is in the direction of excitation segmentation. Data from each segment are viewed as originating from "virtual receive coils" rather than multiple physical coils. The undersampled data are reconstructed using parallel imaging techniques (e.g. as in GRAPPA). The method was tested in vivo with brain imaging at both 3 T and 4 T, and used in conjunction with a 32-channel head coil and conventional GRAPPA on the 3 T data. Relationships with existing techniques and future applications are discussed.
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Affiliation(s)
- Michael Mullen
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA; School of Physics and Astronomy, University of Minnesota, Minneapolis, MN, USA
| | | | - Naoharu Kobayashi
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Jarvis Haupt
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Michael Garwood
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA.
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Wang H, Jia S, Chang Y, Zhu Y, Zou C, Li Y, Liu X, Zheng H, Liang D. Improving GRAPPA reconstruction using joint nonlinear kernel mapped and phase conjugated virtual coils. Phys Med Biol 2019; 64:14NT01. [PMID: 31167169 DOI: 10.1088/1361-6560/ab274d] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
To improve the reconstruction condition and alleviate the noise amplification of GRAPPA reconstruction by aggregating the phase conjugated and nonlinear kernel mapped coils with the original physical coil. Nonlinear GRAPPA (NL-GRAPPA) is a kernel-based non-iterative approach which can reduce noise-induced error in GRAPPA reconstruction. And virtual conjugate coil (VCC) embeds the conjugate symmetric property of k-space into GRAPPA data synthesis to improve reconstruction condition. This work proposed NL-VCC-GRAPPA to jointly utilize the nonlinear mapped virtual coil and phase conjugated virtual coil to further reduce noise amplification in parallel imaging. In vivo static and dynamic 2D imaging accelerated by uniform undersampling schemes were performed to evaluate the proposed method in terms of visual image quality, root-mean-square-error (RMSE), and geometry factor (g-factor). The effects of acceleration factors, calibration data size and kernel shape on the proposed model were also separately analyzed and discussed. The proposed method illustrated improved visual image quality evidenced by reduced retrospective RMSE and prospective g-factor comparing with conventional GRAPPA and the recently proposed iterative SENSE-LORAKS reconstructions. Although a larger amount of calibration data and smaller kernel size were required to stabilize the calibration of fourfold extended kernel for the proposed method, it was non-iterative and relatively insensitive to parameter adjustment in the applications. The proposed NL-VCC-extension to conventional GRAPPA brings visible improvements for imaging scenarios accelerated by the widely available uniform undersampling schemes in a practically efficient manner without iteration.
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Affiliation(s)
- Haifeng Wang
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China. Co-First/Equal Authorship
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Phase-matched virtual coil reconstruction for highly accelerated diffusion echo-planar imaging. Neuroimage 2019; 194:291-302. [DOI: 10.1016/j.neuroimage.2019.04.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 03/26/2019] [Accepted: 04/01/2019] [Indexed: 11/24/2022] Open
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Kettinger AO, Setsompop K, Kannengiesser SAR, Breuer FA, Vidnyanszky Z, Blaimer M. Full utilization of conjugate symmetry: combining virtual conjugate coil reconstruction with partial Fourier imaging for g-factor reduction in accelerated MRI. Magn Reson Med 2019; 82:1073-1090. [PMID: 31081561 DOI: 10.1002/mrm.27799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 02/27/2019] [Accepted: 04/13/2019] [Indexed: 11/10/2022]
Abstract
PURPOSE In this study we propose a method to combine the parallel virtual conjugate coil (VCC) reconstruction with partial Fourier (PF) acquisition to improve reconstruction conditioning and reduce noise amplification in accelerated MRI where PF is used. METHODS Accelerated measurements are reconstructed in k-space by GRAPPA, with a VCC reconstruction kernel trained and applied in the central, symmetrically sampled part of k-space, while standard reconstruction is performed on the asymmetrically sampled periphery. The two reconstructed regions are merged to form a full reconstructed dataset, followed by PF reconstruction. The method is tested in vivo using T1-weighted spin-echo and T2*-weighted gradient-echo echo planar imaging (EPI) sequences, using both in-plane and simultaneous multislice (SMS) acceleration, at 1.5T and 3T field strengths. Noise amplification is estimated with theoretical calculations and pseudo-multiple-replica computations, for different PF factors, using zero-filling, homodyne, and projection onto convex sets (POCS) PF reconstruction. RESULTS Depending on the PF algorithm and the inherent benefit of VCC reconstruction without PF, approximately 35% to 80%, 15% to 60%, and 5% to 30% of that intrinsic SNR gain can be retained for PF factors 7/8, 6/8, and 5/8, respectively, by including the VCC signals in the reconstruction. Compared with VCC-reconstructed acquisitions of higher acceleration, without PF, but having the same net acceleration, the combined method can provide a higher SNR if the inherent benefit of VCC is low or moderate. CONCLUSION The proposed technique enables the partial application of VCC reconstruction to measurements with PF using either in-plane or SMS acceleration, and therefore can reduce the noise amplification of such acquisitions.
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Affiliation(s)
- Adam O Kettinger
- Brain Imaging Centre, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary.,Department of Nuclear Techniques, Budapest University of Technology and Economics, Budapest, Hungary.,Siemens Healthcare GmbH, Erlangen, Germany
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts.,Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts
| | | | - Felix A Breuer
- Magnetic Resonance and X-ray Imaging Department, Fraunhofer Development Center X-ray Technology (EZRT), Würzburg, Germany
| | - Zoltan Vidnyanszky
- Brain Imaging Centre, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary
| | - Martin Blaimer
- Magnetic Resonance and X-ray Imaging Department, Fraunhofer Development Center X-ray Technology (EZRT), Würzburg, Germany
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Park S, Chen L, Beckett A, Feinberg DA. Virtual slice concept for improved simultaneous multi-slice MRI employing an extended leakage constraint. Magn Reson Med 2019; 82:377-386. [PMID: 30883901 DOI: 10.1002/mrm.27741] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Revised: 02/02/2019] [Accepted: 02/25/2019] [Indexed: 11/09/2022]
Abstract
PURPOSE To develop a novel, simultaneous multi-slice (SMS) reconstruction that extends an inter-slice leakage constraint to intra-slice aliasing with a virtual slice concept for artifact reduction. METHODS Inter-slice leakage constraint has been used for SMS reconstruction that mitigates leakage artifacts from the adjacent slices. In this work, the leakage constraint is extended to more general framework that includes SMS and parallel MRI as special cases by viewing intra-slice aliasing artifacts from undersampling as virtual slices while imposing data fidelity to ensure the measurement consistency. In this way, the reconstruction makes it feasible to directly estimate the individual slices from the undersampled SMS acquisition as a one-step method. The performance of the extended method is evaluated with data acquired using 2D GRE and EPI sequences. RESULTS Compared to a two-step method that performs slice unaliasing followed by inplane unaliasing, the proposed one-step method reduces aliasing artifacts by employing the extended leakage constraint while lowering the noise amplification by improving the conditioning for the inverse problem. CONCLUSIONS The proposed one-step method takes advantage of virtual slices as additional encoding power for improved image quality. We successfully demonstrated that the proposed one-step method minimizes a trade-off between aliasing artifacts and amplified noises over the two-step method.
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Affiliation(s)
- Suhyung Park
- Helen Wills Neuroscience Institute, University of California, Berkeley, California
| | - Liyong Chen
- Advanced MRI Technologies, Sebastopol, California
| | - Alexander Beckett
- Helen Wills Neuroscience Institute, University of California, Berkeley, California.,Advanced MRI Technologies, Sebastopol, California
| | - David A Feinberg
- Helen Wills Neuroscience Institute, University of California, Berkeley, California.,Advanced MRI Technologies, Sebastopol, California
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ENLIVE: An Efficient Nonlinear Method for Calibrationless and Robust Parallel Imaging. Sci Rep 2019; 9:3034. [PMID: 30816312 PMCID: PMC6395635 DOI: 10.1038/s41598-019-39888-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Accepted: 02/04/2019] [Indexed: 12/05/2022] Open
Abstract
Robustness against data inconsistencies, imaging artifacts and acquisition speed are crucial factors limiting the possible range of applications for magnetic resonance imaging (MRI). Therefore, we report a novel calibrationless parallel imaging technique which simultaneously estimates coil profiles and image content in a relaxed forward model. Our method is robust against a wide class of data inconsistencies, minimizes imaging artifacts and is comparably fast, combining important advantages of many conceptually different state-of-the-art parallel imaging approaches. Depending on the experimental setting, data can be undersampled well below the Nyquist limit. Here, even high acceleration factors yield excellent imaging results while being robust to noise and the occurrence of phase singularities in the image domain, as we show on different data. Moreover, our method successfully reconstructs acquisitions with insufficient field-of-view. We further compare our approach to ESPIRiT and SAKE using spin-echo and gradient echo MRI data from the human head and knee. In addition, we show its applicability to non-Cartesian imaging on radial FLASH cardiac MRI data. Using theoretical considerations, we show that ENLIVE can be related to a low-rank formulation of blind multi-channel deconvolution, explaining why it inherently promotes low-rank solutions.
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46
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Tao S, Shu Y, Trzasko JD, Huston J, Bernstein MA. Partial fourier shells trajectory for non-cartesian MRI. Phys Med Biol 2019; 64:04NT01. [PMID: 30625455 DOI: 10.1088/1361-6560/aafcc5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Non-Cartesian MRI acquisition has demonstrated various advantages in many clinical applications. The shells trajectory is a 3D non-Cartesian MRI acquisition technique that samples the k-space using a series of concentric shells to achieve efficient 3D isotropic acquisition. Partial Fourier acquisition is an acceleration technique that is widely used in Cartesian MRI. It exploits the conjugate symmetry of k-space measurement to reduce the number of k-space samples compared to full-k-space acquisition, without loss of spatial resolution. For a Cartesian MRI acquisition, the direction of partial Fourier acceleration is aligned either with the phase encoded or frequency encoded direction. In those cases, the underlying image matrix can be reconstructed from the undersampled k-space data using a non-iterative, homodyne reconstruction framework. However, designing a non-Cartesian acquisition trajectory that is compatible with non-iterative homodyne reconstruction is not nearly as straightforward as in the Cartesian case. One reason is the non-iterative homodyne reconstruction requires (slightly over) half of the k-space to be fully sampled. Since the direction of partial Fourier acceleration varies throughout the acquisition in the non-Cartesian trajectory, directly applying the same partial Fourier acquisition pattern (as in Cartesian acquisitions) to a non-Cartesian trajectory does not necessarily yield a continuous, physically-achievable trajectory. In this work, we develop an asymmetric shells trajectory with fully-automated trajectory and gradient waveform design to achieve partial Fourier acquisition for the shells trajectory. We then demonstrate a non-iterative image reconstruction framework for the proposed trajectory. Phantom and in vivo brain scans based on spoiled gradient echo (SPGR) shells and magnetization-prepared shells (MP-shells) were performed to test the proposed trajectory design and reconstruction method. Our phantom and in vivo results demonstrate that the proposed partial Fourier shells trajectory maintains the desirable image contrast and high sampling efficiency from the fully sampled shells, while further reducing data acquisition time.
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Affiliation(s)
- Shengzhen Tao
- Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States of America
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47
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Hu Y, Levine EG, Tian Q, Moran CJ, Wang X, Taviani V, Vasanawala S, McNab JA, Daniel BL, Hargreaves BA. Motion-robust reconstruction of multishot diffusion-weighted images without phase estimation through locally low-rank regularization. Magn Reson Med 2019; 81:1181-1190. [PMID: 30346058 PMCID: PMC6289606 DOI: 10.1002/mrm.27488] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Revised: 07/11/2018] [Accepted: 07/18/2018] [Indexed: 11/12/2022]
Abstract
PURPOSE The goal of this work is to propose a motion robust reconstruction method for diffusion-weighted MRI that resolves shot-to-shot phase mismatches without using phase estimation. METHODS Assuming that shot-to-shot phase variations are slowly varying, spatial-shot matrices can be formed using a local group of pixels to form columns, in which each column is from a different shot (excitation). A convex model with a locally low-rank constraint on the spatial-shot matrices is proposed. In vivo brain and breast experiments were performed to evaluate the performance of the proposed method. RESULTS The proposed method shows significant benefits when the motion is severe, such as for breast imaging. Furthermore, the resulting images can be used for reliable phase estimation in the context of phase-estimation-based methods to achieve even higher image quality. CONCLUSION We introduced the shot-locally low-rank method, a reconstruction technique for multishot diffusion-weighted MRI without explicit phase estimation. In addition, its motion robustness can be beneficial to neuroimaging and body imaging.
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Affiliation(s)
- Yuxin Hu
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Evan G. Levine
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Qiyuan Tian
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Radiology, Stanford University, Stanford, California, USA
| | | | - Xiaole Wang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | | | | | - Jennifer A. McNab
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Bruce L. Daniel
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Brian A. Hargreaves
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
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Mickevicius NJ, Chen X, Boyd Z, Lee HJ, Ibbott GS, Paulson ES. Simultaneous motion monitoring and truth-in-delivery analysis imaging framework for MR-guided radiotherapy. ACTA ACUST UNITED AC 2018; 63:235014. [DOI: 10.1088/1361-6560/aaec91] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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49
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Kim TH, Bilgic B, Polak D, Setsompop K, Haldar JP. Wave-LORAKS: Combining wave encoding with structured low-rank matrix modeling for more highly accelerated 3D imaging. Magn Reson Med 2018; 81:1620-1633. [PMID: 30252157 DOI: 10.1002/mrm.27511] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Revised: 08/06/2018] [Accepted: 08/07/2018] [Indexed: 12/13/2022]
Abstract
PURPOSE Wave-CAIPI is a novel acquisition approach that enables highly accelerated 3D imaging. This paper investigates the combination of Wave-CAIPI with LORAKS-based reconstruction (Wave-LORAKS) to enable even further acceleration. METHODS LORAKS is a constrained image reconstruction framework that can impose spatial support, smooth phase, sparsity, and/or parallel imaging constraints. LORAKS requires minimal prior information, and instead uses the low-rank subspace structure of the raw data to automatically learn which constraints to impose and how to impose them. Previous LORAKS implementations addressed 2D image reconstruction problems. In this work, several recent advances in structured low-rank matrix recovery were combined to enable large-scale 3D Wave-LORAKS reconstruction with improved quality and computational efficiency. Wave-LORAKS was investigated by retrospective subsampling of two fully sampled Wave-encoded 3D MPRAGE datasets, and comparisons were made against existing Wave reconstruction approaches. RESULTS Results show that Wave-LORAKS can yield higher reconstruction quality with 16×-accelerated data than is obtained by traditional Wave-CAIPI with 9×-accerated data. CONCLUSIONS There are strong synergies between Wave encoding and LORAKS, which enables Wave-LORAKS to achieve higher acceleration and more flexible sampling compared to Wave-CAIPI.
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Affiliation(s)
- Tae Hyung Kim
- Department of Electrical Engineering, University of Southern California, Los Angeles, California.,Signal and Image Processing Institute, University of Southern California, Los Angeles, California
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Daniel Polak
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts.,Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Justin P Haldar
- Department of Electrical Engineering, University of Southern California, Los Angeles, California.,Signal and Image Processing Institute, University of Southern California, Los Angeles, California
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50
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Bilgic B, Kim TH, Liao C, Manhard MK, Wald LL, Haldar JP, Setsompop K. Improving parallel imaging by jointly reconstructing multi-contrast data. Magn Reson Med 2018; 80:619-632. [PMID: 29322551 PMCID: PMC5910232 DOI: 10.1002/mrm.27076] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2017] [Revised: 12/10/2017] [Accepted: 12/15/2017] [Indexed: 12/14/2022]
Abstract
PURPOSE To develop parallel imaging techniques that simultaneously exploit coil sensitivity encoding, image phase prior information, similarities across multiple images, and complementary k-space sampling for highly accelerated data acquisition. METHODS We introduce joint virtual coil (JVC)-generalized autocalibrating partially parallel acquisitions (GRAPPA) to jointly reconstruct data acquired with different contrast preparations, and show its application in 2D, 3D, and simultaneous multi-slice (SMS) acquisitions. We extend the joint parallel imaging concept to exploit limited support and smooth phase constraints through Joint (J-) LORAKS formulation. J-LORAKS allows joint parallel imaging from limited autocalibration signal region, as well as permitting partial Fourier sampling and calibrationless reconstruction. RESULTS We demonstrate highly accelerated 2D balanced steady-state free precession with phase cycling, SMS multi-echo spin echo, 3D multi-echo magnetization-prepared rapid gradient echo, and multi-echo gradient recalled echo acquisitions in vivo. Compared to conventional GRAPPA, proposed joint acquisition/reconstruction techniques provide more than 2-fold reduction in reconstruction error. CONCLUSION JVC-GRAPPA takes advantage of additional spatial encoding from phase information and image similarity, and employs different sampling patterns across acquisitions. J-LORAKS achieves a more parsimonious low-rank representation of local k-space by considering multiple images as additional coils. Both approaches provide dramatic improvement in artifact and noise mitigation over conventional single-contrast parallel imaging reconstruction. Magn Reson Med 80:619-632, 2018. © 2018 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Tae Hyung Kim
- Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA
- Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA
| | - Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Mary Kate Manhard
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Lawrence L. Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Justin P. Haldar
- Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA
- Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
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