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Tang L, Zhao Y, Li Y, Guo R, Cai B, Wang J, Li Y, Liang ZP, Peng X, Luo J. JSENSE-Pro: Joint sensitivity estimation and image reconstruction in parallel imaging using pre-learned subspaces of coil sensitivity functions. Magn Reson Med 2023; 89:1531-1542. [PMID: 36480000 DOI: 10.1002/mrm.29548] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 11/12/2022] [Accepted: 11/15/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE To improve calibrationless parallel imaging using pre-learned subspaces of coil sensitivity functions. THEORY AND METHODS A subspace-based joint sensitivity estimation and image reconstruction method was developed for improved parallel imaging with no calibration data. Specifically, we proposed to use a probabilistic subspace model to capture prior information of the coil sensitivity functions from previous scans acquired using the same receiver system. Both the subspace basis and coefficient distributions were learned from a small set of training data. The learned subspace model was then incorporated into the regularized reconstruction formalism that includes a sparsity prior. The nonlinear optimization problem was solved using alternating minimization algorithm. Public fastMRI brain dataset was retrospectively undersampled by different schemes for performance evaluation of the proposed method. RESULTS With no calibration data, the proposed method consistently produced the most accurate coil sensitivity estimation and highest quality image reconstructions at all acceleration factors tested in comparison with state-of-the-art methods including JSENSE, DeepSENSE, P-LORAKS, and Sparse BLIP. Our results are comparable to or even better than those from SparseSENSE, which used calibration data for sensitivity estimation. The work also demonstrated that the probabilistic subspace model learned from T2 w data can be generalized to aiding the reconstruction of FLAIR data acquired from the same receiver system. CONCLUSION A subspace-based method named JSENSE-Pro has been proposed for accelerated parallel imaging without the acquisition of companion calibration data. The method is expected to further enhance the practical utility of parallel imaging, especially in applications where calibration data acquisition is not desirable or limited.
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Affiliation(s)
- Lihong Tang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yibo Zhao
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Yudu Li
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Rong Guo
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Bingyang Cai
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jia Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhi-Pei Liang
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Xi Peng
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Jie Luo
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
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Peng X, Sui Y, Trzasko JD, Glaser KJ, Huston J, Ehman RL, Pipe JG. Fast 3D MR elastography of the whole brain using spiral staircase: Data acquisition, image reconstruction, and joint deblurring. Magn Reson Med 2021; 86:2011-2024. [PMID: 34096097 PMCID: PMC8498799 DOI: 10.1002/mrm.28855] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 04/06/2021] [Accepted: 05/04/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE To address the need for a method to acquire 3D data for MR elastography (MRE) of the whole brain with substantially improved spatial resolution, high SNR, and reduced acquisition time compared with conventional methods. METHODS We combined a novel 3D spiral staircase data-acquisition method with a spoiled gradient-echo pulse sequence and MRE motion-encoding gradients (MEGs). The spiral-out acquisition permitted use of longer-duration motion-encoding gradients (ie, over two full oscillatory cycles) to enhance displacement SNR, while still maintaining a reasonably short TE for good phase-SNR. Through-plane parallel imaging with low noise penalties was implemented to accelerate acquisition along the slice direction. Shared anatomical information was exploited in the deblurring procedure to further boost SNR for stiffness inversion. RESULTS In vivo and phantom experiments demonstrated the feasibility of the proposed method in producing brain MRE results comparable to the spin-echo-based approaches, both qualitatively and quantitatively. High-resolution (2-mm isotropic) brain MRE data were acquired in 5 minutes using our method with good SNR. Joint deblurring with shared anatomical information produced SNR-enhanced images, leading to upward stiffness estimation. CONCLUSION A novel 3D gradient-echo-based approach has been designed and implemented, and shown to have promising potential for fast and high-resolution in vivo MRE of the whole brain.
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Affiliation(s)
- Xi Peng
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Yi Sui
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Kevin J Glaser
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - John Huston
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Richard L Ehman
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - James G Pipe
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
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Iyer S, Ong F, Setsompop K, Doneva M, Lustig M. SURE-based automatic parameter selection for ESPIRiT calibration. Magn Reson Med 2020; 84:3423-3437. [PMID: 32686178 DOI: 10.1002/mrm.28386] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 04/21/2020] [Accepted: 05/29/2020] [Indexed: 11/10/2022]
Abstract
PURPOSE ESPIRiT is a parallel imaging method that estimates coil sensitivity maps from the auto-calibration region (ACS). This requires choosing several parameters for the optimal map estimation. While fairly robust to these parameter choices, occasionally, poor selection can result in reduced performance. The purpose of this work is to automatically select parameters in ESPIRiT for more robust and consistent performance across a variety of exams. METHODS By viewing ESPIRiT as a denoiser, Stein's unbiased risk estimate (SURE) is leveraged to automatically optimize parameter selection in a data-driven manner. The optimum parameters corresponding to the minimum true squared error, minimum SURE as derived from densely sampled, high-resolution, and non-accelerated data and minimum SURE as derived from ACS are compared using simulation experiments. To avoid optimizing the rank of ESPIRiT's auto-calibrating matrix (one of the parameters), a heuristic derived from SURE-based singular value thresholding is also proposed. RESULTS Simulations show SURE derived from the densely sampled, high-resolution, and non-accelerated data to be an accurate estimator of the true mean squared error, enabling automatic parameter selection. The parameters that minimize SURE as derived from ACS correspond well to the optimal parameters. The soft-threshold heuristic improves computational efficiency while providing similar results to an exhaustive search. In-vivo experiments verify the reliability of this method. CONCLUSIONS Using SURE to determine ESPIRiT parameters allows for automatic parameter selections. In-vivo results are consistent with simulation and theoretical results.
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Affiliation(s)
- Siddharth Iyer
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Frank Ong
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | | | - Michael Lustig
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA
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Zhu Q, Wang W, Cheng J, Peng X. Incorporating reference guided priors into calibrationless parallel imaging reconstruction. Magn Reson Imaging 2019; 57:347-358. [PMID: 30597191 DOI: 10.1016/j.mri.2018.12.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 11/27/2018] [Accepted: 12/19/2018] [Indexed: 10/27/2022]
Abstract
PURPOSE To propose and evaluate a new calibrationless parallel imaging method aimed at further improving the reconstruction accuracy of the accelerated multi-channel MR images. METHOD We introduce a new calibrationless parallel imaging method. On top of exploiting joint sparsity cross channels of the target image to be reconstructed, it incorporates similar priors on the grey-level intensity and edge orientation, which both come from a high-spatial resolution reference image that can be easily obtained in many clinical MRI scenarios. The mixed l2-l1 norm is used to enforce joint sparsity and a multi-scale gradient operator is applied to extract fine edges from the reference image. Additionally, this optimization problem can be solved via a non-linear conjugate gradient algorithm with line search in this work. RESULTS The proposed method is compared with the existing state-of-the-art auto-calibration and calibrationless parallel imaging techniques. The experiments on different in-vivo brain MR datasets show that the proposed method has the superior performance in terms of both artifact suppression and detail preservation. CONCLUSION The reference guided calibrationless parallel imaging method can significantly improve the performance of joint reconstruction of target channel images. Even when the reduction factor is high, it can keep edge structures well.
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Affiliation(s)
- Qingyong Zhu
- School of Mathematic & Statistics, Xi'an Jiaotong University, Xi'an 710049, China
| | - Wei Wang
- School of Mathematic & Statistics, Xi'an Jiaotong University, Xi'an 710049, China.
| | - Jing Cheng
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Xi Peng
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
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Wang S, Tan S, Gao Y, Liu Q, Ying L, Xiao T, Liu Y, Liu X, Zheng H, Liang D. Learning Joint-Sparse Codes for Calibration-Free Parallel MR Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:251-261. [PMID: 28866485 DOI: 10.1109/tmi.2017.2746086] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The integration of compressed sensing and parallel imaging (CS-PI) has shown an increased popularity in recent years to accelerate magnetic resonance (MR) imaging. Among them, calibration-free techniques have presented encouraging performances due to its capability in robustly handling the sensitivity information. Unfortunately, existing calibration-free methods have only explored joint-sparsity with direct analysis transform projections. To further exploit joint-sparsity and improve reconstruction accuracy, this paper proposes to Learn joINt-sparse coDes for caliBration-free parallEl mR imaGing (LINDBERG) by modeling the parallel MR imaging problem as an - - minimization objective with an norm constraining data fidelity, Frobenius norm enforcing sparse representation error and the mixed norm triggering joint sparsity across multichannels. A corresponding algorithm has been developed to alternatively update the sparse representation, sensitivity encoded images and K-space data. Then, the final image is produced as the square root of sum of squares of all channel images. Experimental results on both physical phantom and in vivo data sets show that the proposed method is comparable and even superior to state-of-the-art CS-PI reconstruction approaches. Specifically, LINDBERG has presented strong capability in suppressing noise and artifacts while reconstructing MR images from highly undersampled multichannel measurements.
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Morris RH, Newton MI. Magnetic resonance sensors. SENSORS (BASEL, SWITZERLAND) 2014; 14:21722-21725. [PMID: 25407909 PMCID: PMC4279558 DOI: 10.3390/s141121722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2014] [Accepted: 11/13/2014] [Indexed: 06/04/2023]
Affiliation(s)
- Robert H Morris
- School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK.
| | - Michael I Newton
- School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK.
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Majumdar A, Ward RK. Non-convex row-sparse multiple measurement vector analysis prior formulation for EEG signal reconstruction. Biomed Signal Process Control 2014. [DOI: 10.1016/j.bspc.2014.04.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Feng Z, Liu F, Jiang M, Crozier S, Guo H, Wang Y. Improved l1-SPIRiT using 3D walsh transform-based sparsity basis. Magn Reson Imaging 2014; 32:924-33. [DOI: 10.1016/j.mri.2014.04.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2012] [Revised: 03/18/2014] [Accepted: 04/12/2014] [Indexed: 10/25/2022]
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