Congyu Liao1, Ying Chen1, Xiaozhi Cao1, Song Chen1, Hongjian He1, Merry Mani2, Mathews Jacob3, Vincent Magnotta4, Jianhui Zhong1,5. 1. Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China. 2. Department of Psychiatry, University of Iowa, Iowa City, Iowa, USA. 3. Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA. 4. Department of Radiology, University of Iowa, Iowa City, Iowa, USA. 5. Center for Innovative and Collaborative Detection and Treatment of Infectious Diseases, Zhejiang University, Hangzhou, Zhejiang, China.
Abstract
PURPOSE: To propose a novel reconstruction method using parallel imaging with low rank constraint to accelerate high resolution multishot spiral diffusion imaging. THEORY AND METHODS: The undersampled high resolution diffusion data were reconstructed based on a low rank (LR) constraint using similarities between the data of different interleaves from a multishot spiral acquisition. The self-navigated phase compensation using the low resolution phase data in the center of k-space was applied to correct shot-to-shot phase variations induced by motion artifacts. The low rank reconstruction was combined with sensitivity encoding (SENSE) for further acceleration. The efficiency of the proposed joint reconstruction framework, dubbed LR-SENSE, was evaluated through error quantifications and compared with ℓ1 regularized compressed sensing method and conventional iterative SENSE method using the same datasets. RESULTS: It was shown that with a same acceleration factor, the proposed LR-SENSE method had the smallest normalized sum-of-squares errors among all the compared methods in all diffusion weighted images and DTI-derived index maps, when evaluated with different acceleration factors (R = 2, 3, 4) and for all the acquired diffusion directions. CONCLUSION: Robust high resolution diffusion weighted image can be efficiently reconstructed from highly undersampled multishot spiral data with the proposed LR-SENSE method. Magn Reson Med 77:1359-1366, 2017.
PURPOSE: To propose a novel reconstruction method using parallel imaging with low rank constraint to accelerate high resolution multishot spiral diffusion imaging. THEORY AND METHODS: The undersampled high resolution diffusion data were reconstructed based on a low rank (LR) constraint using similarities between the data of different interleaves from a multishot spiral acquisition. The self-navigated phase compensation using the low resolution phase data in the center of k-space was applied to correct shot-to-shot phase variations induced by motion artifacts. The low rank reconstruction was combined with sensitivity encoding (SENSE) for further acceleration. The efficiency of the proposed joint reconstruction framework, dubbed LR-SENSE, was evaluated through error quantifications and compared with ℓ1 regularized compressed sensing method and conventional iterative SENSE method using the same datasets. RESULTS: It was shown that with a same acceleration factor, the proposed LR-SENSE method had the smallest normalized sum-of-squares errors among all the compared methods in all diffusion weighted images and DTI-derived index maps, when evaluated with different acceleration factors (R = 2, 3, 4) and for all the acquired diffusion directions. CONCLUSION: Robust high resolution diffusion weighted image can be efficiently reconstructed from highly undersampled multishot spiral data with the proposed LR-SENSE method. Magn Reson Med 77:1359-1366, 2017.
Authors: Congyu Liao; Jason Stockmann; Qiyuan Tian; Berkin Bilgic; Nicolas S Arango; Mary Kate Manhard; Susie Y Huang; William A Grissom; Lawrence L Wald; Kawin Setsompop Journal: Magn Reson Med Date: 2019-08-01 Impact factor: 4.668
Authors: Yuxin Hu; Evan G Levine; Qiyuan Tian; Catherine J Moran; Xiaole Wang; Valentina Taviani; Shreyas S Vasanawala; Jennifer A McNab; Bruce A Daniel; Brian L Hargreaves Journal: Magn Reson Med Date: 2018-10-22 Impact factor: 4.668
Authors: Erpeng Dai; Philip K Lee; Zijing Dong; Fanrui Fu; Kawin Setsompop; Jennifer A McNab Journal: IEEE Trans Med Imaging Date: 2021-12-30 Impact factor: 10.048