Samuel T Ting1,2, Rizwan Ahmad3,2, Ning Jin4, Jason Craft5, Juliana Serafim da Silveira6, Hui Xue7, Orlando P Simonetti1,2,6,8. 1. Department of Biomedical Engineering, The Ohio State University, Columbus, Ohio, USA. 2. Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University, Columbus, Ohio, USA. 3. Department of Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio, USA. 4. Siemens Medical Solutions, Columbus, Ohio, USA. 5. Division of Cardiology, Advocate Christ Medical Center, Oak Lawn, Illinois, USA. 6. Department of Internal Medicine, Division of Cardiovascular Medicine, The Ohio State University, Columbus, Ohio, USA. 7. The National Heart, Lung and Blood Institute, The National Institutes of Health, Bethesda, Maryland, USA. 8. Department of Radiology, The Ohio State University, Columbus, Ohio, USA.
Abstract
PURPOSE: Sparsity-promoting regularizers can enable stable recovery of highly undersampled magnetic resonance imaging (MRI), promising to improve the clinical utility of challenging applications. However, lengthy computation time limits the clinical use of these methods, especially for dynamic MRI with its large corpus of spatiotemporal data. Here, we present a holistic framework that utilizes the balanced sparse model for compressive sensing and parallel computing to reduce the computation time of cardiac MRI recovery methods. THEORY AND METHODS: We propose a fast, iterative soft-thresholding method to solve the resulting ℓ1-regularized least squares problem. In addition, our approach utilizes a parallel computing environment that is fully integrated with the MRI acquisition software. The methodology is applied to two formulations of the multichannel MRI problem: image-based recovery and k-space-based recovery. RESULTS: Using measured MRI data, we show that, for a 224 × 144 image series with 48 frames, the proposed k-space-based approach achieves a mean reconstruction time of 2.35 min, a 24-fold improvement compared a reconstruction time of 55.5 min for the nonlinear conjugate gradient method, and the proposed image-based approach achieves a mean reconstruction time of 13.8 s. CONCLUSION: Our approach can be utilized to achieve fast reconstruction of large MRI datasets, thereby increasing the clinical utility of reconstruction techniques based on compressed sensing. Magn Reson Med 77:1505-1515, 2017.
PURPOSE: Sparsity-promoting regularizers can enable stable recovery of highly undersampled magnetic resonance imaging (MRI), promising to improve the clinical utility of challenging applications. However, lengthy computation time limits the clinical use of these methods, especially for dynamic MRI with its large corpus of spatiotemporal data. Here, we present a holistic framework that utilizes the balanced sparse model for compressive sensing and parallel computing to reduce the computation time of cardiac MRI recovery methods. THEORY AND METHODS: We propose a fast, iterative soft-thresholding method to solve the resulting ℓ1-regularized least squares problem. In addition, our approach utilizes a parallel computing environment that is fully integrated with the MRI acquisition software. The methodology is applied to two formulations of the multichannel MRI problem: image-based recovery and k-space-based recovery. RESULTS: Using measured MRI data, we show that, for a 224 × 144 image series with 48 frames, the proposed k-space-based approach achieves a mean reconstruction time of 2.35 min, a 24-fold improvement compared a reconstruction time of 55.5 min for the nonlinear conjugate gradient method, and the proposed image-based approach achieves a mean reconstruction time of 13.8 s. CONCLUSION: Our approach can be utilized to achieve fast reconstruction of large MRI datasets, thereby increasing the clinical utility of reconstruction techniques based on compressed sensing. Magn Reson Med 77:1505-1515, 2017.
Authors: Rizwan Ahmad; Charles A Bouman; Gregery T Buzzard; Stanley Chan; Sizhuo Liu; Edward T Reehorst; Philip Schniter Journal: IEEE Signal Process Mag Date: 2020-01-17 Impact factor: 12.551
Authors: Christopher Ebersole; Rizwan Ahmad; Adam V Rich; Lee C Potter; Huiming Dong; Arunark Kolipaka Journal: Magn Reson Med Date: 2018-01-15 Impact factor: 4.668
Authors: Chong Chen; Yingmin Liu; Philip Schniter; Ning Jin; Jason Craft; Orlando Simonetti; Rizwan Ahmad Journal: Magn Reson Med Date: 2019-01-21 Impact factor: 4.668
Authors: Aaron Pruitt; Adam Rich; Yingmin Liu; Ning Jin; Lee Potter; Matthew Tong; Saurabh Rajpal; Orlando Simonetti; Rizwan Ahmad Journal: Magn Reson Med Date: 2020-09-30 Impact factor: 4.668