Azar Tolouee1, Javad Alirezaie2, Paul Babyn3. 1. Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, M5B2K3, Canada. 2. Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, M5B2K3, Canada. javad@ee.ryerson.ca. 3. Department of Medical Imaging, University of Saskatchewan and Saskatoon Health Region, Saskatoon, SK, Canada.
Abstract
OBJECTIVES: In dynamic cardiac magnetic resonance imaging (MRI), the spatiotemporal resolution is often limited by low imaging speed. Compressed sensing (CS) theory can be applied to improve imaging speed and spatiotemporal resolution. The combination of compressed sensing and low-rank matrix completion represents an attractive means to further increase imaging speed. By extending prior work, a Motion-Compensated Data Decomposition (MCDD) algorithm is proposed to improve the performance of CS for accelerated dynamic cardiac MRI. MATERIALS AND METHODS: The process of MCDD can be described as follows: first, we decompose the dynamic images into a low-rank (L) and a sparse component (S). The L component includes periodic motion in the background, since it is highly correlated among frames, and the S component corresponds to respiratory motion. A motion-estimation/motion-compensation (ME-MC) algorithm is then applied to the low-rank component to reconstruct a cardiac motion compensated dynamic cardiac MRI. RESULTS: With validations on the numerical phantom and in vivo cardiac MRI data, we demonstrate the utility of the proposed scheme in significantly improving compressed sensing reconstructions by minimizing motion artifacts. The proposed method achieves higher PSNR and lower MSE and HFEN for medium to high acceleration factors. CONCLUSION: The proposed method is observed to yield reconstructions with minimal spatiotemporal blurring and motion artifacts in comparison to the existing state-of-the-art methods.
OBJECTIVES: In dynamic cardiac magnetic resonance imaging (MRI), the spatiotemporal resolution is often limited by low imaging speed. Compressed sensing (CS) theory can be applied to improve imaging speed and spatiotemporal resolution. The combination of compressed sensing and low-rank matrix completion represents an attractive means to further increase imaging speed. By extending prior work, a Motion-Compensated Data Decomposition (MCDD) algorithm is proposed to improve the performance of CS for accelerated dynamic cardiac MRI. MATERIALS AND METHODS: The process of MCDD can be described as follows: first, we decompose the dynamic images into a low-rank (L) and a sparse component (S). The L component includes periodic motion in the background, since it is highly correlated among frames, and the S component corresponds to respiratory motion. A motion-estimation/motion-compensation (ME-MC) algorithm is then applied to the low-rank component to reconstruct a cardiac motion compensated dynamic cardiac MRI. RESULTS: With validations on the numerical phantom and in vivo cardiac MRI data, we demonstrate the utility of the proposed scheme in significantly improving compressed sensing reconstructions by minimizing motion artifacts. The proposed method achieves higher PSNR and lower MSE and HFEN for medium to high acceleration factors. CONCLUSION: The proposed method is observed to yield reconstructions with minimal spatiotemporal blurring and motion artifacts in comparison to the existing state-of-the-art methods.
Authors: Li Feng; Ricardo Otazo; Hong Jung; Jens H Jensen; Jong C Ye; Daniel K Sodickson; Daniel Kim Journal: Magn Reson Med Date: 2011-02-28 Impact factor: 4.668