title: Energy preserved sampling for compressed sensing MRI.
abstract: The sampling patterns, cost functions, and reconstruction algorithms play important roles in optimizing compressed sensing magnetic resonance imaging (CS-MRI). Simple random sampling patterns did not take into account the energy distribution in k-space and resulted in suboptimal reconstruction of MR images. Therefore, a variety of variable density (VD) based samplings patterns had been developed. To further improve it, we propose a novel energy preserving sampling (ePRESS) method. Besides, we improve the cost function by introducing phase correction and region of support matrix, and we propose iterative thresholding algorithm (ITA) to solve the improved cost function. We evaluate the proposed ePRESS sampling method, improved cost function, and ITA reconstruction algorithm by 2D digital phantom and 2D in vivo MR brains of healthy volunteers. These assessments demonstrate that the proposed ePRESS method performs better than VD, POWER, and BKO; the improved cost function can achieve better reconstruction quality than conventional cost function; and the ITA is faster than SISTA and is competitive with FISTA in terms of computation time.
authors: School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China ; Brain Imaging Laboratory, Department of Psychiatry, Columbia University, New York, NY 10032, USA ; MRI Unit, New York State Psychiatric Institute, New York, NY 10032, USA.
authors: Yudong
authors: 0000-0002-4870-1493
authors: Y
authors: Zhang
authors: Brain Imaging Laboratory, Department of Psychiatry, Columbia University, New York, NY 10032, USA ; MRI Unit, New York State Psychiatric Institute, New York, NY 10032, USA.
authors: Bradley S
authors: BS
authors: Peterson
authors: School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China.
authors: Genlin
authors: G
authors: Ji
authors: Brain Imaging Laboratory, Department of Psychiatry, Columbia University, New York, NY 10032, USA ; MRI Unit, New York State Psychiatric Institute, New York, NY 10032, USA.
authors: Zhengchao
authors: Z
authors: Dong
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D044962:Data Compression / Q000379:methods*; D064368:Healthy Volunteers; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008962:Models, Theoretical; D019047:Phantoms, Imaging; D011336:Probability; D012016:Reference Values; D012984:Software