Improved reconstruction of non-Cartesian magnetic resonance imaging data through total variation minimization and POCS optimization.
In the article, an iterative reconstruction algorithm based on total variation minimization and POCS optimization for non-Cartesian K-space data is proposed. The proposed algorithm interpolates non-Cartesian data onto a 2D Cartesian grid using gridding method first, and then during the iterative process of total variation minimization, the frequency values on grid points near the measured data are replaced with the interpolated ones according to POCS. The experiments on simulated and real data show that the proposed method can reconstruct image more accurately and rapidly than constrained total variation minimization method.
Feng
Yanqiu
Y
Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Liu
Ping
P
Li
Benxing
B
Yu
Lihong
L
Chen
Wufan
W
D000465:Algorithms; D001698:Biomedical Engineering / Q000379:methods; D001931:Brain Mapping / Q000295:instrumentation / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000295:instrumentation* / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results