Parallel magnetic resonance imaging (pMRI) is a technique to accelerate the magnetic resonance imaging process. The problem of reconstructing an image from the collected pMRI data is ill-posed. Regularization on multi-coil images by tight-frame systems is proposed to reduce the aliasing artifacts on reconstructed images. Numerical experiments for in-silico and in-vivo data sets are provided to demonstrate the superiority of the 3-D regularization model and the efficiency of our proposed algorithm for pMRI reconstruction.

This presentation is part of Minisymposium “MS41 - Framelets, Optimization, and Image Processing (3 parts)”
organized by: Xiaosheng Zhuang (City University of Hong Kong) , Lixin Shen (Syracuse University) , Bin Han (University of Alberta) , Yan-Ran Li (Shenzhen Univeristy) .

Authors:

Yan-Ran Li (Shenzhen Univeristy)

Keywords:

image reconstruction, inverse problems, nonlinear optimization, pmri, regularization, tight-frame