Accelerating Dynamic Cardiac MR Imaging Using Structured Sparse Representation

Nian Cai,Shasha Zhu,Dong Liang,Shengru Wang

doi:10.1155/2013/160139

Abstract

Compressed sensing (CS) has produced promising results on dynamic cardiac MR imaging by exploiting the sparsity in image series. In this paper, we propose a new method to improve the CS reconstruction for dynamic cardiac MRI based on the theory of structured sparse representation. The proposed method user the PCA subdictionaries for adaptive sparse representation and suppresses the sparse coding noise to obtain good reconstructions. An accelerated iterative shrinkage algorithm is used to solve the optimization problem and achieve a fast convergence rate. Experimental results demonstrate that the proposed method improves the reconstruction quality of dynamic cardiac cine MRI over the state-of-the-art CS method.

Highlights

Dynamic cardiac cine MR imaging aims at simultaneously providing a series of dynamic magnetic resonance image in spatial and temporal domains (x-t space) at a high frame rate
One is based on compressed sensing (CS) theory [11, 12] utilizing the sparsity in dynamic images to be reconstructed, and the other is based on the partial separable theory [13] exploiting the low-rank property of images in x-t space
Jung et al [7, 9] uncovered an intriguing link between the compressed sensing and k-t BLAST/SENSE and proposed the k-t FOCUSS algorithm to achieve high spatiotemporal resolution in cardiac cine imaging

Summary

Introduction

Dynamic cardiac cine MR imaging aims at simultaneously providing a series of dynamic magnetic resonance image in spatial and temporal domains (x-t space) at a high frame rate. It usually acquires the k-space at each time frame and collects the raw data in the spatial frequency and temporal domain, the so called k-t space. One is based on compressed sensing (CS) theory [11, 12] utilizing the sparsity in dynamic images to be reconstructed, and the other is based on the partial separable theory [13] exploiting the low-rank property of images in x-t space. Liang et al [5] developed k-t iterative support detection (k-t ISD) method to further utilize the detected partial support information besides the sparsity in cardiac cine images

Methods

Results

Conclusion