title: Cardiac diffusion tensor imaging based on compressed sensing using joint sparsity and low-rank approximation.
abstract: Diffusion tensor magnetic resonance (DTMR) imaging and diffusion tensor imaging (DTI) have been widely used to probe noninvasively biological tissue structures. However, DTI suffers from long acquisition times, which limit its practical and clinical applications. This paper proposes a new Compressed Sensing (CS) reconstruction method that employs joint sparsity and rank deficiency to reconstruct cardiac DTMR images from undersampled k-space data. Diffusion-weighted images acquired in different diffusion directions were firstly stacked as columns to form the matrix. The matrix was row sparse in the transform domain and had a low rank. These two properties were then incorporated into the CS reconstruction framework. The underlying constrained optimization problem was finally solved by the first-order fast method. Experiments were carried out on both simulation and real human cardiac DTMR images. The results demonstrated that the proposed approach had lower reconstruction errors for DTI indices, including fractional anisotropy (FA) and mean diffusivities (MD), compared to the existing CS-DTMR image reconstruction techniques.
authors.affiliation: Metislab, LIA CNRS, Harbin Institute of Technology, Harbin, Heilongjiang, China.|CREATIS, INSA Lyon, INSA Lyon, University of Lyon, Villeurbanne, France.
authors.forename: Jianping
authors.initials: J
authors.lastname: Huang
authors.affiliation: College of Computer Science and Technology, Guizhou University, Guiyang, Guizhou, China.
authors.forename: Lihui
authors.initials: L
authors.lastname: Wang
authors.affiliation: Metislab, LIA CNRS, Harbin Institute of Technology, Harbin, Heilongjiang, China.
authors.forename: Chunyu
authors.initials: C
authors.lastname: Chu
authors.affiliation: Metislab, LIA CNRS, Harbin Institute of Technology, Harbin, Heilongjiang, China.
authors.forename: Yanli
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Metislab, LIA CNRS, Harbin Institute of Technology, Harbin, Heilongjiang, China.|CREATIS, INSA Lyon, INSA Lyon, University of Lyon, Villeurbanne, France.
authors.forename: Wanyu
authors.initials: W
authors.lastname: Liu
authors.affiliation: Metislab, LIA CNRS, Harbin Institute of Technology, Harbin, Heilongjiang, China.|CREATIS, INSA Lyon, INSA Lyon, University of Lyon, Villeurbanne, France.
authors.forename: Yuemin
authors.initials: Y
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006321:Heart; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*
keywords: Diffusion tensor imaging; compressed sensing; low rank; sparsity constraint