title: Analysis of cardiac diffusion tensor magnetic resonance images using sparse representation.
abstract: In cardiac diffusion tensor magnetic resonance imaging (DT-MRI), low signal-to-noise ratio (SNR) inherently hampers the measurement accuracy of myocardium fiber structures. This paper presents a new method for filtering diffusion weighted (DW) images in cardiac DT-MRI. The method is based on sparse representation through using basis pursuit denoising (BPDN) algorithm allowing seeking overall sparest solution. It decomposes useful structures in DW images into sparsely representing atoms with Heaviside dictionary, while yielding nonsparse representation on noise, which leads to the separation of the noise from the image's useful structures. The proposed method is evaluated on both simulated and real cardiac DW images.
authors.affiliation: CREATIS-LRMN, France
authors.forename: Lijun
authors.initials: L
authors.lastname: Bao
authors.forename: Yuemin
authors.initials: Y
authors.lastname: Zhu
authors.forename: Wanyu
authors.initials: W
authors.lastname: Liu
authors.forename: Marc
authors.initials: M
authors.lastname: Robini
authors.forename: Zhaobang
authors.initials: Z
authors.lastname: Pu
authors.forename: Isabelle
authors.initials: I
authors.lastname: Magnin
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006321:Heart / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D011859:Radiography