SDR

Sparse and dense hybrid representation via subspace modeling for dynamic MRI

% Distribution code Version 1.0 -- 06/22/2015
%% The current version is not optimized.
% All rights reserved.
% This work should only be used for nonprofit purposes.
%
% Please cite the paper when you use th code:
% The Code is created based on the method described in the following paper
% [1] Q. Liu, S. Wang, D. Liang. Sparse and dense hybrid representation via subspace modeling for dynamic MRI,
% Computerized Medical Imaging and Graphics, 56: 24-37, 2017.
% Copyright 2015, Nanchang University.
% The code and the algorithm are for non-comercial use only.
%% the demo produces the results shown in Fig. 14 of the ref.[1].
% The data phan.mat is available at: https://drive.google.com/drive/folders/0B3EiIvcKNZj8fkplX1JGR21yNjdORkhralp1NGxNb1RTRGFfOWZ0dGthNk5CeVpBV1FWZVE.

Visual illustration of the signal characteristics with two-subspace modeling.

The dMRI series data is modeled as the sum of a static component and a motion features component under the desired dictionary V.

Visual illustration of the coefficients in two subspaces.

Different types of separation into low-rank and sparse components using SDR.

From top to bottom: the low-rank, sparse and the final result.

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