1Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Korea, Republic of, 2Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Korea, Republic of
Dynamic contrast-enhanced magnetic resonance angiography(DCE-MRA) has been widely used for diagnostic assessment in clinical practices. To enhance the conspicuity of arteries relative to unwanted background tissues, subtraction between the pre-contrast and the post-contrast images was typically performed displaying maximum intensity projection images(MIP). Nevertheless, if there exists non-stationary signal transition due to time-drifting field inhomogeneity, and subject motion etc, the subtraction leads to incomplete background suppression, impairing the detectability of arteries as well as small vessel particularly at high reduction factors. In this work, we propose a novel DCE-MRA method exploiting motion subspace learning and sparsity priors for robust angiogram separation, in which the motion subspace is learned using partial Casorati matrix without any motion information while image reconstruction with sparsity priors is performed to jointly estimate motion-induced artifacts and DCE angiograms of interest under the framework of the decomposition. Simulation and experimental studies show that the proposed method is highly competitive with the competing methods including subtraction and fast reconstruction techniques.