Rank-One and Transformed Sparse Decomposition for Dynamic Cardiac MRI.

Abstract:

:It is challenging and inspiring for us to achieve high spatiotemporal resolutions in dynamic cardiac magnetic resonance imaging (MRI). In this paper, we introduce two novel models and algorithms to reconstruct dynamic cardiac MRI data from under-sampled k - t space data. In contrast to classical low-rank and sparse model, we use rank-one and transformed sparse model to exploit the correlations in the dataset. In addition, we propose projected alternative direction method (PADM) and alternative hard thresholding method (AHTM) to solve our proposed models. Numerical experiments of cardiac perfusion and cardiac cine MRI data demonstrate improvement in performance.

journal_name

Biomed Res Int

authors

Xiu X,Kong L

doi

10.1155/2015/169317

subject

Has Abstract

pub_date

2015-01-01 00:00:00

pages

169317

eissn

2314-6133

issn

2314-6141

journal_volume

2015

pub_type

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