Abstract #4211

Accelerated Dynamic MRI Using Tensor Dictionaries Learning

Jinhong Huang^1,2, Biaoshui Liu¹, Gaohang Yu^1,2, Yanqiu Feng¹, and Wufan Chen¹

¹School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, South Medical University, Guangzhou, China, People's Republic of, ²School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, China, People's Republic of

Conventional CS methods treat a 2D/3D image to be reconstructed as a vector. However, many data types do not lend themselves to vector data representation, and this vectorization based model may lose the inherent spatial structure property of original data and suffer from curse of dimensionality that occurs when working with high-dimensional data. In this work, we introduce a novel tensor dictionary learning method for dynamic MRI reconstruction. Numerical experiments on synthetic data and in vivo data show approximately 2 dB improvement in PSNR presented by the proposed scheme over existing method with overcomplete dictionary learning.

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