Abstract #3815

From Matrix to Tensor: Compressed Sensing Dynamic MRI Using Tensor Based Sparsity

Yeyang Yu¹, Jin Jin², Feng Liu², Stuart Crozier², Mingjian Hong³

¹The School of Information Technology and Electrical and Engineering, The University of Queensland, Brisbane, QLD, Australia; ²The School of Information Technology and Electrical and Engineering, The University of Queensland, Brisbane, QLD, Australia; ³School of Software Engineering, Chongqing Univeristy, Chongqing, China

In this work, we introduce the conception of tensor sparsity for Compressed Sensing dynamic MRI. Conventionally, the spatial and temporal information were then sparsified independently and sequentially. Therefore the spatial-temporal correlation may not be sufficiently exploited. This work applys the Tucker model based Higher-order Singular Value Decomposition (HOSVD) in the Compressed Sensing dynamic MRI framework. Instead of treating the 3D/4D data as series of 2D images, HOSVD inherits the high-dimensional data format, leading to significantly improved dMRI reconstructions compared with those well-established CS-dMRI methods. The advantages of the tensor sparsity in terms of reconstruction accuracy have been demonstrated in a given cardiac dynamic MRI study.