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Abstract #4395

Parallel Reconstruction using Patch based K-space Dictionary Learning

Zechen Zhou 1 , Jinnan Wang 2,3 , Niranjan Balu 3 , and Chun Yuan 1,3

1 Center for Biomedical Imaging Research, Tsinghua University, Beijing, China, 2 Philips Research North America, Seattle, Washington, United States, 3 Radiology, University of Washington, Seattle, Washington, United States

Recently, a parallel reconstruction technique SAKE has been developed using Singular Value Decomposition (SVD) to impose low rank property for calibrationless parallel reconstruction, which can improve the result of SPIRiT. We hypothesize that a learned dictionary rather than SVD method can better adapt to acquired data and improve the reconstruction result. In this study, we propose a new patch-based dictionary learning method to estimate the local signal features in k-space and demonstrate its improved performance in-vivo.

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