Parallel Reconstruction using Patch based K-space Dictionary Learning
Zechen Zhou 1 , Jinnan Wang 2,3 , Niranjan Balu 3 , and Chun Yuan 1,3
Center for Biomedical Imaging Research,
Tsinghua University, Beijing, China,
Research North America, Seattle, Washington, United
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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