Abstract #2423
STEP: Self-supporting Tailored k-space Estimation for Parallel imaging reconstruction
Zechen Zhou 1 , Jinnan Wang 2,3 , Niranjan Balu 3 , Rui Li 1 , and Chun Yuan 1,3
1
Center for Biomedical Imaging Research,
Department of Biomedical Engineering, School of
Medicine, Tsinghua University, Beijing, China,
2
Philips
Research North America, Briarcliff Manor, NY, United
States,
3
Vascular
Imaging Lab, Department of Radiology, University of
Washington, Seattle, WA, United States
Parallel Imaging (PI) has been widely used for MR
imaging acceleration in clinical applications. However,
current subspace based PI methods may not provide
accurate reconstruction when it comes to spatially
variant correlations due to the varying signal-to-noise
characteristics. In this work, we developed a
Self-supporting Tailored k-space Estimation for Parallel
imaging reconstruction (STEP) technique to further
improve the subspace PI reconstruction and the proposed
algorithm has demonstrated its performance of reduced
noise amplification, less aliasing artifacts and better
structure preservation when compared to the existing PI
algorithms.
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