Abstract #4393
A general Hierarchical Mapping Framework (HMF) for coil compression
Stephen F Cauley 1 , Berkin Bilgic 1,2 , Jonathan R Polimeni 1,2 , Himanshu Bhat 3 , Lawrence L Wald 1,4 , and Kawin Setsompop 1,2
1
A.A. Martinos Center for Biomedical Imaging,
Dept. of Radiology, MGH, Charlestown, MA, United States,
2
Harvard
Medical School, Boston, MA, United States,
3
Siemens
Medical Solutions Inc, Malvern, PA, United States,
4
Harvard-MIT
Division of Health Sciences and Technology, Cambridge,
MA, United States
High channel-count array coils have enabled accurate
parallel imaging (PI) reconstruction at very high
acceleration factors. However, the computational scaling
of many PI algorithms leads to long reconstruction
times. Methods such as SVD are applicable to a wide
range of k-space sampling patterns but produce poor
image quality. Other improved methods such as
Geometric-decomposition Coil Compression are tailored
for Cartesian sampling. In this work, we introduce a
Hierarchical Mapping Framework (HMF) for coil
compression that improves upon previously proposed
algorithms. The additional flexibility provided by HMF
should enable accurate PI reconstruction for many
acquisition types.
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