Meeting Banner
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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here