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Abstract #3400

Gradient nonlinearity-induced bias calibration and correction in diffusion imaging using DIADEM and a simple, uniform gel phantom

Myung-Ho In1, Uten Yarach1, Daehun Kang1, Ek Tsoon Tan2, Erin M Gray1, Nolan K Meyer1, Joshua D Trzasko1, Yunhong Shu1, John Huston1, and Matt A Bernstein1

1Department of Radiology, Mayo Clinic, Rochester, MN, United States, 2GE Global Research, Niksayuna, NY, United States

This study reports a novel gradient nonlinearity (GNL) calibration approach using DIADEM (Distortion-free Imaging Approach with a Double Encoding Method) diffusion imaging. Unlike standard diffusion-weighted echo-planar-imaging (DW-EPI), DIADEM is free from DW-EPI distortions. This allows GNL calibration with a uniform phantom, since confounding effects between DW-EPI and GNL-induced distortions in the calibration are separated. Direct bias correction could be applied to the corresponding in-vivo data from the DIADEM scans, which results in reliable quantitative diffusion imaging. The feasibility was successfully demonstrated in phantom and in-vivo on a compact 3T system.

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