Abstract #4651

Comparison of Motion Correction Schemes for Cardiac Diffusion Tensor Imaging Using Image Registration

Yuchi Liu¹, Danielle Kara², Thomas Garrett², Ning Jin³, Peter Speier⁴, Deborah Kwon^2,5,6, Xiaoming Bi⁷, and Christopher Nguyen^2,5,6,8

¹Siemens Medical Solutions USA, Inc., Cleveland, OH, United States, ²Cardiovascular Innovation Research Center, Heart Vascular Thoracic Institute, Cleveland Clinic, Cleveland, OH, United States, ³Siemens Medical Solutions USA, Inc., Columbus, OH, United States, ⁴Siemens Healthcare, Erlangen, Germany, ⁵Cardiovascular Medicine, Heart Vascular Thoracic Institute, Cleveland Clinic, Cleveland, OH, United States, ⁶Imaging Institute, Cleveland Clinic, Cleveland, OH, United States, ⁷Siemens Medical Solutions USA, Inc., Los Angeles, CA, United States, ⁸Department of Biomedical Engineering, Case Western Reserve University & Cleveland Clinic, Cleveland, OH, United States

Synopsis

Keywords: Motion Correction, Motion Correction

Motivation: Motion correction (MOCO) is critical for free-breathing cardiac DTI, but it is challenging due to low SNR and variations in b-value contrast in acquired images.

Goal(s): To compare MOCO schemes for cardiac DTI with the ultimate goal of implementing the entire post-processing workflow inline on the scanner.

Approach: Three image registration-based MOCO schemes were compared with a previous low rank tensor (LRT) method in 7 volunteers.

Results: Compared with the LRT method, proposed MOCO methods achieved smaller standard deviations of MD and FA values, more smoothly varying helical structure in HA maps, and visually less motion of the heart in diffusion weighted images.

Impact: A robust and fast motion correction approach for free-breathing cardiac diffusion tensor imaging (DTI) improves the quality of diffusion maps, and facilitates clinical translation of cardiac DTI.

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