Abstract #0253

Deep Learning MR Relaxometry with Joint Spatial-Temporal Under-sampling

Hongyu Li¹, Mingrui Yang², Jeehun Kim², Ruiying Liu¹, Chaoyi Zhang¹, Peizhou Huang¹, Sunil Kumar Gaire¹, Dong Liang³, Xiaojuan Li², and Leslie Ying¹

¹Department of Biomedical Engineering, Department of Electrical Engineering, The State University of New York at Buffalo, Buffalo, NY, United States, ²Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, United States, ³Paul C. Lauterbur Research Center for Biomedical Imaging, Medical AI research center, SIAT, CAS, Shenzhen, China

This abstract presents a deep learning method to generate MR parameter maps from very few subsampled echo images. The method uses deep convolutional neural networks to learn the nonlinear relationship between the subsampled T1rho/T2-weighted images and the T1rho/T2 maps, bypassing the conventional exponential decay models. Experimental results show that the proposed method is able to generate T1rho/T2 maps from only 2 subsampled echo images with quantitative values comparable to those of the T1rho/T2 maps generated from fully-sampled 8 echo images using the conventional exponential decay curve fitting.

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

Join Here