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Abstract #2669

Ultra-Fast Simultaneous T1rho and T2 Mapping Using Deep Learning

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 T1rho and T2 relaxation maps simultaneously within one scan. The method uses 3D deep convolutional neural networks to exploit the nonlinear relationship between and within the combined subsampled T1rho and T2-weighted images and the combined T1rho and T2 maps, bypassing conventional fitting models. Compare with separated trained relaxation maps, this new method also exploits the autocorrelation and cross-correlation between subsampled echoes. Experiments show that the proposed method is capable of generating T1rho and T2 maps simultaneously from only 3 subsampled echoes within one scan with quantification results comparable to reference maps.

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