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Abstract #4320

Accurate  parameter estimation using scan-specific unsupervised deep learning for relaxometry and MR fingerprinting

Mengze Gao1, Huihui Ye2, Tae Hyung Kim3,4, Zijing Zhang2, Seohee So5, and Berkin Bilgic3,4
1Department of Precision Instrument, Tsinghua University, Beijing, China, 2State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China, 3Harvard Medical School, Boston, MA, United States, 4Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, 5School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea, Republic of


We propose an unsupervised convolutional neural network (CNN) for relaxation parameter estimation. This network incorporates signal relaxation and Bloch simulations while taking advantage of residual learning and spatial relations across neighboring voxels. Quantification accuracy and robustness to noise is shown to be significantly improved compared to standard parameter estimation methods in numerical simulations and in vivo data for multi-echo T2 and T2* mapping. The combination of the proposed network with subspace modeling and MR fingerprinting (MRF) from highly undersampled data permits high quality T1 and T2 mapping.

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