Abstract #4320

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

Mengze Gao¹, Huihui Ye², Tae Hyung Kim^3,4, Zijing Zhang², Seohee So⁵, and Berkin Bilgic^3,4

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

Synopsis

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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