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Abstract #2888

MR Fingerprinting Reconstruction using Convolutional Neural Network (MRF-CNN)

Qiang Zhang1, Rui Guo1, Huikun Qi1, Di Cui2, Edward S Hui2, Shuo Chen1, Hua Guo1, and Huijun Chen1

1Department of Biomedical Engineering, Tsinghua University, Beijing, China, 2Department of ​Diagnostic ​Radiology, The University of Hong Kong, Hong Kong, China

The purpose of this study is to develop a MR fingerprinting (MRF) reconstruction algorithm using convolutional neural network (MRF-CNN). Better MRF reconstruction fidelity was achieved using our MRF-CNN compared with that of the conventional approach (R2 of T1: 0.98 vs 0.97, R2 of T2: 0.97 vs 0.59). This study further demonstrated the performance of our MRF-CNN, which was retrained using MR signal evolutions in the continuous parameter space with various levels of Gaussian noise, amidst noise contamination, suggesting that it may likely be a better alternative than the conventional MRF dictionary matching approach.

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