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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