Fine-Tuning Deep Learning Model For Quantitative Knee Joint Mapping with MR Fingerprinting

Motivation: Estimating MRF quantitative parameters with neural networks (NNs) is faster than dictionary-matching methods (DMs), and it has the advantage of providing continuously distributed parameters. Goal(s): We investigate different aspects of NN training and evaluate its quantitative MRF performance and compare them with DMs. Approach: We exploit how training data sizes, noise levels, and SVD compression sizes affect the MRF performance of the NNs and compare them with DMs. Results: The NN provides a faster way of multi-parametric mapping from NIST/ISMRM phantom and knee joint MRF data sets with comparable performance to DMs. Impact: Well-tuned NN is much more efficient for quantitative MRF, particularly for the knee joint. Besides computational speed, fine-tuning can also increase the performance and robustness to noise.