Keywords: Cartilage, MR Fingerprinting, Knee cartilage, Deep learning
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.
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