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MR fingerprinting (MRF) is a framework to simultaneously quantify multiple tissue properties. Acquisition parameters are varied pseudo-randomly and each signal evolution is matched with a dictionary of simulated entries. However, dictionary methods are computationally and memory intensive. Deep learners (DL) are capable of mapping complex MRF signal evolutions to a quantitative parametric space, reducing the computational requirements and reconstruction time; yet fail to perform as well in the setting of noise. Drawing from natural language processing (NLP) we proposed a transfer learning (TL) model to improve MRF parametric estimates with realistic noise levels. The weights of a network trained on clean data are used to instantiate the weights of a noisy model. The model is constrained to learn noise invariant features, by freezing the last layer. Signal evolutions were modeled using a recurrent neural network (RNN) to reconstruct T1, T2, and the apparent diffusion coefficient (ADC). Compared to a model trained with noise, but without TL our approached resulted in a 15% reduction in mean squared error (MSE). Monte Carlo simulations performed at varying SNR (10-60 dB) showed our method yielded losses comparable to the clean model at higher SNRs and proved more robust in the setting of noise at lower SNRs.
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Friday 8th July
Poster Session 3.2 - onsite 11:00 - 12:00, virtual 15:20 - 16:20 (UTC+2)
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