Physics Informed Neural Network for Fitting Ultra High Field Magnetic Resonance Spectrum

Traditional neural networks have shown promising performance in fitting proton Magnetic Resonance Spectroscopy (1H-MRS). However, they have difficulty adapting to low abundance metabolites and have limitations that make them difficult to use in clinical applications: the training database needs to be large and they do not take into account the physical reality of 1H-MRS. Recently, a new neural network method has emerged, the Physics Informed Neural Network (PINN), which links neural networks to physical laws. It provides a promising solution for fitting 1H-MRS data. In this paper, we propose a PINN framework for fitting 1H-MRS 7T data. The proposed method consists of an end-to-end one-dimensional CNN encoder-decoder model integrating a new method of discriminative spectral parameter extraction and weight adjustment guided by the physical reality of 1H-MRS. The data used in this article are collected in our center, which contain 90 1H-MRS samples, healthy and pathological, from multilobe white and grey matter. The proposed framework gives similar and better results than the current scientific literature with a speed of 0.030 ms for the 1H-MRS 7T fitting task. Thus, our model provide accurate and fast fitting of brain metabolites in clinical contexts that can discriminate pathologies.