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Abstract #2523

Prior-knowledge MRS Metabolite Quantification using Deep Learning Frameworks: A proof-of-concept

Federico Turco1 and Johannes Slotboom1
1SCAN, Institute of Diagnostic and Interventional Neuroradiology, Bern, Switzerland


We implemented a mathematical representation of a prior-knowledge model in a Neural Network using a Tensorflow. The trainables tensors are directly the free parameters of the model and we do metabolite quantification by overfitting the output to the signal that we want to replicate. We found that this way of fitting has a relatively low performance in time domain but similar to the state-of-the-art (TDFDFit) when using frequency domain. In addition we have a faster method and it can be used in future works as a component of a more complex network.

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