High-resolution magnetic resonance spectroscopic imaging quantification at 3T is affected by a poor signal to noise ratio as well as signal contamination from macromolecules and field inhomogeneities. For the metabolite identification problem, the convolutional neural network (CNN) algorithm seems to be a very adapted tool. We demonstrate here the performance our CNN on metabolite concentration estimation compared to the well-used LCModel. Achieving better accuracy on simulated datasets, we obtained also comparable results as LCModel on concentration maps on in-vivo data but with 103 times less computing time.
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