Abstract #2018

Deep Learning Based MRS Metabolite Quantification: CNN and ResNet versus Non Linear Least Square Fitting

Federico Turco¹, Irena Zubak², and Johannes Slotboom¹

¹Institute of Diagnostic and Interventional Neuroradiology / SCAN, University Hospital Bern and Inselspital, University Bern, Bern, Switzerland, ²Neurosurgery, University Hospital Bern and Inselspital, University Bern, Bern, Switzerland

We present and compare two different deep CNN architectures, and VGG-like and an ResNet. We aim at performing metabolite quantification, and compare their performance to a NLLS-fitting algorithm (TDFDfit). We show the performance of the two AI algorithms in a set of 2 in vivo cases as well as in a brain tumor patient. We found that our ResNet outperform the CNN when predicting spatial distribution of metabolite concentration and showed a bigger correlation with the metabolites predicted by NLLS-fitting algorithm.

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