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Abstract #2845

31P Magnetic Resonance Spectroscopy analyzed and quantified by Convolutional Neural Network (CNN)

Julien Songeon1, Sebastien Courvoisier1, Antoine Klauser1, Alban Longchamp2, Jean-Marc Corpataux2, Leo Buhler3, and Fran├žois Lazeyras1
1Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland, 2Department of Vascular Surgery, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland, 3Faculty of Science and Medicine, Section of Medicine, University of Fribourg, Fribourg, Switzerland

Phosphorus magnetic resonance spectroscopy imaging (31P-MRSI) allows the probing of biological compounds that hold fundamental cellular information. High resolution MRSI at 3T suffers from low signal-to-noise ratio (SNR) inherent to the nuclear low sensitivity. This is accentuated in the MRSI in comparison to unlocalized free induction decay (FID) where acquired volumes are smaller and consequently lower the SNR. Our Convolutional Neural Network (CNN) based algorithm perform efficient quantification of metabolite and is compared to last-square fitting algorithm. Our model was trained with a simulated dataset and tested with both simulated spectra and real spectra from 3D 31P-MRSI acquired in kidneys.

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