Abstract #0475

Evaluation of Deep Learning Models for Processing Lactate-Edited MR Spectroscopic Imaging in Patients with Glioma

Sana Vaziri¹, Adam W Autry¹, Marisa Lafontaine¹, Janine Lupo¹, Susan Chang², and Yan Li¹

¹Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, ²Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States

Synopsis

Keywords: Data Processing, SpectroscopyThe use of deep learning models for frequency and phase correction of spectral data was evaluated to reduce processing time of 3D MRSI datasets acquired from patients with glioma. Models for frequency and phase offset estimation were trained for data prior to coil-combination. Separate models for baseline removal of coil-combined data were similarly trained and evaluated. The voxel-wise peaks for spectra processed using the standard approach were evaluated and compared to the spectra processed using the proposed deep learning approach. Compared to standard processing, deep learning processing produced spectra with comparable SNR and linewidths in a shorter processing time.

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