Abstract #4496

Magnetic Resonance Spectroscopy Frequency and Phase Correction Using Convolutional Neural Networks

David Ma¹, Hortense Le¹, Yuming Ye¹, Andrew Laine¹, Jeffrey Lieberman², Douglas Rothman³, Scott Small^2,4,5, and Jia Guo^2,6

¹Department of Biomedical Engineering, Columbia University, New York, NY, United States, ²Department of Psychiatry, Columbia University, New York, NY, United States, ³Radiology and Biomedical Imaging of Biomedical Engineering, Yale University, New Haven, CT, United States, ⁴Department of Neurology, Columbia University, New York, NY, United States, ⁵Taub Institute Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, United States, ⁶Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States

Synopsis

Frequency and Phase Correction (FPC) is an essential technique to resolve frequency and phase shifts that arise in Magnetic Resonance Spectroscopy (MRS). As of today, a deep learning method using multilayer perceptrons has been developed to correct these shifts. However, a more robust network such as convolutional neural networks (CNN) can be considered as this approach more accurately obtains spatial information and extract key features of the given data. In this study, we aim to investigate the feasibility and utility of CNNs for FPC of single voxel MEGA-PRESS MRS simulated and in vivo data.

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