Magnetic Resonance Spectroscopy Frequency and Phase Correction Using Convolutional Neural Networks
David Ma1, Hortense Le1, Yuming Ye1, Andrew Laine1, Jeffrey Lieberman2, Douglas Rothman3, Scott Small2,4,5, and Jia Guo2,6
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
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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