Keywords: Acquisition Methods, Machine Learning/Artificial Intelligence, Spectroscopy, Brain, Artifacts, Convolutional Neural Network
Motivation: Deep learning is a promising new tool for post-processing MRS data. Neural network-based MRS acquisition methods do not yet exist, but should lead to higher-quality data.
Goal(s): The goal of this work was to create an “intelligent MRS scan” by integrating a Convolutional Neural Network (CNN) directly into a MRS acquisition protocol.
Approach: Here, a CNN-powered pre-scan collects a single-transient from 48 different gradient geometries, and updates future scans, without human intervention, to avoid out-of-voxel (OOV) artifacts.
Results: The AI-informed scan produced high-quality data for all participants while the control parameters failed half of the time in the artifact-rich mPFC region.
Impact: The work demonstrates the first AI-integrated MRS scan protocol in which an intelligent pre-scan modifies scan parameters to improve data quality, here reducing out-of-voxel artifacts.
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