AI-Integrated MRS Scan Identifies and Updates Scan Parameters in the Presence of OOV Artifacts
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.
Authors
- Kathleen E. Hupfeld (ORCID: https://orcid.org/0000-0001-5086-4841)
- Helge J. Zöllner (ORCID: https://orcid.org/0000-0002-7148-292X)
- Aaron T. Gudmundson (ORCID: https://orcid.org/0000-0001-5104-0959)
- Georg Oeltzschner (ORCID: https://orcid.org/0000-0003-3083-9811)
- Richard A.E. Edden (ORCID: https://orcid.org/0000-0002-0671-7374)
- Michael Schär (ORCID: https://orcid.org/0000-0002-7044-9941)
- Sandeep Ganji (ORCID: https://orcid.org/0000-0002-0247-7596)
- Yulu Song (ORCID: https://orcid.org/0000-0002-4416-7959)
- Christopher W. Davies‐Jenkins (ORCID: https://orcid.org/0000-0002-6015-762X)
- İpek Özdemir (ORCID: https://orcid.org/0000-0001-6807-9390)
- Gizeaddis Simegn
Publication Details
- Journal
- Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
- Published
- 2024-11-26
- DOI
- https://doi.org/10.58530/2024/4659
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00