Abstract #4702

Towards Prediction of Motion Affected Spectra for MRSI at 7T

Stanislav Motyka^1,2, Eva Niess¹, Bernhard Strasser¹, Amir Shamaei³, Lukas Hingerl¹, Paul Weiser^4,5,6, Fabian Niess¹, and Wolfgang Bogner^2,7

¹High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria, ²Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria, ³University of Calgary, Calgary, AB, Canada, ⁴Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States, ⁵Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States, ⁶Computational Imaging Research Lab - Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria, ⁷Medical University of Vienna, Vienna, Austria

Synopsis

Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence, Motion, MRSI, Brain, Quality assurance

Motivation: Quality assessment of whole-brain MRSI spectra is usually based on post-quantification analyses, which does not reflect if the estimated metabolite concentration is true.

Goal(s): Simulate effects of subject motion for the raw non-Cartesian MRSI kSpace data. Build a dataset of motion-corrupted MRSI data with a corresponding ground truth version. Train a classifier to assess the quality of MRSI data.

Approach: Translations and rotations were simulated in the kSpace domain. A classifier is trained in a supervised fashion with the thresholded deviation between the motion-affected and original data as the target.

Results: The classifier outperforms the CRLBs in the quality assessment of MRSI data.

Impact: Simulation of subject motion effects on raw non-Cartesian kSpace MRSI data allows us to assess the quality of MRSI spectra and can lead us toward the understanding of lipid artifacts, which is the main limiting factor of MRSI.

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