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Abstract #4833

Preliminary Results from the 2024 MRSI Data Processing and Quantification Challenge

Brian J Soher1, John T LaMaster2, Julian P Merkofer3, Bernhard Strasser4, Dennis van de Sande3, and Chao Ma5
1Center for Advanced MR Development, Department of Radiology, Duke University Medical Center, Durham, NC, United States, 2Munich Institute of Biomedical Engineering, Technical University of Munich, Munich, Germany, 3Eindhoven University of Technology, Eindhoven, Netherlands, 4High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria, 5School of Medicine, Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States

Synopsis

Keywords: Data Processing, Analysis/Processing, Brain, Simulation/Validation, Data Analysis, Image Reconstruction, Neuro, Software Tools, Spectroscopy

Motivation: Realistic simulated training/testing MRSI data based on in vivo data are necessary for development of nuisance signal removal and spectral analysis methods.

Goal(s): To test data processing/analysis pipelines' ability to create accurate metabolite maps from simulated FID-MRSI datasets corrupted by noise/nuisance signals.

Approach: Challenge: 1) Nuisance signal removal and spectral quantification on data contaminated by noise, baseline signals, spectral distortions, and residual water/lipid, and 2) Spectral quantification on data contaminated by noise, baseline signals, and spectral distortions.

Results: Nine teams signed up, three submitted: each challenge received two submissions. Variability in results were seen. Data and code are available freely to the community.

Impact: This is a resource, both code and data, that the MRS community can use to create repeatable simulated data based on real world data inputs. It is a platform that encourages collaboration to simplify creation of reusable infrastructure.

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Keywords