Author

Dennis M J van de Sande

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#machine learning May 2025

Physics-Informed Sylvester Normalizing Flows for Bayesian Inference in Magnetic Resonance Spectroscopy

A Bayesian inference framework using Sylvester normalizing flows (SNFs) to approximate posterior distributions over metabolite concentrations, enhancing quantification reliability, and a physics-based decoder incorporates prior knowledge of MRS signal formation, ensuring realistic distribution representations.

Julian P. Merkofer, D. V. D. van de Sande, A. Bhogal et al. · arXiv.org · 0 citations

#software testing Preprint Sep 2026

The 2024 MRSI Data Processing and Quantification Challenge Synthetic Dataset

This synthetic benchmark supports development and comparison of MRSI processing, nuisance-signal removal, and metabolite-quantification methods, while enabling method evaluation, and illustrates how characterized synthetic data can support rigorous evaluation when ground truth is difficult or impossible to obtain.

John LaMaster, J. Merkofer, Dennis M J van de Sande et al. · 0 citations