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arXiv Machine Learning By Julian P. Merkofer, Dennis M. J. van de Sande, Alex A. Bhogal, Ruud J. G. van Sloun

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

Read the original on arXiv Machine Learning →

The paper presents a Bayesian inference framework for magnetic resonance spectroscopy (MRS) that employs Sylvester normalizing flows (SNFs) to approximate posterior distributions over metabolite concentrations. A physics-based decoder incorporates prior knowledge of MRS signal formation, ensuring realistic distribution representations. Validation on simulated 7T proton MRS data shows accurate metabolite quantification, well-calibrated uncertainties, and insights into parameter correlations and multi‑modal distributions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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