Physiologically Informed Bayesian Analysis of ASL fMRI Data

TL;DR

This paper introduces a physiologically informed Bayesian model for ASL fMRI data that jointly estimates hemodynamic and perfusion components, improving perfusion response estimation under low SNR conditions.

Contribution

It presents a novel Bayesian framework incorporating physiological links between hemodynamic and perfusion signals for better ASL data analysis.

Findings

Enhanced accuracy in perfusion response estimation.

Effective separation of hemodynamic and perfusion components.

Improved analysis of low SNR ASL data.

Abstract

Arterial Spin Labelling (ASL) functional Magnetic Resonance Imaging (fMRI) data provides a quantitative measure of blood perfusion, that can be correlated to neuronal activation. In contrast to BOLD measure, it is a direct measure of cerebral blood flow. However, ASL data has a lower SNR and resolution so that the recovery of the perfusion response of interest suffers from the contamination by a stronger hemodynamic component in the ASL signal. In this work we consider a model of both hemodynamic and perfusion components within the ASL signal. A physiological link between these two components is analyzed and used for a more accurate estimation of the perfusion response function in particular in the usual ASL low SNR conditions.