What type of study is this?

This is a Quantitative Study study.

September 17, 2025

Deep Learning-Based MR Vascular Fingerprinting for Dynamic Quantification of Cerebral Hemodynamics During Gas Challenges

Key Points

We show increased T2 levels in gray matter during hyperoxia, affirming gas-induced effects.
Cerebral blood volume and vessel radius were elevated in both gray and white matter during hypoxia.
Dynamic quantification reveals important insights into cerebral oxygenation changes under controlled gas conditions.
Methodology utilized a GESFIDE-based model trained on synthetic data for parameter estimation accuracy.

Abstract

Motivation: Dynamic contrast enhancement MRI and fMRI provide perfusion and oxygenation information but cannot quantify multiparametric estimations. Magnetic Resonance Vascular Fingerprinting (MRvF) decodes multiple physiological parameters in one acquisition. Deep learning enhances MRvF for accurate estimations. Goal(s): Using deep learning-based MRvF, we quantify cerebral oxygen saturation, blood volume (CBV), vessel radius (R), and T2 across normoxia, hyperoxia, and hypoxia. Approach: We train GESFIDE-based (gradient-echo sampling of free induction decay and echo) model on synthetic data to estimate parameters during gas challenge. Results: We show increased T2 in GM during hyperoxia and elevated CBV and R in GM and WM during hypoxia, aligning with literature. Impact: We demonstrate that deep learning-enhanced MRvF offers improved accuracy in quantifying cerebral oxygenation dynamics compared to dictionary matching. By testing differences during controlled gas challenges, we show expected range and dynamics of vascular parameter quantifications for understanding underlying brain health.

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