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This is a Experimental Study study.

September 17, 2025

Fast and Non-Invasive Oxygen Extraction Fraction Mapping Using Hyperoxic BOLD-fMRI and Deep-Learning-Accelerated Relaxometry

Key Points

Hyperoxic BOLD-fMRI allows for new methods of quantifying cerebral blood volume non-invasively, and deep learning enhances R2ʹ measurements.
Reduction in R2ʹ acquisition times by around 82% suggests significant improvements in efficiency when using deep-learning techniques.
The research compares hyperoxic BOLD-fMRI models and examines the efficacy of deep learning for accelerated relaxometry measurement methods.
Continued exploration of systematic biases in oxygen extraction fraction maps underscores the need for deeper investigations to validate findings.

Abstract

Motivation: Oxygen extraction fraction (OEF) mapping by mqBOLD MRI requires contrast-agent-based DSC MRI to quantify cerebral blood volume (CBV) and suffers from overall long acquisition times. Goal(s): To investigate the utility of CBV estimates from non-invasive, hyperoxic BOLD-fMRI together with deep-learning-(DL-)accelerated R2ʹ measurements for OEF mapping. Approach: Compare CBV from hyperoxic BOLD-fMRI and two different models (Bulte vs. Blockley), R2ʹ from DL-accelerated vs. non-accelerated acquisition, and the respective combinations of both parameters for OEF mapping. Results: Hyperoxic BOLD-fMRI enables non-invasive CBV quantification, and DL-acceleration reduces R2ʹ acquisition times by ~82%. Systematic bias in resulting OEF maps needs further investigation. Impact: We investigated CBV estimation from hyperoxic BOLD-fMRI with two quantification models as a non-invasive alternative to contrast-agent-based DSC MRI, and the reduction of R2ʹ acquisition time by DL-based acceleration. Ultimately, this parameter combination enables fast and non-invasive OEF mapping.

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