Abstract #0724

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

Elisa Saks^1,2, Stephan Kaczmarz^1,2,3, Nicholas Blockley⁴, Claus Zimmer¹, Christine Preibisch^1,2,5, and Gabriel Hoffmann^1,2

¹Institute for Neuroradiology, School of Medicine and Health, TUM University Hospital, Technical University of Munich, Munich, Germany, ²TUM-Neuroimaging Center, TUM University Hospital, Technical University of Munich, Munich, Germany, ³Philips GmbH Market DACH, Hamburg, Germany, ⁴School of Life Science, University of Nottingham, Nottingham, United Kingdom, ⁵Clinic for Neurology, School of Medicine and Health, TUM University Hospital, Technical University of Munich, Munich, Germany

Synopsis

Keywords: Oxygenation, Brain, cerebral blood volume, hyperoxia, quantitative BOLD, oxygen extraction fraction

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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