Free-Breathing Hepatic Oxygen Extraction Fraction (OEF) Mapping Using Radial GESSE

Noninvasive assessment of hepatic oxygenation is relevant for evaluating liver both in acute and long-term liver pathologies. However, existing methods are limited to single-slice acquisitions and require breath-holding. This study aims to develop and evaluate a motion-robust technique for whole-liver oxygen extraction fraction (OEF) mapping using a radial gradient-echo sampling of spin-echo (rGESSE) sequence in combination with an artificial neural network (ANN) for quantification.

Seven healthy volunteers were scanned using a 1.5 T MRI system with an 18-channel body coil. A multi-slice rGESSE sequence with radial sampling was employed under free-breathing conditions to acquire volumetric liver data. Signal processing included bias-field correction and 3D median filtering. OEF, deoxygenated blood volume (DBV), and transverse relaxation rate (R₂) were estimated using a trained feedforward ANN based on simulated qBOLD signal models.

Whole-liver OEF maps were successfully obtained in all volunteers under free-breathing. Representative parameter maps showed consistent spatial patterns and anatomical correspondence. The mean hepatic OEF across subjects was 55.75% ± 6.88%, and the mean DBV was 0.568 ± 0.039. Comparison with literature values suggested a systematic overestimation, likely arising from a combination of model approximations, residual B₀ inhomogeneity, motion effects, and acquisition-specific sensitivities. ANN-based fitting outperformed standard least-squares regression in terms of stability and artifact suppression.

This study demonstrates the feasibility of using rGESSE combined with ANN analysis for free-breathing, whole-liver OEF mapping. The proposed approach allows for noninvasive, volumetric hepatic oxygenation assessment with improved motion robustness, offering potential for clinical application in liver disease diagnosis and monitoring.