Free‐Breathing Hepatic Oxygen Extraction Fraction (
OEF
) Mapping Using Radial
GESSE
Ke Zhang, Simon M. F. Triphan, Felix T. Kurz, Congcong Fu, Na Zhang, Christian H. Ziener, Mark E. Ladd, Heinz‐Peter Schlemmer, Hans‐Ulrich Kauczor, Oliver Sedlaczek ABSTRACT
Purpose
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.
Methods
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 ( R 2 ) were estimated using a trained feedforward ANN based on simulated qBOLD signal models.
Results
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 B 0 inhomogeneity, motion effects, and acquisition‐specific sensitivities. ANN‐based fitting outperformed standard least‐squares regression in terms of stability and artifact suppression.
Conclusion
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.