QQ‐NET – using deep learning to solve quantitative susceptibility mapping and quantitative blood oxygen level dependent magnitude (QSM+qBOLD or QQ) based oxygen extraction fraction (OEF) mapping

Purpose To improve accuracy and speed of quantitative susceptibility mapping plus quantitative blood oxygen level‐dependent magnitude (QSM+qBOLD or QQ) ‐based oxygen extraction fraction (OEF) mapping using a deep neural network (QQ‐NET). Methods The 3D multi‐echo gradient echo images were acquired in 34 ischemic stroke patients and 4 healthy subjects. Arterial spin labeling and diffusion weighted imaging (DWI) were also performed in the patients. NET was developed to solve the QQ model inversion problem based on Unet. QQ‐based OEF maps were reconstructed with previously introduced temporal clustering, tissue composition, and total variation (CCTV) and NET. The results were compared in simulation, ischemic stroke patients, and healthy subjects using a two‐sample Kolmogorov‐Smirnov test. Results In the simulation, QQ‐NET provided more accurate and precise OEF maps than QQ‐CCTV with 150 times faster reconstruction speed. In the subacute stroke patients, OEF from QQ‐NET had greater contrast‐to‐noise ratio (CNR) between DWI‐defined lesions and their unaffected contralateral normal tissue than with QQ‐CCTV: 1.9 ± 1.3 vs 6.6 ± 10.7 ( p = 0.03). In healthy subjects, both QQ‐CCTV and QQ‐NET provided uniform OEF maps. Conclusion QQ‐NET improves the accuracy of QQ‐based OEF with faster reconstruction.