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. Issue 3 (31st October 2021)

• Record Type: Journal Article
• Authors: Cho, Junghun; Zhang, Jinwei; Spincemaille, Pascal; Zhang, Hang; Hubertus, Simon; Wen, Yan; Jafari, Ramin; Zhang, Shun; Nguyen, Thanh D.; Dimov, Alexey V.; Gupta, Ajay; Wang, Yi
• Is Part Of: Magnetic resonance in medicine. Volume 87:Issue 3(2022)
• Page Start: 1583
• Page End: 1594
• Publication Date: 2021-10-31
• Subjects: cerebral metabolic rate of oxygen -- deep learning -- DL -- oxygen extraction fraction -- qBOLD -- QQ -- QQ‐NET -- QSM -- QSM+qBOLD -- quantitative blood oxygenation level‐dependent imaging -- quantitative susceptibility mapping -- Unet; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.29057
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : 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.