Temporal clustering, tissue composition, and total variation for mapping oxygen extraction fraction using QSM and quantitative BOLD. Issue 5 (10th June 2021)

• Record Type: Journal Article
• Authors: Cho, Junghun; Spincemaille, Pascal; Nguyen, Thanh D.; Gupta, Ajay; Wang, Yi
• Is Part Of: Magnetic resonance in medicine. Volume 86:Issue 5(2021)
• Page Start: 2635
• Page End: 2646
• Publication Date: 2021-06-10
• Subjects: oxygen extraction fraction -- quantitative blood oxygenation level‐dependent imaging -- quantitative susceptibility mapping -- temporal clustering -- tissue composition -- total variation; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.28875
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : Purpose: To improve the accuracy of quantitative susceptibility mapping plus quantitative blood oxygen level‐dependent magnitude (QSM+qBOLD or QQ) based mapping of oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2 ) using temporal clustering, tissue composition, and total variation (CCTV). Methods: Three‐dimensional multi‐echo gradient echo and arterial spin labeling images were acquired from 11 healthy subjects and 33 ischemic stroke patients. Diffusion‐weighted imaging (DWI) was also obtained from patients. The CCTV mapping was developed for incorporating tissue‐type information into clustering of the previous cluster analysis of time evolution (CAT) and applying total variation (TV). The QQ‐based OEF and CMRO2 were reconstructed with CAT, CAT+TV (CATV), and the proposed CCTV, and results were compared using region‐of‐interest analysis, Kruskal‐Wallis test, and post hoc Wilcoxson rank sum test. Results: In simulation, CCTV provided more accurate and precise OEF than CAT or CATV. In healthy subjects, QQ‐based OEF was less noisy and more uniform with CCTV than CAT. In subacute stroke patients, OEF with CCTV had a greater contrast‐to‐noise ratio between DWI‐defined lesions and the unaffected contralateral side than with CAT or CATV: 1.9 ± 1.3 versus 1.1 ± 0.7 ( P = .01) versus 0.7 ± 0.5 ( P < .001). Conclusion: The CCTV mapping significantly improves the robustness of QQ‐based OEF against noise.