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Cluster analysis of time evolution (CAT) for quantitative susceptibility mapping (QSM) and quantitative blood oxygen level‐dependent magnitude (qBOLD)‐based oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO 2 ) mapping
Author(s) -
Cho Junghun,
Zhang Shun,
Kee Youngwook,
Spincemaille Pascal,
Nguyen Thanh D.,
Hubertus Simon,
Gupta Ajay,
Wang Yi
Publication year - 2020
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.27967
Subject(s) - quantitative susceptibility mapping , oxygen , blood oxygen level dependent , oxygen metabolism , nuclear magnetic resonance , magnetic resonance imaging , nuclear medicine , chemistry , physics , medicine , radiology , organic chemistry
Purpose To improve the accuracy of QSM plus quantitative blood oxygen level‐dependent magnitude (QSM + qBOLD or QQ)‐based mapping of the oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO 2 ) using cluster analysis of time evolution (CAT). Methods 3D multi‐echo gradient echo and arterial spin labeling images were acquired in 11 healthy subjects and 5 ischemic stroke patients. DWI was also carried out on patients. CAT was developed for analyzing signal evolution over TE. QQ‐based OEF and CMRO 2 were reconstructed with and without CAT, and results were compared using region of interest analysis and a paired t‐test. Results Simulations demonstrated that CAT substantially reduced noise error in QQ‐based OEF. In healthy subjects, QQ‐based OEF appeared less noisy and more uniform with CAT than without CAT; average OEF with and without CAT in cortical gray matter was 32.7 ± 4.0% and 37.9 ± 4.5%, with corresponding CMRO 2 of 148.4 ± 23.8 and 171.4 ± 22.4 μmol/100 g/min, respectively. In patients, regions of low OEF were confined within the ischemic lesions defined on DWI when using CAT, which was not observed without CAT. Conclusion The cluster analysis of time evolution (CAT) significantly improves the robustness of QQ‐based OEF against noise.