Traditional hemodynamic imaging approaches such as arterial spin labeling (ASL) and hypercapnic blood oxygenation level-dependent (BOLD) reactivity provide contrasts that are frequently difficult to interpret using conventional analyses in arterial steno-occlusive disease patients with extreme blood arrival and vascular reactivity delay times. We investigated applying a supervised learning procedure to exploit endovascular and vascular compliance artifacts as potential indicators of disease severity; results show that less-conventional variables which report on endovascular blood signal and delayed vascular compliance outperform conventional variables, such as mean ASL signal and BOLD signal change.
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