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. 2020 Jul 14;296(3):627–637. doi: 10.1148/radiol.2020192793

Figure 5:

Representative images in three patients with severe Moyamoya disease with impaired relative cerebral blood flow (CBF) change (rΔCBF) measured with PET. Upper row: Case 1 shows a 43-year-old woman with moderate right M1 stenosis (arrow). Baseline CBF is preserved, while arterial transit time (ATT) is prolonged (*), in right cerebral hemisphere. Both PET-plus-MRI and MRI-only models correctly predict impaired rΔCBF in right cerebral hemisphere, while rΔCBF measured with multidelay (MD) arterial spin labeling (ASL) (ASL rΔCBF) mistakenly shows lower rΔCBF in left side. Middle row: Case 2 shows 18-year-old woman with occluded right distal internal carotid artery (double arrows). In addition to prolonged baseline ATT (*), reduced baseline PET-CBF, ASL CBF, and mean ASL difference signal (mASL) (open arrowheads) are noted in right cerebral hemisphere, as well as a chronic infarct on T2 fluid-attenuated inversion-recovery (FLAIR) (open arrow). Both deep learning models correctly predicted more severely impaired rΔCBF than in case 1. Lower row: Case 3 shows a 33-year-old woman with bilateral M1 occlusion (double arrows). Both models successfully predicted impaired rΔCBF from inputs of prolonged baseline ATT (*) and mildly reduced MD-mASL bilaterally (open arrowheads), PD = proton density–weighted image, PET-rΔCBF = relative CBF change measured with PET, SD = single-delay, TOF-MRA = time-of-flight MR angiography.

Representative images in three patients with severe Moyamoya disease with impaired relative cerebral blood flow (CBF) change (rΔCBF) measured with PET. Upper row: Case 1 shows a 43-year-old woman with moderate right M1 stenosis (arrow). Baseline CBF is preserved, while arterial transit time (ATT) is prolonged (*), in right cerebral hemisphere. Both PET-plus-MRI and MRI-only models correctly predict impaired rΔCBF in right cerebral hemisphere, while rΔCBF measured with multidelay (MD) arterial spin labeling (ASL) (ASL rΔCBF) mistakenly shows lower rΔCBF in left side. Middle row: Case 2 shows 18-year-old woman with occluded right distal internal carotid artery (double arrows). In addition to prolonged baseline ATT (*), reduced baseline PET-CBF, ASL CBF, and mean ASL difference signal (mASL) (open arrowheads) are noted in right cerebral hemisphere, as well as a chronic infarct on T2 fluid-attenuated inversion-recovery (FLAIR) (open arrow). Both deep learning models correctly predicted more severely impaired rΔCBF than in case 1. Lower row: Case 3 shows a 33-year-old woman with bilateral M1 occlusion (double arrows). Both models successfully predicted impaired rΔCBF from inputs of prolonged baseline ATT (*) and mildly reduced MD-mASL bilaterally (open arrowheads), PD = proton density–weighted image, PET-rΔCBF = relative CBF change measured with PET, SD = single-delay, TOF-MRA = time-of-flight MR angiography.