Identifying Hypoperfusion in Moyamoya Disease With Arterial Spin Labeling and an [15O]-Water Positron Emission Tomography/Magnetic Resonance Imaging Normative Database.

Background and Purpose- Noninvasive imaging of brain perfusion has the potential to elucidate pathophysiological mechanisms underlying Moyamoya disease and enable clinical imaging of cerebral blood flow (CBF) to select revascularization therapies for patients. We used hybrid positron emission tomography (PET)/magnetic resonance imaging (MRI) technology to characterize the distribution of hypoperfusion in Moyamoya disease and its relationship to vessel stenosis severity, through comparisons with a normative perfusion database of healthy controls. Methods- To image CBF, we acquired [15O]-water PET as a reference and simultaneously acquired arterial spin labeling (ASL) MRI scans in 20 Moyamoya patients and 15 age-matched, healthy controls on a PET/MRI scanner. The ASL MRI scans included a standard single-delay ASL scan with postlabel delay of 2.0 s and a multidelay scan with 5 postlabel delays (0.7-3.0s) to estimate and account for arterial transit time in CBF quantification. The percent volume of hypoperfusion in patients (determined as the fifth percentile of CBF values in the healthy control database) was the outcome measure in a logistic regression model that included stenosis grade and location. Results- Logistic regression showed that anterior ( P<0.0001) and middle cerebral artery territory regions ( P=0.003) in Moyamoya patients were susceptible to hypoperfusion, whereas posterior regions were not. Cortical regions supplied by arteries with stenosis on MR angiography showed more hypoperfusion than normal arteries ( P=0.001), but the extent of hypoperfusion was not different between mild-moderate versus severe stenosis. Multidelay ASL did not perform differently from [15O]-water PET in detecting perfusion abnormalities, but standard ASL overestimated the extent of hypoperfusion in patients ( P=0.003). Conclusions- This simultaneous PET/MRI study supports the use of multidelay ASL MRI in clinical evaluation of Moyamoya disease in settings where nuclear medicine imaging is not available and application of a normative perfusion database to automatically identify abnormal CBF in patients.

Long-Delay Arterial Spin Labeling Provides More Accurate Cerebral Blood Flow Measurements in Moyamoya Patients: A Simultaneous Positron Emission Tomography/MRI Study.

BACKGROUND AND PURPOSE: Arterial spin labeling (ASL) MRI is a promising, noninvasive technique to image cerebral blood flow (CBF) but is difficult to use in cerebrovascular patients with abnormal, long arterial transit times through collateral pathways. To be clinically adopted, ASL must first be optimized and validated against a reference standard in these challenging patient cases. METHODS: We compared standard-delay ASL (post-label delay=2.025 seconds), multidelay ASL (post-label delay=0.7-3.0 seconds), and long-label long-delay ASL acquisitions (post-label delay=4.0 seconds) against simultaneous [15O]-positron emission tomography (PET) CBF maps in 15 Moyamoya patients on a hybrid PET/MRI scanner. Dynamic susceptibility contrast was performed in each patient to identify areas of mild, moderate, and severe time-to-maximum (Tmax) delays. Relative CBF measurements by each ASL scan in 20 cortical regions were compared with the PET reference standard, and correlations were calculated for areas with moderate and severe Tmax delays. RESULTS: Standard-delay ASL underestimated relative CBF by 20% in areas of severe Tmax delays, particularly in anterior and middle territories commonly affected by Moyamoya disease (P<0.001). Arterial transit times correction by multidelay acquisitions led to improved consistency with PET, but still underestimated CBF in the presence of long transit delays (P=0.02). Long-label long-delay ASL scans showed the strongest correlation relative to PET, and there was no difference in mean relative CBF between the modalities, even in areas of severe delays. CONCLUSIONS: Post-label delay times of ≥4 seconds are needed and may be combined with multidelay strategies for robust ASL assessment of CBF in Moyamoya disease.

Predicting 15O-Water PET cerebral blood flow maps from multi-contrast MRI using a deep convolutional neural network with evaluation of training cohort bias.

To improve the quality of MRI-based cerebral blood flow (CBF) measurements, a deep convolutional neural network (dCNN) was trained to combine single- and multi-delay arterial spin labeling (ASL) and structural images to predict gold-standard 15O-water PET CBF images obtained on a simultaneous PET/MRI scanner. The dCNN was trained and tested on 64 scans in 16 healthy controls (HC) and 16 cerebrovascular disease patients (PT) with 4-fold cross-validation. Fidelity to the PET CBF images and the effects of bias due to training on different cohorts were examined. The dCNN significantly improved CBF image quality compared with ASL alone (mean ± standard deviation): structural similarity index (0.854 ± 0.036 vs. 0.743 ± 0.045 [single-delay] and 0.732 ± 0.041 [multi-delay], P < 0.0001); normalized root mean squared error (0.209 ± 0.039 vs. 0.326 ± 0.050 [single-delay] and 0.344 ± 0.055 [multi-delay], P < 0.0001). The dCNN also yielded mean CBF with reduced estimation error in both HC and PT (P < 0.001), and demonstrated better correlation with PET. The dCNN trained with the mixed HC and PT cohort performed the best. The results also suggested that models should be trained on cases representative of the target population.