Use of Deep Learning to Predict Final Ischemic Stroke Lesions From Initial Magnetic Resonance Imaging.

Importance: Predicting infarct size and location is important for decision-making and prognosis in patients with acute stroke. Objectives: To determine whether a deep learning model can predict final infarct lesions using magnetic resonance images (MRIs) acquired at initial presentation (baseline) and to compare the model with current clinical prediction methods. Design, Setting, and Participants: In this multicenter prognostic study, a specific type of neural network for image segmentation (U-net) was trained, validated, and tested using patients from the Imaging Collaterals in Acute Stroke (iCAS) study from April 14, 2014, to April 15, 2018, and the Diffusion Weighted Imaging Evaluation for Understanding Stroke Evolution Study-2 (DEFUSE-2) study from July 14, 2008, to September 17, 2011 (reported in October 2012). Patients underwent baseline perfusion-weighted and diffusion-weighted imaging and MRI at 3 to 7 days after baseline. Patients were grouped into unknown, minimal, partial, and major reperfusion status based on 24-hour imaging results. Baseline images acquired at presentation were inputs, and the final true infarct lesion at 3 to 7 days was considered the ground truth for the model. The model calculated the probability of infarction for every voxel, which can be thresholded to produce a prediction. Data were analyzed from July 1, 2018, to March 7, 2019. Main Outcomes and Measures: Area under the curve, Dice score coefficient (DSC) (a metric from 0-1 indicating the extent of overlap between the prediction and the ground truth; a DSC of ≥0.5 represents significant overlap), and volume error. Current clinical methods were compared with model performance in subgroups of patients with minimal or major reperfusion. Results: Among the 182 patients included in the model (97 women [53.3%]; mean [SD] age, 65 [16] years), the deep learning model achieved a median area under the curve of 0.92 (interquartile range [IQR], 0.87-0.96), DSC of 0.53 (IQR, 0.31-0.68), and volume error of 9 (IQR, -14 to 29) mL. In subgroups with minimal (DSC, 0.58 [IQR, 0.31-0.67] vs 0.55 [IQR, 0.40-0.65]; P = .37) or major (DSC, 0.48 [IQR, 0.29-0.65] vs 0.45 [IQR, 0.15-0.54]; P = .002) reperfusion for which comparison with existing clinical methods was possible, the deep learning model had comparable or better performance. Conclusions and Relevance: The deep learning model appears to have successfully predicted infarct lesions from baseline imaging without reperfusion information and achieved comparable performance to existing clinical methods. Predicting the subacute infarct lesion may help clinicians prepare for decompression treatment and aid in patient selection for neuroprotective clinical trials.

Simultaneous phase-contrast MRI and PET for noninvasive quantification of cerebral blood flow and reactivity in healthy subjects and patients with cerebrovascular disease.

BACKGROUND: H215 O-positron emission tomography (PET) is considered the reference standard for absolute cerebral blood flow (CBF). However, this technique requires an arterial input function measured through continuous sampling of arterial blood, which is invasive and has limitations with tracer delay and dispersion. PURPOSE: To demonstrate a new noninvasive method to quantify absolute CBF with a PET/MRI hybrid scanner. This blood-free approach, called PC-PET, takes the spatial CBF distribution from a static H215 O-PET scan, and scales it to the whole-brain average CBF value measured by simultaneous phase-contrast MRI. STUDY TYPE: Observational. SUBJECTS: Twelve healthy controls (HC) and 13 patients with Moyamoya disease (MM) as a model of chronic ischemic disease. FIELD STRENGTH/SEQUENCES: 3T/2D cardiac-gated phase-contrast MRI and H215 O-PET. ASSESSMENT: PC-PET CBF values from whole brain (WB), gray matter (GM), and white matter (WM) in HCs were compared with literature values since 2000. CBF and cerebrovascular reactivity (CVR), which is defined as the percent CBF change between baseline and post-acetazolamide (vasodilator) scans, were measured by PC-PET in MM patients and HCs within cortical regions corresponding to major vascular territories. Statistical Tests: Linear, mixed effects models were created to compare CBF and CVR, respectively, between patients and controls, and between different degrees of stenosis. RESULTS: The mean CBF values in WB, GM, and WM in HC were 42 ± 7 ml/100 g/min, 50 ± 7 ml/100 g/min, and 23 ± 3 ml/100 g/min, respectively, which agree well with literature values. Compared with normal regions (57 ± 23%), patients showed significantly decreased CVR in areas with mild/moderate stenosis (47 ± 17%, P = 0.011) and in severe/occluded areas (40 ± 16%, P = 0.016). Data Conclusion: PC-PET identifies differences in cerebrovascular reactivity between healthy controls and cerebrovascular patients. PC-PET is suitable for CBF measurement when arterial blood sampling is not accessible, and warrants comparison to fully quantitative H215 O-PET in future studies. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;51:183-194.

Contralateral Hemispheric Cerebral Blood Flow Measured With Arterial Spin Labeling Can Predict Outcome in Acute Stroke.

Background and Purpose- Imaging is frequently used to select acute stroke patients for intra-arterial therapy. Quantitative cerebral blood flow can be measured noninvasively with arterial spin labeling magnetic resonance imaging. Cerebral blood flow levels in the contralateral (unaffected) hemisphere may affect capacity for collateral flow and patient outcome. The goal of this study was to determine whether higher contralateral cerebral blood flow (cCBF) in acute stroke identifies patients with better 90-day functional outcome. Methods- Patients were part of the prospective, multicenter iCAS study (Imaging Collaterals in Acute Stroke) between 2013 and 2017. Consecutive patients were enrolled after being diagnosed with anterior circulation acute ischemic stroke. Inclusion criteria were ischemic anterior circulation stroke, baseline National Institutes of Health Stroke Scale score ≥1, prestroke modified Rankin Scale score ≤2, onset-to-imaging time <24 hours, with imaging including diffusion-weighted imaging and arterial spin labeling. Patients were dichotomized into high and low cCBF groups based on median cCBF. Outcomes were assessed by day-1 and day-5 National Institutes of Health Stroke Scale; and day-30 and day-90 modified Rankin Scale. Multivariable logistic regression was used to test whether cCBF predicted good neurological outcome (modified Rankin Scale score, 0-2) at 90 days. Results- Seventy-seven patients (41 women) met the inclusion criteria with median (interquartile range) age of 66 (55-76) yrs, onset-to-imaging time of 4.8 (3.6-7.7) hours, and baseline National Institutes of Health Stroke Scale score of 13 (9-20). Median cCBF was 38.9 (31.2-44.5) mL per 100 g/min. Higher cCBF predicted good outcome at day 90 (odds ratio, 4.6 [95% CI, 1.4-14.7]; P=0.01), after controlling for baseline National Institutes of Health Stroke Scale, diffusion-weighted imaging lesion volume, and intra-arterial therapy. Conclusions- Higher quantitative cCBF at baseline is a significant predictor of good neurological outcome at day 90. cCBF levels may inform decisions regarding stroke triage, treatment of acute stroke, and general outcome prognosis. Clinical Trial Registration- URL: https://www.clinicaltrials.gov. Unique identifier: NCT02225730.

Diagnostic and prognostic benefit of arterial spin labeling in subacute stroke.

BACKGROUND AND PURPOSE: Brain perfusion measurement in the subacute phase of stroke may support therapeutic decisions. We evaluated whether arterial spin labeling (ASL), a noninvasive perfusion imaging technique based on magnetic resonance imaging (MRI), adds diagnostic and prognostic benefit to diffusion-weighted imaging (DWI) in subacute stroke. METHODS: In a single-center imaging study, patients with DWI lesion(s) in the middle cerebral artery (MCA) territory were included. Onset to imaging time was ≤7 days and imaging included ASL and DWI sequences. Qualitative (standardized visual analysis) and quantitative perfusion analyses (region of interest analysis) were performed. Dichotomized early outcome (modified Rankin Scale [mRS] 0-2 vs. 3-6) was analyzed in two logistic regression models. Model 1 included DWI lesion volume, age, vascular pathology, admission NIHSS, and acute stroke treatment as covariates. Model 2 added the ASL-based perfusion pattern to Model 1. Receiver-operating-characteristic (ROC) and area-under-the-curve (AUC) were calculated for both models to assess their predictive power. The likelihood-ratio-test compared both models. RESULTS: Thirty-eight patients were included (median age 70 years, admission NIHSS 4, onset to imaging time 67 hr, discharge mRS 2). Qualitative perfusion analysis yielded additional diagnostic information in 84% of the patients. In the quantitative analysis, AUC for outcome prediction was 0.88 (95% CI 0.77-0.99) for Model 1 and 0.97 (95% CI 0.91-1.00) for Model 2. Inclusion of perfusion data significantly improved performance and outcome prediction (p = 0.002) of stroke imaging. CONCLUSIONS: In patients with subacute stroke, our study showed that adding perfusion imaging to structural imaging and clinical data significantly improved outcome prediction. This highlights the usefulness of ASL and noninvasive perfusion biomarkers in stroke diagnosis and management.

Correction for Susceptibility Distortions Increases the Performance of Arterial Spin Labeling in Patients with Cerebrovascular Disease.

BACKGROUND AND PURPOSE: Arterial spin labeling (ASL) is an MRI technique to measure cerebral blood flow (CBF) without the need of exogenous contrast agents and is thus a promising alternative to the clinical standard dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion imaging. Latest international guidelines encourage its application in the clinical setting. However, susceptibility-induced image distortions impair ASL with fast readout modules (eg Echo Planar Imaging, EPI; gradient and spin echo, GRASE). In the present study, we investigated the benefit of a distortion correction for ASL compared to DSC. METHODS: A pulsed ASL (PASL) sequence combined with a 3D-GRASE readout at multiple inflow times (multi-TI) was used and was corrected for susceptibility distortions using a FMRIB Software Library (FSL) implemented tool TOPUP. We performed qualitative (three expert raters) and quantitative (volume of interest [VOI]-based) comparisons of ASL and DSC imaging in 13 patients with chronic steno-occlusive disease. RESULTS: In the qualitative analysis, distortion correction of the images led to a strong increase in diagnostic precision of ASL compared to DSC in the anterior cerebral artery (ACA) perfusion territory, where the susceptibility artifact was most pronounced (specificity 8% vs. 75%). In the quantitative analysis, the correlation between ASL and DSC values increased for all perfusion territories with the best improvement for the ACA territory (for anterior, middle and posterior cerebral artery: ACA: rho -0.22 vs. 0.71; MCA: rho 0.58 vs. 0.76; PCA: rho 0.58 vs. 0.63). CONCLUSIONS: We showed that susceptibility distortion correction strongly improves the comparability of multi-TI ASL 3D-GRASE to DSC in steno-occlusive disease. We suggest it to be implemented in ASL postprocessing routines.