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Background and Purpose- Selection of patients with acute ischemic stroke for endovascular treatment generally relies on dynamic susceptibility contrast magnetic resonance imaging or computed tomography perfusion. Dynamic susceptibility contrast magnetic resonance imaging requires injection of contrast, whereas computed tomography perfusion requires high doses of ionizing radiation. The purpose of this work was to develop and evaluate a deep learning (DL)-based algorithm for assisting the selection of suitable patients with acute ischemic stroke for endovascular treatment based on 3-dimensional pseudo-continuous arterial spin labeling (pCASL). Methods- A total of 167 image sets of 3-dimensional pCASL data from 137 patients with acute ischemic stroke scanned on 1.5T and 3.0T Siemens MR systems were included for neural network training. The concurrently acquired dynamic susceptibility contrast magnetic resonance imaging was used to produce labels of hypoperfused brain regions, analyzed using commercial software. The DL and 6 machine learning (ML) algorithms were trained with 10-fold cross-validation. The eligibility for endovascular treatment was determined retrospectively based on the criteria of perfusion/diffusion mismatch in the DEFUSE 3 trial (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke). The trained DL algorithm was further applied on twelve 3-dimensional pCASL data sets acquired on 1.5T and 3T General Electric MR systems, without fine-tuning of parameters. Results- The DL algorithm can predict the dynamic susceptibility contrast-defined hypoperfusion region in pCASL with a voxel-wise area under the curve of 0.958, while the 6 ML algorithms ranged from 0.897 to 0.933. For retrospective determination for subject-level endovascular treatment eligibility, the DL algorithm achieved an accuracy of 92%, with a sensitivity of 0.89 and specificity of 0.95. When applied to the GE pCASL data, the DL algorithm achieved a voxel-wise area under the curve of 0.94 and a subject-level accuracy of 92% for endovascular treatment eligibility. Conclusions- pCASL perfusion magnetic resonance imaging in conjunction with the DL algorithm provides a promising approach for assisting decision-making for endovascular treatment in patients with acute ischemic stroke.

The purpose of this study was to develop and evaluate a scoring system for assessing reperfusion status based on arterial spin labeled (ASL) perfusion MRI in acute ischemic stroke (AIS) patients receiving thrombolysis and/or endovascular treatment. Pseudo-continuous ASL with background suppressed 3D GRASE was acquired along with DWI in 90 patients within 24 h post-treatment. An automatic reperfusion scoring system (auto-RPS) was devised based on the Alberta Stroke Program Early CT Score (ASPECTS) template, and compared with manual RPS and DWI-ASPECTS. TICI (thrombolysis in cerebral infarction) scores were graded in 48 patients who received endovascular treatment. Favorable outcomes were defined by a modified Rankin Scale score of 0-2 at three months. Auto-RPS was positively correlated with DWI-ASPECTS (ρ = 0.6, P

PurposeDifferentiating WHO grade I-III of meningioma by non-invasive imaging is challenging. This study investigated the potential of MR arterial spin labeling (ASL) to establish tumor grade in meningioma patients.Material and methodsPseudo-continuous ASL with 3D background suppressed gradient and spin echo (GRASE) was acquired on 54 patients with newly diagnosed or recurrent intracranial meningioma. Perfusion patterns characterized in CBF color maps were independently evaluated by three neuroradiologists blinded to patient history, and correlated with tumor grade from histo-pathological review.ResultsThree perfusion patterns could be discerned by visual evaluation of CBF maps. Pattern 1 consisted of homogeneous hyper-perfusion of the entire tumor; pattern 2 demonstrated heterogeneous hyper-perfusion; pattern 3 showed no substantial hyper-perfusion. Evaluation of the perfusion patterns was highly concordant among the three readers (Kendall W=0.9458, P

The purpose of this study was to investigate the relationship between hyperperfusion and hemorrhagic transformation (HT) in acute ischemic stroke (AIS). Pseudo-continuous arterial spin labeling (ASL) with background suppressed 3D GRASE was performed during routine clinical magnetic resonance imaging (MRI) on AIS patients at various time points. Arterial spin labeling cerebral blood flow (CBF) maps were visually inspected for the presence of hyperperfusion. Hemorrhagic transformation was followed during hospitalization and was graded on gradient recalled echo (GRE) scans into hemorrhagic infarction (HI) and parenchymal hematoma (PH). A total of 361 ASL scans were collected from 221 consecutive patients with middle cerebral artery stroke from May 2010 to September 2013. Hyperperfusion was more frequently detected posttreatment (odds ratio (OR) = 4.8, 95% confidence interval (CI) 2.5 to 8.9, P < 0.001) and with high National Institutes of Health Stroke Scale (NIHSS) scores at admission (P

The purpose of this study was to develop and evaluate a scoring system for assessing reperfusion status based on arterial spin labeled (ASL) perfusion MRI in acute ischemic stroke (AIS) patients receiving thrombolysis and/or endovascular treatment. Pseudo-continuous ASL with background suppressed 3D GRASE was acquired along with DWI in 90 patients within 24 h post-treatment. An automatic reperfusion scoring system (auto-RPS) was devised based on the Alberta Stroke Program Early CT Score (ASPECTS) template, and compared with manual RPS and DWI-ASPECTS. TICI (thrombolysis in cerebral infarction) scores were graded in 48 patients who received endovascular treatment. Favorable outcomes were defined by a modified Rankin Scale score of 0-2 at three months. Auto-RPS was positively correlated with DWI-ASPECTS (ρ = 0.6, P < 0.001) and was on average 1 point lower than DWI-ASPECTS ( P < 0.001). The area under the receiver operating characteristic curve for discriminating poor functional outcome (n = 90) was 0.75 (95% CI, 0.64-0.86) for manual RPS, 0.85 (95% CI, 0.76-0.94) for auto-RPS, and 0.81 (95% CI, 0.71-0.90) for DWI-ASPECTS. Multiple logistic regression analysis in the TICI-graded patients (n = 48) showed that auto-RPS is highly associated with functional outcome (OR = 25.2, 95% CI 4.02-496, P < 0.01). Post treatment auto-RPS within 24 h provides a useful tool to predict functional outcome in AIS patients.