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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.

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Laminar fMRI based on BOLD and CBV contrast at ultrahigh magnetic fields has been applied for studying the dynamics of mesoscopic brain networks. However, the quantitative interpretations of BOLD/CBV fMRI results are confounded by different baseline physiology across cortical layers. Here we introduce a novel 3D zoomed pseudo-continuous arterial spin labeling technique at 7T that offers the unique capability for quantitative measurements of laminar cerebral blood flow (CBF) both at rest and during task activation with high spatial specificity and sensitivity. We found arterial transit time in superficial layers is ~100 msec shorter than in middle/deep layers revealing the dynamics of labeled blood flowing from pial arteries to downstream microvasculature. Resting state CBF peaked in the middle layers which is highly consistent with microvascular density measured from human cortex specimens. Finger tapping induced a robust two-peak laminar profile of CBF increases in the superficial (somatosensory and premotor input) and deep (spinal output) layers of M1, while finger brushing task induced a weaker CBF increase in superficial layers (somatosensory input). We further demonstrated that top-down attention induced a predominant CBF increase in deep layers and a smaller CBF increase on top of the lower baseline CBF in superficial layers of V1 (feedback cortical input), while bottom-up stimulus driven activity peaked in the middle layers (feedforward thalamic input). These quantitative laminar profiles of perfusion activity suggest an important role of M1 superficial layers for the computation of finger movements, and that visual attention may amplify deep layer output to the subcortex.

Purpose: To present a pulse sequence and mathematical models for quantification of blood-brain barrier water exchange and permeability. Methods: Motion-compensated diffusion-weighted (MCDW) gradient-and-spin echo (GRASE) pseudo-continuous arterial spin labeling (pCASL) sequence was proposed to acquire intravascular/extravascular perfusion signals from five postlabeling delays (PLDs, 1590-2790 ms). Experiments were performed on 11 healthy subjects at 3 T. A comprehensive set of perfusion and permeability parameters including cerebral blood flow (CBF), capillary transit time (τ c ), and water exchange rate (k w ) were quantified, and permeability surface area product (PS w ), total extraction fraction (E w ), and capillary volume (V c ) were derived simultaneously by a three-compartment single-pass approximation (SPA) model on group-averaged data. With information (i.e., V c and τ c ) obtained from three-compartment SPA modeling, a simplified linear regression of logarithm (LRL) approach was proposed for individual k w quantification, and E w and PS w can be estimated from long PLD (2490/2790 ms) signals. MCDW-pCASL was compared with a previously developed diffusion-prepared (DP) pCASL sequence, which calculates k w by a two-compartment SPA model from PLD = 1800 ms signals, to evaluate the improvements. Results:Using three-compartment SPA modeling, group-averaged CBF = 51.5/36.8 ml/100 g/min, k w = 126.3/106.7 min −1 , PS w = 151.6/93.8 ml/100 g/min, E w = 94.7/92.2%, τ c = 1409.2/1431.8 ms, and V c = 1.2/0.9 ml/100 g in gray/white matter, respectively. Temporal SNR of MCDW-pCASL perfusion signals increased 3-fold, and individual k w maps calculated by the LRL method achieved higher spatial resolution (3.5 mm 3 isotropic) as compared with DP pCASL (3.5 × 3.5 × 8 mm 3 ). Conclusion: MCDW-pCASL allows visualization of intravascular/extravascular ASL signals across multiple PLDs. The three-compartment SPA model provides a comprehensive measurement of blood-brain barrier water dynamics from group-averaged data, and a simplified LRL method was proposed for individual k w quantification. K E Y W O R D S blood-brain barrier (BBB), diffusion-weighted arterial spin labeling (DW-ASL), water exchange rate (k w ), water permeability (PS w )This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.