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Deep Learning Detection of Penumbral Tissue on Arterial Spin Labeling in Stroke.

ABSTRACT: 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.

SUBMITTER: Wang K

PROVIDER: S-EPMC7224203 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Deep Learning Detection of Penumbral Tissue on Arterial Spin Labeling in Stroke.

Wang Kai K Shou Qinyang Q Ma Samantha J SJ Liebeskind David D Qiao Xin J XJ Saver Jeffrey J Salamon Noriko N Kim Hosung H Yu Yannan Y Xie Yuan Y Zaharchuk Greg G Scalzo Fabien F Wang Danny J J DJJ

Stroke 20191230 2

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 selecti ...[more]

PMID: 31884904

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Project description:BackgroundArterial spin labeling (ASL) perfusion-weighted imaging (PWI) by magnetic resonance imaging (MRI) has been shown to be useful for identifying asphyxiated newborns at risk of developing brain injury, whether or not therapeutic hypothermia was administered. However, this technique has been only rarely used in newborns until now, because of the challenges to obtain sufficient signal-to-noise ratio (SNR) and spatial resolution in newborns.ObjectiveTo compare two methods of ASL-PWI (i.e., single inversion-time pulsed arterial spin labeling [single TI PASL], and pseudo-continuous arterial spin labeling [pCASL]) to assess brain perfusion in asphyxiated newborns treated with therapeutic hypothermia and in healthy newborns.Design/methodsWe conducted a prospective cohort study of term asphyxiated newborns meeting the criteria for therapeutic hypothermia; four additional healthy term newborns were also included as controls. Each of the enrolled newborns was scanned at least once during the first month of life. Each MRI scan included conventional anatomical imaging, as well as PASL and pCASL PWI-MRI. Control and labeled images were registered separately to reduce the effect of motion artifacts. For each scan, the axial slice at the level of the basal ganglia was used for comparisons. Each scan was scored for its image quality. Quantification of whole-slice cerebral blood flow (CBF) was done afterwards using previously described formulas.ResultsA total number of 61 concomitant PASL and pCASL scans were obtained in nineteen asphyxiated newborns treated with therapeutic hypothermia and four healthy newborns. After discarding the scans with very poor image quality, 75% (46/61) remained for comparison between the two ASL methods. pCASL images presented a significantly superior image quality score compared to PASL images (p < 0.0001). Strong correlation was found between the CBF measured by PASL and pCASL (r = 0.61, p < 0.0001).ConclusionThis study demonstrates that both ASL methods are feasible to assess brain perfusion in healthy and sick newborns. However, pCASL might be a better choice over PASL in newborns, as pCASL perfusion maps had a superior image quality that allowed a more detailed identification of the different brain structures.

Project description: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.

Dataset Information

Deep Learning Detection of Penumbral Tissue on Arterial Spin Labeling in Stroke.

Publications

Deep Learning Detection of Penumbral Tissue on Arterial Spin Labeling in Stroke.

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