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Background and Purpose: Randomized controlled trials have demonstrated the importance of time to endovascular therapy (EVT) on clinical outcomes in large vessel occlusion (LVO) acute ischemic stroke. Delays to treatment are particularly prevalent when patients require a transfer from hospitals without EVT capability onsite. A novel computer aided triage system, Viz LVO, has the potential to streamline workflows. This platform includes an image viewer, communication system, and an artificial intelligence (AI) algorithm that automatically identifies suspected LVO strokes on CTA imaging and rapidly triggers alerts. We hypothesize that the Viz application will decrease time-to-treatment. Methods: A prospective database was assessed for patients who presented to a stroke center utilizing Viz LVO in the Mount Sinai Health System in New York and underwent EVT following transfer for LVO stroke between July 2018 and March 2020. This time period was chosen due to the COVID-19 pandemic affecting stroke workflow after March 2020. Time intervals were compared for 55 patients divided into Pre- and Post-Viz cohorts. Results: The median initial door-to-neuroendovascular team (NT) notification time interval was significantly faster (25.0 minutes [IQR=12.0] vs 40.0 minutes [IQR=61.0]; p=0.01) with significantly less variation (p Conclusions: Our preliminary results have shown that Viz LVO implementation is associated with earlier, more consistent NT notification times and potentially treatment times. This platform presents a novel application of AI that can serve as an early warning system and a failsafe to ensure that no LVO is left behind. Further studies are warranted.

Study design Cross-sectional database study. Objective The objective of this study was to develop an algorithm for the automated measurement of spinopelvic parameters on lateral lumbar radiographs with comparable accuracy to surgeons. Summary of background data Sagittal alignment measurements are important for the evaluation of spinal disorders. Manual measurement methods are time-consuming and subject to rater-dependent error. Thus, a need exists to develop automated methods for obtaining sagittal measurements. Previous studies of automated measurement have been limited in accuracy, inapplicable to common plain films, or unable to measure pelvic parameters. Methods Images from 816 patients receiving lateral lumbar radiographs were collected sequentially and used to develop a convolutional neural network (CNN) segmentation algorithm. A total of 653 (80%) of these radiographs were used to train and validate the CNN. This CNN was combined with a computer vision algorithm to create a pipeline for the fully automated measurement of spinopelvic parameters from lateral lumbar radiographs. The remaining 163 (20%) of radiographs were used to test this pipeline. Forty radiographs were selected from the test set and manually measured by three surgeons for comparison. Results The CNN achieved an area under the receiver-operating curve of 0.956. Algorithm measurements of L1-S1 cobb angle, pelvic incidence, pelvic tilt, and sacral slope were not significantly different from surgeon measurement. In comparison to criterion standard measurement, the algorithm performed with a similar mean absolute difference to spine surgeons for L1-S1 Cobb angle (4.30° ± 4.14° vs. 4.99° ± 5.34°), pelvic tilt (2.14° ± 6.29° vs. 1.58° ± 5.97°), pelvic incidence (4.56° ± 5.40° vs. 3.74° ± 2.89°), and sacral slope (4.76° ± 6.93° vs. 4.75° ± 5.71°). Conclusion This algorithm measures spinopelvic parameters on lateral lumbar radiographs with comparable accuracy to surgeons. The algorithm could be used to streamline clinical workflow or perform large scale studies of spinopelvic parameters.Level of Evidence: 3.

Background and Purpose— Estimation of infarction based on computed tomographic perfusion (CTP) has been challenging, mainly because of noise associated with CTP data. The Bayesian method is a robust probabilistic method that minimizes effects of oscillation, tracer delay, and noise during residue function estimation compared with other deconvolution methods. This study compares CTP-estimated ischemic core volume calculated by the Bayesian method and by the commonly used block-circulant singular value deconvolution technique. Methods— Patients were included if they had (1) anterior circulation ischemic stroke, (2) baseline CTP, (3) successful recanalization defined by thrombolysis in cerebral infarction ≥IIb, and (4) minimum infarction volume of >5 mL on follow-up magnetic resonance imaging (MRI). CTP data were processed with circulant singular value deconvolution and Bayesian methods. Two established CTP methods for estimation of ischemic core volume were applied: cerebral blood flow (CBF) method (relative CBF, 2 seconds) and cerebral blood volume method (145%). Final infarct volume was determined on MRI (fluid-attenuated inversion recovery images). CTP and MRI-derived ischemic core volumes were compared by univariate and Bland-Altman analysis. Results— Among 35 patients included, the mean/median (mL) difference for CTP-estimated ischemic core volume against MRI was −4/−7 for Bayesian CBF ( P =0.770), 20/12 for Bayesian cerebral blood volume ( P =0.041), 21/10 for circulant singular value deconvolution CBF ( P =0.006), and 35/18 for circulant singular value deconvolution cerebral blood volume ( P Conclusions— Despite existing variabilities between CTP postprocessing methods, Bayesian postprocessing increases accuracy and limits variability in CTP estimation of ischemic core.

A 61-year-old man with a history of alcoholic cirrhosis was transferred from an outside hospital for spontaneous bacterial peritonitis, septic shock, and respiratory failure after intubation. The patient was initially on sedation; however, more than 48 hours after the sedative was discontinued, his mental status remained depressed and he also developed new onset of conjugate rightward gaze deviation. On neurologic examination, the patient was unresponsive to verbal stimuli and sternal rub. He could not follow any command, including closing or opening eyes and squeezing hands. He had remarkable conjugate, forced eye deviation that could not be corrected to cross the midline using the vestibulo-ocular reflex. Corneal and gag reflexes were preserved. He blinked to visual threat less on the left side, had no clear facial asymmetry, and withdrew on his right arm and knees but had a flaccid left arm. His reflexes were brisker on the left biceps and brachioradialis, and the other deep tendon reflexes were absent. His plantar reflex was upgoing on the left side and downgoing on the right side. CT of the head had no significant findings.

Beyond fat suppression (FS), the efficacy of (fat-water separation or Dixon [FWD]) Dixon imaging in gadolinium-enhanced spine imaging has yet to be validated. This study evaluated enhanced opposed-phase (OP) and fat-only (FO) images along with water-only (WO; FS) images against traditional unenhanced techniques and rated the incremental value of in-phase imaging in patients with presumed neoplastic focal spine lesions.A retrospective cohort study of 36 subjects with focal spine lesions imaged with FWD was evaluated qualitatively and quantitatively.Enhanced OP, WO, and FO images were of significant value in detection of osseous lesions, surpassing the lesion conspicuity with conventional techniques both qualitatively and quantitatively, although the impact of in-phase imaging was limited. Water-only imaging performed well for FS.Contrast-enhanced FO, WO, and OP outperform traditional techniques, providing reliable lesion characterization and highest conspicuity. In-phase imaging offered limited impact on the subjective assessment of enhancement. The added value and robustness of FWD, particularly the unique contrast provided by FO imaging, suggests consideration for routine use for postgadolinium spine imaging.

Introduction: Good collateral flow is an independent predictor of reperfusion that can be used to extend the treatment window in the new era of endovascular therapies for patients with acute ischemic stroke (AIS). Using a multiparametric approach, we aimed to identify perfusion parameter/s that can represent the extent of collaterals in comparison to CTA. Methods: AIS patients with anterior circulation large vessel occlusion who had baseline CTA and CT perfusion were included. CT perfusion data were processed by Bayesian method to generate arterial tissue delay (ATD) maps at thresholds of 2 & 6 seconds. The volume of mild delayed perfusion (Vol-ATD >2sec ), moderate delayed (Vol-ATD 2-6sec ) and critical delayed perfusion (Vol-ATD >6sec ) in addition to corresponding rCBV and rCBF were calculated. Baseline CTA collaterals were scored using an established scoring scale1 and dichotomized to poor or good. The association of perfusion parameters and status of collaterals was assessed by repeated measure of analyses and receiver operating characteristic (ROC). Results: In 28 patients included, 16 had good collaterals on CTA. After controlling for age, sex, baseline NIHSS and type of treatment, multivariate logistic regression analysis identified rCBV (p2-6sec (p=0.003), but not rCBF, Vol-ATD > 2sec or Vol-ATD >6sec , as independent predictors of good collaterals. ROC analysis showed AUC of 0.88 (sensitivity/specificity: 75%/100%) for rCBV and AUC of 0.84 (sensitivity/specificity: 93%/67%) for Vol-ATD 2-6sec . We defined a perfusion collateral index (PCI) calculated from Vol-ATD 2-6sec x its rCBV, that remained an independent predictor of good collaterals with improved diagnostic accuracy over each measure alone resulting in nominal AUC of 1 (sensitivity/specificity: 100%/100%). Conclusions: Multiparametric CT perfusion can be used to assess the status of collaterals in patients with AIS. Perfusion collateral index (PCI) defined as Vol-ATD 2-6sec x rCBV is a new perfusion index with a nominal diagnostic accuracy of 100% compared to baseline CTA to predict status of collaterals in our small cohort. Our results need to be validated in a larger prospective cohort.

Purpose: Collateral flow is an independent predictor of reperfusion and infarct size in patients with acute ischemic stroke (AIS). MR perfusion time-maps show delayed perfusion but unable to differentiate antegrade from collateral flow if used alone. Using a multiparametric approach, we aimed to identify a perfusion biomarker that can represent the extent of collaterals in comparison to angiographic findings. Methods: AIS patients with MCA proximal occlusion who had baseline MRI and cerebral angiography included. MR perfusion data were processed by Bayesian method to generate arterial tissue delay (ATD) maps at thresholds of 2 & 6 seconds. The volume of delayed perfusion (Vol-ATD 2sec ), critical hypoperfusion (Vol-ATD 6sec ), and hypoperfusion (Vol-ATD 2sec-6sec ) in addition to corresponding rCBV and rCBF were calculated. Baseline angiography collaterals were dichotomized to poor (TICI ≤2a) or good collaterals (TICI≥ 2b). The association of perfusion biomarkers and status of collaterals was assessed by repeated measure of analyses and receiver operating characteristic (ROC) to determine the optimal parameters for predicting the status of collaterals. Results: In 37 patients included, 20 had good collaterals on cerebral angiography. After controlling for age, baseline NIHSS and infarct volume, multivariate logistic regression analysis identified rCBV (p=0.001) and hypoperfused volume (Vol-ATD 2sec-6sec ), but not rCBF, Vol-ATD 2sec or Vol-ATD 6sec , as independent predictors of good collaterals. ROC analysis showed AUC of 0.89 (sensitivity/specificity: 85%90%) for rCBV and AUC of 0.78 (sensitivity/specificity: 70%82%) for Vol-ATD 2sec-6sec . Hypoperfused tissue volume (Vol-ATD 2sec-6sec ) x its rCBV, termed hypoperfused tissue collateral index , remained an independent predictor of good collaterals with improved diagnostic accuracy over each measure alone (AUC: 0.96, sensitivity/specificity: 91%100%). Conclusions: Multiparametric MR perfusion can be used to assess the status of collaterals in patients with AIS. Hypoperfused tissue collateral index defined as hypoperfused volume (Vol-ATD 2sec-6sec ) x rCBV is a new perfusion index with diagnostic accuracy of 96% compared to angiographic findings to predict status of collaterals.