Concurrent acute ischemic stroke and acute myocardial infarction is an uncommon medical emergency condition. The challenge for the physicians regarding the management of this situation is paramount since early management of one condition will inevitably delay the other. We present two illustrative cases of “hyperacute simultaneous cardiocerebral infarction” who presented with simultaneous cardiocerebral infarction and arrived at the hospital within the thrombolytic therapeutic window for acute ischemic stroke of 4.5 h. We propose an algorithm for managing the patient with hyperacute simultaneous cardiocerebral infarction based on hemodynamic status and suggest close cardiac monitoring based on the site of cerebral infarction.

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Background and Purpose: Accurate automated infarct segmentation is needed for acute ischemic stroke studies relying on infarct volumes as an imaging phenotype or biomarker that require large numbers of subjects. This study investigates whether an ensemble of convolutional neural networks (CNN) trained on multiparametric DWI maps outperforms single networks trained on solo DWI parametric maps. Materials and Methods: CNNs were trained on combinations of DWI, ADC, and low b-value-weighted images from 116 subjects. The performances of the networks (measured by Dice score, sensitivity and precision) were compared to one another and to ensembles of 5 networks. To assess the generalizability of the approach, the best performing model was applied to an independent evaluation cohort of 151 subjects. Agreement between manual and automated segmentations for identifying patients with large lesions volumes was calculated across multiple thresholds (21 cm3, 31 cm3, 51 cm3, and 70 cm3). Results An ensemble of CNNs trained on DWI, ADC and low b-value-weighted images produced the most accurate acute infarct segmentation over individual networks (p<0.0001). Automated volumes correlated with manually measured volumes (Spearman’s ρ=0.91, p<0.0001) for the independent cohort. For the task of identifying patients with large lesion volumes, agreement between manual outlines and automated outlines was high (Cohen’s κ 0.86 to 0.90, p<0.0001). Conclusion Acute infarcts are more accurately segmented using ensembles of CNNs trained with multi-parametric maps than using a single model trained with a solo map. Automated lesion segmentation can perform with high agreement with manual techniques for identifying patients with large lesion volumes.