BACKGROUND AND OBJECTIVE: Estimates of parameters used to select patients for endovascular thrombectomy (EVT) for acute ischemic stroke differ among software packages for automated computed tomography (CT) perfusion analysis. To determine impact of these differences in decision making, we analyzed intra-observer and inter-observer agreement in recommendations about whether to perform EVT based on perfusion maps from 4 packages. METHODS: Perfusion CT datasets from 63 consecutive patients with suspected acute ischemic stroke were retrospectively postprocessed with 4 packages of Minerva, RAPID, Olea, and IntelliSpace Portal (ISP). We used Pearson correlation coefficients and Bland-Altman analysis to compare volumes of infarct core, penumbra, and mismatch calculated by Minerva and RAPID. We used kappa analysis to assess agreement among decisions of 3 radiologists about whether to recommend EVT based on maps generated by 4 packages. RESULTS: We found significant differences between using Minerva and RAPID to estimate penumbra (67.39±41.37mL vs. 78.35±45.38 mL, p <  0.001) and mismatch (48.41±32.03 vs. 61.27±32.73mL, p <  0.001), but not of infarct core (p = 0.230). Pearson correlation coefficients were 0.94 (95%CI:0.90–0.96) for infarct core, 0.87 (95%CI:0.79–0.91) for penumbra, and 0.72 (95%CI:0.57–0.83) for mismatch volumes (p <  0.001). Limits of agreements were (–21.22–25.02) for infarct core volumes, (–54.79–32.88) for penumbra volumes, and (–60.16–34.45) for mismatch volumes. Final agreement for EVT decision-making was substantial between Minerva vs. RAPID (k = 0.722), Minerva vs. Olea (k = 0.761), and RAPID vs. Olea (k = 0.782), but moderate for ISP vs. the other three. CONCLUSIONS: Despite quantitative differences in estimates of infarct core, penumbra, and mismatch using 4 software packages, their impact on radiologists’ decisions about EVT is relatively small.

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Characterization of the heart anatomy and function is mostly done with magnetic resonance image cine series. To achieve a correct characterization, the volume of the right and left ventricle need to be segmented, which is a timeconsuming task. We propose a new convolutional neural network architecture that combines U-net with PSP modules (PSPU-net) for the segmentation of left and right ventricle cavities and left ventricle myocardium in the diastolic frame of short-axis cine MRI images and compare its results against a classic 3D U-net architecture. We used a dataset containing 399 cases in total. The results showed higher quality results in both segmentation and final volume estimation for a test set of 99 cases in the case of the PSPU-net, with global dice metrics of 0.910 and median absolute relative errors in volume estimations of 0.026 and 0.039 for the left ventricle cavity and myocardium and 0.051 for the right ventricles cavity.

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