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This cine FSE method produced repeatable dynamic carotid artery measurements with less artifact and greater temporal efficiency compared with prospectively gated FSE.

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Purpose: To validate the performance of a 3D convolutional neural network (CNN) based algorithm i.e. Stroke SENS LVO, in automatically detecting the presence of large vessel occlusions (LVO) on computed tomography angiography (CTA) images of the head. Method: A total of 400 studies (217 LVO, 183 non-LVO) were used in the analysis. The LVO group includes internal carotid artery (ICA) and m1 segment of the middle cerebral artery (M1-MCA) occlusions; and the non-LVO group includes more distal or posterior cerebral artery occlusions, no occlusions, and hemorrhagic stroke cases. Expert consensus reads were used as reference standard. Performance was evaluated using sensitivity and specificity and corresponding 95% confidence intervals (CI). Additional analysis was performed on several subgroups of interest. Results: For detecting LVO, the algorithm achieved a sensitivity of 0.894 [0.853, 0.935] and specificity of 0.874 [0.826, 0.922]. Furthermore, sensitivities of 0.857 [0.779, 0.935] on ICA cases (N=77) and 0.914 [0.868, 0.961] on M1-MCA cases (N=140) were noted; similarly, specificities of 0.891 [0.833, 0.949] on hemorrhagic stroke cases (N=110) and 0.849 [0.767, 0.931] on non-LVO-non-hemorrhage cases (N=73) were noted. Similar performances were observed across stratified datasets based on age, sex, scanner manufacturer and slice thickness when compared to the full cohort. Conclusion: Stroke SENS LVO demonstrated high accuracy in automatic detection of LVO on a large heterogeneous dataset.