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Estimation of Acute Infarct Core and Hypoperfused Region from Baseline Noncontrast Computed Tomography and Computed
Mahsa Mojtahedi1, Lucas de Vries1,2, Laura van Poppel1
1Department of Biomedical Engineering and Physics University of Amsterdam AZ Amsterdam The Netherlands.
Background:
In acute ischemic stroke, the infarct core and hypoperfused regions are key indicators for assessing and prognosticating patients. They are typically estimated with computed tomography perfusion (CTP). However, because noncontrast CT and CT angiography are more widely available, we trained a neural network to estimate the ischemic lesion from noncontrast CT and CT angiography scans.
Methods:
In this retrospective study, an nnU-Net model was trained to estimate infarcted and hypoperfused regions from noncontrast CT and CT angiography using reference standards from a commercial CTP software (StrokeViewer). We included data from 859 patients for training and 137 for testing. We used data from the Collaboration for New Treatments of Acute Stroke consortium, including MR CLEAN (Multicenter Randomized Controlled Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands)-NO-IV, MR CLEAN-MED, MR CLEAN-LATE, and MR CLEAN-Registry, and a local cohort. In addition to testing our model against StrokeViewer, we also compared our results with 3 other commercial CTP software packages.
Results:
Our model achieves a Dice of 0.45 (95% CI, 0.39-0.50) for core and 0.66 (95% CI, 0.62-0.69) for hypoperfused region, underestimating core volume by -9.3 mL (95% CI, -12.5 to -6.1) and hypoperfused region volume by -12.9 mL (95% CI, -21.1 to -4.7) compared with StrokeViewer. When comparing the 4 CTP software packages together, the average of their 2-by-2 agreement ranges from a Dice of 0.22 to 0.28 for core, and a Dice of 0.50 to 0.56 for hypoperfused region. This is similar to the average agreement of nnU-Net with these 4 software packages (average Dice 0.27 for core and 0.56 for hypoperfused). Furthermore, nnU-Net produces fewer connected components (1.3 for core, 1.6 for hypoperfused) than the average of the 4 CTP software packages (60.8 for core and 110.8 and hypoperfused), indicating more cohesive segmentations.
Conclusion:
Our model's performance in segmenting infarct core and hypoperfused regions from noncontrast CT and CT angiography is comparable to commercial CTP software packages, with potentially fewer segmentation artifacts. It can therefore be used when CTP is not available.
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