Computer-Aided Diagnosis of Acute Ischemic Stroke Based on Cerebral Hypoperfusion Using 4D CT Angiography

The presence of collateral blood flow is found to be a strong predictor of patient outcome after acute ischemic stroke. Collateral blood flow is defined as an alternative way to provide oxygenated blood to ischemic cerebral tissue. Assessment of collateral blood supply is currently performed by visual inspection of a Computed Tomography Angiogram (CTA) which introduces inter-observer variability and depends on the grading scale. Furthermore, variations in the arterial contrast arrival time may lead to underestimation of collateral blood supply in a CTA which exerts a negative influence on the prediction of patient outcome. In this study, the feasibility of a Computer-aided Diagnosis system is investigated capable of objectively predicting patient outcome. We present a novel automatic method for quantitative assessment of cerebral hypoperfusion in timing-invariant (i.e. delay insensitive) CTA (TI-CTA). The proposed Vessel Density Symmetry algorithm automatically generates descriptive maps based on hemispheric asymmetry of blood vessels. Intensity and symmetry based features are extracted from these descriptive maps and subjected to a best-first-search feature selection. Linear Discriminant Analysis is performed to combine selected features into a likelihood of good patient outcome. Receiver operating characteristic (ROC) analysis is conducted to evaluate the diagnostic performance of the CAD by leave-one-patient-out cross validation. A Positive Predicting Value of 1 was obtained at a sensitivity of 25% with an area under the ROC-curve of 0.86. The results show that the CAD is feasible to objectively predict patient outcome. The presented CAD could make an important contribution to acute ischemic stroke diagnosis and treatment.