Detection of Ischemic Infarct Core in Non-contrast Computed Tomography

Abstract

Fast diagnosis is of critical importance for stroke treatment. In clinical routine, a non-contrast computed tomography scan (NCCT) is typically acquired immediately to determine whether the stroke is ischemic or hemorrhagic and plan therapy accordingly. In case of ischemia, early signs of infarction may appear due to increased water uptake. These signs may be subtle, especially if observed only shortly after symptom onset, but hold the potential to provide a crucial first assessment of the location and extent of the infarction. In this paper, we train a deep neural network to predict the infarct core from NCCT in an image-to-image fashion. To facilitate exploitation of anatomic correspondences, learning is carried out in the standardized coordinate system of a brain atlas to which all images are deformably registered. Apart from binary infarct core masks, perfusion maps such as cerebral blood volume and flow are employed as additional training targets to enrich the physiologic information available to the model. This extension is demonstrated to substantially improve the predictions of our model, which is trained on a data set consisting of 141 cases. It achieves a higher volumetric overlap (statistically significant,) of the predicted core with the reference mask as well as a better localization, although significance could not be shown for the latter. Agreement with human and automatic assessment of affected ASPECTS regions is likewise improved, measured as an increase of the area under the receiver operating characteristic curve from 72.7% to 75.1% and 71.9% to 83.5%, respectively.