Predicting tissue outcome in stroke: new approaches

Abstract

Multimodal MRI provides powerful tools to study acute stroke pathophysiology and to guide stroke therapy. In particular, the perfusion-diffusion mismatch has been hypothesized as a target for treatment beyond the 3 h time window. Studies of infarct progression and of tissue oxygen metabolism suggest that infarct risk is extremely heterogeneous across the diffusion and perfusion lesion. The review describes techniques to more accurately image and model penumbral infarct risk. Methods assessing oxygen supply by either blood oxygen level-dependent contrast MRI or models of oxygen delivery capacity may improve the detection of tissue-at-risk. Informatics approaches integrate acute multimodal and follow-up images from large patient cohorts into models of infarct progression. When applied to subsequent acute image data, these techniques may assign infarct risks to mismatch tissue. Recent studies suggest that such estimates of tissue infarct risk may detect treatment-related risk reduction in small patient cohorts. MRI methods may detect markers of metabolic derangement in ischemia, facilitating the detection of penumbral tissue. Predictive models extend the current perfusion-diffusion mismatch concept by estimating voxel-based risk estimates. With future developments, predictive models may support advanced prognostic support and cost-effective testing of novel stroke therapies.