Predicting Tissue Outcome in Acute Human Cerebral Ischemia Using Combined Diffusion- and Perfusion-Weighted MR Imaging

TLDR

Acute MR imaging tissue signatures can categorize physiological status to aid clinical decision making, and voxel‑wise risk assessment shows promise for studying ischemic damage evolution. The study aimed to develop voxel‑level statistical algorithms that predict infarction risk from acute diffusion‑ and perfusion‑weighted MRI. Using retrospective DWI and PWI scans obtained within 12 h of stroke onset, the authors built thresholding and generalized linear model algorithms and evaluated them with receiver‑operator‑characteristic curves. Combined DWI‑PWI algorithms achieved 66 % sensitivity and 83–84 % specificity, outperforming single‑modality methods and showing comparable performance between thresholding and GLM approaches.

Abstract

Background and Purpose —Tissue signatures from acute MR imaging of the brain may be able to categorize physiological status and thereby assist clinical decision making. We designed and analyzed statistical algorithms to evaluate the risk of infarction for each voxel of tissue using acute human functional MRI. Methods —Diffusion-weighted MR images (DWI) and perfusion-weighted MR images (PWI) from acute stroke patients scanned within 12 hours of symptom onset were retrospectively studied and used to develop thresholding and generalized linear model (GLM) algorithms predicting tissue outcome as determined by follow-up MRI. The performances of the algorithms were evaluated for each patient by using receiver operating characteristic curves. Results —At their optimal operating points, thresholding algorithms combining DWI and PWI provided 66% sensitivity and 83% specificity, and GLM algorithms combining DWI and PWI predicted with 66% sensitivity and 84% specificity voxels that proceeded to infarct. Thresholding algorithms that combined DWI and PWI provided significant improvement to algorithms that utilized DWI alone ( P =0.02) but no significant improvement over algorithms utilizing PWI alone ( P =0.21). GLM algorithms that combined DWI and PWI showed significant improvement over algorithms that used only DWI ( P =0.02) or PWI ( P =0.04). The performances of thresholding and GLM algorithms were comparable ( P >0.2). Conclusions —Algorithms that combine acute DWI and PWI can assess the risk of infarction with higher specificity and sensitivity than algorithms that use DWI or PWI individually. Methods for quantitatively assessing the risk of infarction on a voxel-by-voxel basis show promise as techniques for investigating the natural spatial evolution of ischemic damage in humans.