Predicting tissue outcome in acute human cerebral ischemia using combined diffusion- and perfusion-weighted MR imaging

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 evalua te the risk of infarction for each voxel of tissue using acute human functi onal 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 generaliz ed 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 combinin g DWI and PWI provided 66% sensitivity and 83% specificity, and GLM algorit hms combining DWI and PWI predicted with 66% sensitivity and 84% specificit y voxels that proceeded to infarct, Thresholding algorithms that combined D WI and PWI provided significant improvement to algorithms that utilized DWI alone (P=0.02) but no significant improvement over algorithms utilizing PW I alone (P=0.21). GLM algorithms that combined DWI and PWI showed significa nt 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 o f infarction with higher specificity and sensitivity than algorithms that u se DWI or PWI individually. Methods for quantitatively assessing the risk o f infarction on a voxel-by-voxel basis show promise as techniques for inves tigating the natural spatial evolution of ischemic damage in humans.