The Existence and Evolution of Diffusion–Perfusion Mismatched Tissue in White and Gray Matter After Acute Stroke

Abstract

Background and Purpose-Although white matter is a potential target of acute stroke therapy, there is uncertainty about its relative resistance to ischemia and whether it is capable of mounting a penumbral response. To explore these issues further, we examined the differential effects of ischemia on gray and white matter using magnetic resonance (MR) perfusion-diffusion mismatch after acute stroke. Methods-MR imaging studies were performed within 12 hours in patients with initial hemispheric ischemic stroke."At-risk" tissue was defined as tissue with abnormal diffusion-weighted imaging/perfusion-weight imaging or infarction on follow-up image. Tissue was segmented using a probabilistic atlas generated from age-matched controls. The proportions of "at-risk" tissue, which was penumbral at the time of imaging, were compared between gray and white matter. Results-Thirty-two patients had diffusion-perfusion mismatched penumbral tissue present in both gray and white matter compartments. Although the absolute mismatch volumes were greater in gray (median 42 cm 3 , interquartile range 18 to 70 cm 3 ) than in white matter (39 cm 3 , 17 to 49 cm 3 ; PϽ0.001), the proportion of "at-risk" tissue, which was penumbral at the time of imaging (median 3.7 hours, range 1.5 to 9.9 hours) was greater in white (69%, 49% to 86%) than gray matter (62%, 52% to 75%; Pϭ0.026). However, the proportions spontaneously salvaged by 3 months were similar in both compartments. Conclusions-These findings are consistent with white matter being able to mount an ischemic penumbral response in humans and being more resistant to cerebral ischemia than gray matter. They also raise the possibility that the therapeutic window is longer for white matter and may require alternative therapeutic strategies. (Stroke. 2005;36:2132-2137.)

Is White Matter More Prone to Diffusion Lesion Reversal After Thrombolysis?

“…Although no previous similar study is available for a comparison, our finding is consistent with the notion that WM may be more resistant to ischemia compared with GM. [1][2][3][4] Experimental studies also support this notion. 4 In humans, although the fate of WM versus GM has rarely been studied, our results are consistent with the available data, irrespective of the imaging approach.…”

“…4 In humans, although the fate of WM versus GM has rarely been studied, our results are consistent with the available data, irrespective of the imaging approach. [1][2][3]9,10 For instance, a positron emission tomography study using the hypoxia tracer 18 F-misonidazole found salvageable tissue in WM at later time points than in GM, suggesting that the former has a slower evolution toward infarction. 2 At earlier time points and using perfusion DWI, the same investigators again emphasized the greater resistance of WM to ischemia.…”

“…The accuracy of normalization was assessed by visual inspection. After segmentation based on a probabilistic map (threshold=0.5) 3 for WM, GM and cerebrospinal fluid obtained from the 3DT1 Montreal Neurological Institute template, DWI 1 , core, and RAD lesion ROI volumes and their WM/GM distribution were calculated using a voxel-counting algorithm (Matlab 7.9.0; Figure 1). Cerebrospinal fluid and unclassified voxels (<3%) were discarded.…”

“…Accordingly, animal and human studies based on histopathology or imaging have suggested that WM is more resistant than GM. [1][2][3][4] Assessing the fate of GM versus WM within the acute diffusion-weighted imaging (DWI) lesion may serve to test this hypothesis. Whether WM lesions are more prone to reversal than GM lesions is also clinically relevant because if true, it would imply that reperfusion would be more efficient if DWI lesions predominate in WM.…”

Predicting final extent of ischemic infarction using an artificial neural network analysis of multiparametric MRI in patients with stroke

“…Most of these analyses were performed using as an outcome metric the volume of a region-of-interest (ROI) defined by the difference between an acute data set and the outcome image [9,10,11]. For instance, it has been shown that an unsupervised clustering technique such as ISODATA (Iterative Self-Organizing Data Analysis) can utilize combined MRI data sets from the acute phase [2,12] and the subacute phase [5] post-stroke to predict final infarct volume and thus produce a time-independent surrogate MRI outcome predictor [13,14,15,16,17]. However ISODATA has some significant drawbacks.…”