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Comparative Study
. 1998 Sep;29(9):1783-90.
doi: 10.1161/01.str.29.9.1783.

Diffusion-weighted magnetic resonance imaging in acute stroke

Affiliations
Comparative Study

Diffusion-weighted magnetic resonance imaging in acute stroke

K J van Everdingen et al. Stroke. 1998 Sep.

Abstract

Background and purpose: Diffusion-weighted MRI (DWI) is highly sensitive in detecting early cerebral ischemic changes in acute stroke patients. In this study we compared the sensitivity of DWI with that of conventional MRI techniques. Furthermore, we investigated the prognostic value of the volume of ischemic lesions on DWI scans and of the apparent diffusion coefficient (ADC).

Methods: We performed DWI, fluid-attenuated inversion recovery, spin-echo T2-weighted MRI, and spin-echo proton density-weighted MRI in 42 patients with acute stroke and 15 control subjects. The volume of ischemic lesions was measured on early (<60 hours after onset) and follow-up MRI scans. Clinical outcome was measured 4 months after onset of symptoms with the National Institutes of Health Stroke Scale, the Barthel Index, and the Rankin Scale.

Results: With DWI, 98% of the ischemic lesions were detected, and with fluid-attenuated inversion recovery, 91% were detected, whereas with early T2-weighted or proton density-weighted scans, only 71% (P=0.002, chi2) and 80% (P=0.02, chi2) of lesions, respectively, were found. Lesion volume on early DWI scans correlated significantly with clinical outcome ratings (P<0.01). In patients with a first-ever stroke, a lesion volume of < or =22 mL on DWI predicted good outcome with a 75% sensitivity and a 100% specificity. The mean ADC of ischemic lesions was 29% lower than the ADC of normal-appearing parts of the brain (P<0.001). The ADC ratio correlated significantly with clinical outcome (P<0.05).

Conclusions: DWI is a better imaging method than conventional MRI in detecting early ischemic lesions in stroke patients. Lesion size as measured on DWI scans and, to a lesser extent, ADC values are potential parameters for predicting clinical outcome in acute stroke patients.

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