Apparent diffusion coefficient mapping of experimental focal cerebral ischemia using diffusion-weighted echo-planar imaging

✍ Scribed by Bernard J. Dardzinski; Christopher H. Sotak; Marc Fisher; Yasuhiro Hasegawa; Lirnin Li; Kazuo Minematsu

• Publisher: John Wiley and Sons
• Year: 1993
• Tongue: English
• Weight: 1019 KB
• Volume: 30
• Category: Article
• ISSN: 0740-3194
• DOI: 10.1002/mrm.1910300307

✦ Synopsis

Abstract

Diffusion‐weighted, echo‐planar imaging (EPI) was used to map regional changes In the apparent diffusion coefficient (ADC) during experimental focal ischemia in the rat brain following permanent middle cerebral arterial occlusion (MCAO). Sixteen 64 × 64 diffusion‐weighted EPIs were acquired in 32 s with successively increasing amplitudes of the diffusion‐sensitive gradient pulses. A linear least‐squares regression algorithm was used to fit 15 of the 16 two‐dimensional matrices, on a pixel‐by‐pixel basis, to solve for the slope from which the ADC value was calculated. The correlation coefficient of the fit, R^2^ was used to filter the final ADC maps, and the ADCs were then scaled appropriately to be displayed in a 256 gray level format. Ranges (bins) of 0.05 × 10^−3^ mm^2^/s were then grouped and color coded to qualify and quantify the evolution of ischemia in the MCA territory. The percentage of area in the ischemic and contralateral hemispheres in seven ADC bins were calculated at 30, 60, and 120 min after MCAO for 10 animals and demonstrated a significant increase in ADC bins below 0.45 × 10^−3^ mm^2^/s and a decrease in bins above 0.50 × 10^−3^ mm^2^/s over the. The postmortem infarct area, as measured by TTC staining, was highly correlated with the portion of the ischemic hemisphere falling below ADC values of 0.55 × 10^−3^ mm^2^/s at 2 h after stroke onset. These studies suggest that focally ischemic brain tissue can be quantitatively subdivided according to ADC values and that ADC values below 0.55 × 10^−3^ mm^2^/s 2 h following ischemia highly predict infarction in a rat permanent occlusion stroke model.