Textures in magnetic resonance images of the ischemic rat brain treated with an anti-inflammatory agent☆
Received 1 December 2008; accepted 19 February 2009. published online 05 May 2009.
Abstract
Computer-based analysis of textures in magnetic resonance images provides a higher sensitivity to textural changes that cannot be recognized by the naked human eye. Thus, there is a better potential for identifying pathophysiological processes at an earlier stage or of a different character than even a trained radiologist can find. In the present study, the potential of texture analysis for in vivo identification of the administering effect of an anti-inflammatory drug in cerebral stroke in rats was evaluated.
Twenty-seven Wistar rats underwent middle cerebral artery occlusion resulting in local ischemic brain infarct. One group of rats received alpha-melanocyte stimulating hormone (α-MSH) and a control group received saline only. T2-weighted images, apparent diffusion maps, and T2 maps were recorded by MR. Texture features were calculated in the T2-weighted images and correlated to the apparent diffusion coefficient (ADC) and the T2 values. From an array of tested texture features three independent features were tested further. Two of which were found to provide a significant discriminative classification between the control and the α-MSH groups. Furthermore, the same two texture features were significantly correlated to the ADCs. Thus, quantification of texture features can be helpful in detecting the effects of stroke therapy.
aMR Research Centre, Aarhus University Hospital, 8200 Aarhus N, Denmark
bInstitute of Electronics, Technical University of Lodz, Lodz, Poland
cDepartment of Neurosurgery, Shandong Provincial Hospital, Shandong University, Shandong 250021, China
dDivision of Electronics and Information Engineering, Chonbuk National University, 561-756 Jeonju, Korea
Corresponding author. MR Research Center, Aarhus University Hospital, Skejby, DK8200 Aarhus N, Denmark. Tel.: +45 89495264; fax: +45 89495264/89496004.
☆ This work was supported by the Aarhus University Research Foundation and the Danish Helga and Peter Kornings Foundation. This research was partially supported by the MIC, Korea, under the IT Foreign Specialist Inviting Program supervised by the Institute of Information Technology Advancement (IITA).