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(Stroke. 2001;32:943.)
© 2001 American Heart Association, Inc.

Original Contributions

A Model for Multiparametric MRI Tissue Characterization in Experimental Cerebral Ischemia With Histological Validation in Rat

Part 1

Presented and published in part at the 25th International Stroke Conference, New Orleans, La, February 10–12, 2000.

Michael A. Jacobs, PhD; Zheng G. Zhang, MD, PhD; Robert A. Knight, PhD; Hamid Soltanian-Zadeh, PhD; Anton V. Goussev, MD; Donald J. Peck, PhD Michael Chopp, PhD

From the Departments of Neurology (M.A.J., Z.G.Z., R.A.K., A.V.G., M.C.) and Radiology, Medical Image Analysis Research (M.A.J., H.S-Z., D.J.P.), Henry Ford Health Sciences Center, Detroit, Mich; Department of Electrical and Computer Engineering, University of Tehran (Iran) (H.S-Z.); Department of Physics, Oakland University, Rochester, Mich (M.A.J., M.C.); and Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Md (M.A.J.).

Correspondence to Michael Chopp, PhD, Department of Neurology, Center for Stroke Research, Henry Ford Hospital E&R 3056, 2799 W Grand Blvd, Detroit, MI 48202. E-mail chopp{at}neuro.hfh.edu

Background and Purpose—After stroke, brain tissue undergoes time-dependent heterogeneous histopathological change. These tissue alterations have MRI characteristics that allow segmentation of ischemic from nonischemic tissue. Moreover, MRI segmentation generates different zones within the lesion that may reflect heterogeneity of tissue damage.

Methods—A vector tissue signature model is presented that uses multiparametric MRI for segmentation and characterization of tissue. An objective (unsupervised) computer segmentation algorithm was incorporated into this model with the use of a modified version of the Iterative Self-Organizing Data Analysis Technique (ISODATA). The ability of the model to characterize ischemic tissue after permanent middle cerebral ischemia occlusion in the rat was tested. Multiparametric ISODATA measurements of the ischemic tissue were compared with quantitative histological characterization of the tissue from 4 hours to 1 week after stroke.

Results—The ISODATA segmentation of tissue identified a gradation of cerebral tissue damage at all time points after stroke. The histological scoring of ischemic tissue from 4 hours to 1 week after stroke on all the animals was significantly correlated with ISODATA segmentation (r=0.78, P<0.001; n=20) when a multiparametric (T2-, T1-, diffusion-weighted imaging) data set was used, less correlated (r=0.70, P<0.01; n=20) when a T2- and T1-weighted data set was used, and not correlated (r=-0.12, P>0.47; n=20) when only a diffusion-weighted imaging data set was used.

Conclusions—Our data indicate that an integrated set of MRI parameters can distinguish and stage ischemic tissue damage in an objective manner.

Key Words: cerebral ischemia, focal • computer-assisted image processing • diffusion imaging • magnetic resonance imaging • signal processing, computer assisted, ISODATA • stroke, acute • stroke classification • tissue signature

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