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Updated: Jul 18, 2026
A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia
Published on: September 16, 2017
Multiparametric iterative self-organizing data analysis of ischemic lesions using pre- or post-Gd T1 MRI
Hamid Soltanian-Zadeh1, Hassan Bagher-Ebadian, James R Ewing
1Image Analysis Laboratory, Department of Radiology, Henry Ford Health System, Detroit, MI 48202, USA. hamids@rad.hfh.edu
Background:
The purpose of this work was to evaluate effects of Gd-diethylenetriaminepentacetic acid (DTPA) injection on T(1)-weighted images of stroke and lesion segmentation and characterization results generated by our multiparametric iterative self-organizing data (ISODATA) method. The post-Gd image incorporates vasculature information into the analysis.
Methods:
Either a pre-Gd T(1)-weighted image (T1WI) or a post-Gd T1WI was used along with diffusion-, T(2)- and proton-density-weighted images in the analysis. ISODATA is a data-driven method that segments and characterizes tissue damage in stroke using multiparametric MRI.
Results:
Experimental results in both animal and human studies showed that the use of post-Gd T1WI modified the segmentation and characterization results on the periphery of the lesion. The peripheral region that changes with Gd-DTPA has a higher permeability compared to the rest of the lesion. Either of the data sets (including pre- or post-Gd T1WI) was used to estimate the tissue recovery and generated consistent results.
Conclusions:
This study shows that our multiparametric ISODATA approach consistently identifies and characterizes the core of the ischemic lesion. It also shows that the inclusion of post-Gd T1WI results in the segmentation and characterization of the lesion periphery if it has a higher permeability compared to the rest of the lesion. Finally, it confirms that the multiparametric ISODATA MRI characterizes tissue damage and recovery in stroke.
