Multiparametric iterative self-organizing data analysis of ischemic lesions using pre- or post-Gd T1 MRI.

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.