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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
Published on: September 25, 2019
Brain white matter hyperintensity lesion characterization in 3D T2 fluid-attenuated inversion recovery magnetic
Chih-Ying Gwo1, David C Zhu2,3, Rong Zhang4,5
1Department of Information Management, Chien Hsin University of Science and Technology, Taoyuan City, Taiwan.
Abstract:
Analyses of age-related white matter hyperintensity (WMH) lesions manifested in T2 fluid-attenuated inversion recovery (FLAIR) magnetic resonance images (MRI) have been mostly on understanding the size and location of the WMH lesions and rarely on the morphological characterization of the lesions. This work extends our prior analyses of the morphological characteristics and texture of WMH from 2D to 3D based on 3D T2 FLAIR images. 3D Zernike transformation was used to characterize WMH shape; a fuzzy logic method was used to characterize the lesion texture. We then clustered 3D WMH lesions into groups based on their 3D shape and texture features. A potential growth index (PGI) to assess dynamic changes in WMH lesions was developed based on the image texture features of the WMH lesion penumbra. WMH lesions with various sizes were segmented from brain images of 32 cognitively normal older adults. The WMH lesions were divided into two groups based on their size. Analyses of Variance (ANOVAs) showed significant differences in PGI among WMH shape clusters (P = 1.57 × 10-3 for small lesions; P = 3.14 × 10-2 for large lesions). Significant differences in PGI were also found among WMH texture group clusters (P = 1.79 × 10-6). In conclusion, we presented a novel approach to characterize the morphology of 3D WMH lesions and explored the potential to assess the dynamic morphological changes of WMH lesions using PGI.
Insights
This study introduces a novel 3D approach to analyze white matter hyperintensity (WMH) lesion morphology and texture using MRI. A potential growth index (PGI) was developed to assess dynamic changes in WMH, revealing significant differences based on lesion characteristics.
Area of Science:
- Neuroimaging
- Radiology
- Biomedical Engineering
Background:
- Age-related white matter hyperintensity (WMH) lesions on MRI are typically analyzed by size and location, with less focus on morphology.
- Previous research has primarily used 2D analyses, limiting a comprehensive understanding of WMH characteristics.
Purpose of the Study:
- To extend the analysis of WMH morphology and texture from 2D to 3D using 3D T2 FLAIR MRI.
- To develop and validate a potential growth index (PGI) for assessing dynamic changes in WMH lesions.
- To cluster WMH lesions based on 3D shape and texture features and analyze their relationship with PGI.
Main Methods:
- Utilized 3D Zernike transformation for WMH shape characterization and fuzzy logic for texture analysis.
- Segmented WMH lesions from 3D T2 FLAIR MRI scans of 32 cognitively normal older adults.
- Developed a PGI based on WMH lesion penumbra texture features and performed ANOVAs to compare PGI across lesion clusters.
Main Results:
- Significant differences in PGI were observed among WMH shape clusters for both small (P = 1.57 × 10⁻³) and large (P = 3.14 × 10⁻²) lesions.
- Highly significant differences in PGI were found among WMH texture group clusters (P = 1.79 × 10⁻⁶).
- The study successfully clustered 3D WMH lesions based on shape and texture, linking these features to potential growth.
Conclusions:
- A novel 3D approach for characterizing WMH lesion morphology and texture was presented.
- The developed PGI shows potential for assessing dynamic morphological changes in WMH lesions.
- Findings suggest that 3D morphological and textural features are important indicators of WMH lesion behavior.
