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Updated: Jun 28, 2026
Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
Published on: April 14, 2014
Computational atlases of severity of white matter lesions in elderly subjects with MRI
Stathis Hadjidemetriou1, Peter Lorenzen, Norbert Schuff
1NCIRE/VA UCSF, 4150 Clement St., San Francisco, CA 94121, USA.
Abstract:
MRI of cerebral white matter may show regions of signal abnormalities. These changes may be associated with hypertension, inflammation, or ischemia, as well as altered brain function. The goal of this work has been to construct computational atlases of white matter lesions that represent both their severity as well as the frequency of their occurrence in a population to achieve a better classification of white matter disease. An atlas is computed with a pipeline that uses 4T FLAIR and 4T T1-weighted (T1w) brain images of a group of subjects. The processing steps include intensity correction, lesion extraction, intra-subject FLAIR to T1w rigid registration, and seamless replacement of lesions in T1w images with synthetic white matter texture. Subsequently, the T1w images and lesion images of different subjects are registered non-rigidly to the same space. The decrease in T1w intensities is used to obtain severity information. Atlases were constructed for two groups of subjects, elderly normal controls or with mild cognitive impairment, and subjects with cerebrovascular disease. The lesion severities of the two groups have a significant statistical difference with the severity in the atlas of cerebrovascular disease being higher.
Insights
This study developed computational atlases to classify white matter diseases by analyzing MRI scans. The new method revealed significantly higher lesion severity in patients with cerebrovascular disease compared to controls.
Area of Science:
- Neuroimaging
- Computational anatomy
- Neurology
Background:
- Cerebral white matter abnormalities on MRI can indicate hypertension, inflammation, or ischemia, impacting brain function.
- Accurate classification of white matter disease is crucial for understanding its progression and impact.
Purpose of the Study:
- To construct computational atlases of white matter lesions, quantifying both severity and frequency.
- To improve the classification of white matter diseases using advanced imaging analysis.
Main Methods:
- Utilized a pipeline involving 4T FLAIR and 4T T1-weighted (T1w) MRI images.
- Processed images through intensity correction, lesion extraction, registration, and synthetic texture replacement.
- Constructed population-specific atlases for elderly controls/mild cognitive impairment and cerebrovascular disease groups.
Main Results:
- Developed computational atlases representing white matter lesion severity and occurrence.
- Demonstrated a significant statistical difference in lesion severity between the two subject groups.
- Observed higher lesion severity in the atlas derived from subjects with cerebrovascular disease.
Conclusions:
- Computational atlases provide a robust method for quantifying white matter lesion severity.
- The findings highlight distinct differences in white matter pathology between cerebrovascular disease and healthy/mild cognitive impairment groups.
- This approach aids in better classification and understanding of white matter diseases.
