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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
Published on: September 25, 2019
Evaluation of voxel-based group-level analysis of diffusion tensor images using simulated brain lesions
Jaana Hiltunen1, Mika Seppä, Riitta Hari
1Brain Research Unit, Low Temperature Laboratory, Aalto University School of Science, P.O. Box 15100, 00076-AALTO, Finland. jaana@neuro.hut.fi
Abstract:
We simulated brain lesions in mean diffusivity (MD) and fractional anisotropy (FA) images of healthy subjects to evaluate the performance of voxel-based analysis (VBA) with SPM2. We increased MD and decreased FA, simulating the most typical abnormalities in brain pathologies, in the superior longitudinal fasciculus (SLF), corticospinal tract (CST), and corpus callosum (CC). Lesion sizes varied from 10 to 400 voxels (10.5 mm³ each) and intensity changes from 10 to 100%. The VBA contained eddy current correction, spatial normalization, smoothing, and statistical analysis. The preprocessing steps changed the intensities of MD and FA lesions from the original values, and many lesions remained undetected. The detection thresholds varied between the three brain areas, and between MD and FA images. Although spatial smoothing often improved the sensitivity, it also markedly enlarged the estimated lesion sizes. Since conventional VBA preprocessing significantly affected the outcome and sensitivity of the method itself, the impact of analysis steps should be verified and considered before interpreting the findings. Our results provide insight into the sizes and intensity changes of lesions that can be detected with VBA applied to diffusion tensor imaging (DTI) data.
Insights
Voxel-based analysis (VBA) of diffusion tensor imaging (DTI) data can miss simulated brain lesions. Preprocessing steps significantly impact lesion detection sensitivity and size estimation, requiring careful consideration during interpretation.
Area of Science:
- Neuroimaging
- Diffusion Tensor Imaging (DTI)
- Voxel-Based Analysis (VBA)
Background:
- Diffusion Tensor Imaging (DTI) is crucial for assessing white matter integrity.
- Voxel-Based Analysis (VBA) is commonly used with DTI data to detect abnormalities.
- Understanding VBA performance in detecting simulated lesions is essential for accurate clinical interpretation.
Purpose of the Study:
- To evaluate the performance of Voxel-Based Analysis (VBA) using SPM2 for detecting simulated brain lesions in Diffusion Tensor Imaging (DTI) data.
- To assess how lesion size and intensity changes affect detection rates.
- To investigate the impact of standard preprocessing steps on lesion detection.
Main Methods:
- Simulated brain lesions with varying sizes (10-400 voxels) and intensity changes (10-100%) in mean diffusivity (MD) and fractional anisotropy (FA) maps.
- Lesions were introduced in the superior longitudinal fasciculus (SLF), corticospinal tract (CST), and corpus callosum (CC).
- Standard VBA preprocessing pipeline including eddy current correction, spatial normalization, and smoothing was applied.
Main Results:
- Preprocessing steps altered lesion intensities, leading to many simulated lesions remaining undetected.
- Detection thresholds varied significantly across different brain regions and between MD and FA images.
- Spatial smoothing improved sensitivity but also substantially enlarged the estimated lesion sizes.
Conclusions:
- Conventional VBA preprocessing significantly influences the sensitivity and outcome of lesion detection in DTI data.
- The impact of analysis steps must be verified before interpreting findings from VBA of DTI.
- This study provides insights into detectable lesion sizes and intensity changes using VBA on DTI data.
