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Increased differentiation of intracranial white matter lesions by multispectral 3D-tissue segmentation: preliminary
F B Mohamed1, S Vinitski, C F Gonzalez
1Department of Radiology, MCP/Hahnemann University, Philadelphia, PA, USA. feroze@drexel.edu
Abstract:
MRI is a very sensitive imaging modality, however with relatively low specificity. The aim of this work was to determine the potential of image post-processing using 3D-tissue segmentation technique for identification and quantitative characterization of intracranial lesions primarily in the white matter. Forty subjects participated in this study: 28 patients with brain multiple sclerosis (MS), 6 patients with subcortical ischemic vascular dementia (SIVD), and 6 patients with lacunar white matter infarcts (LI). In routine MR imaging these pathologies may be almost indistinguishable. The 3D-tissue segmentation technique used in this study was based on three input MR images (T(1), T(2)-weighted, and proton density). A modified k-Nearest-Neighbor (k-NN) algorithm optimized for maximum computation speed and high quality segmentation was utilized. In MS lesions, two very distinct subsets were classified using this procedure. Based on the results of segmentation one subset probably represent gliosis, and the other edema and demyelination. In SIVD, the segmented images demonstrated homogeneity, which differentiates SIVD from the heterogeneity observed in MS. This homogeneity was in agreement with the general histological findings. The LI changes pathophysiologically from subacute to chronic. The segmented images closely correlated with these changes, showing a central area of necrosis with cyst formation surrounded by an area that appears like reactive gliosis. In the chronic state, the cyst intensity was similar to that of CSF, while in the subacute stage, the peripheral rim was more prominent. Regional brain lesion load were also obtained on one MS patient to demonstrate the potential use of this technique for lesion load measurements. The majority of lesions were identified in the parietal and occipital lobes. The follow-up study showed qualitatively and quantitatively that the calculated MS load increase was associated with brain atrophy represented by an increase in CSF volume as well as decrease in "normal" brain tissue volumes. Importantly, these results were consistent with the patient's clinical evolution of the disease after a six-month period. In conclusion, these results show there is a potential application for a 3D tissue segmentation technique to characterize white matter lesions with similar intensities on T(2)-weighted MR images. The proposed methodology warrants further clinical investigation and evaluation in a large patient population.
Insights
This study shows 3D-tissue segmentation can differentiate brain lesions like multiple sclerosis (MS) and vascular dementia. This advanced MRI post-processing technique aids in characterizing white matter pathologies.
Area of Science:
- Neuroimaging
- Medical Image Analysis
- Neurology
Background:
- Magnetic Resonance Imaging (MRI) offers high sensitivity but limited specificity for intracranial lesions.
- Distinguishing between pathologies like multiple sclerosis (MS), subcortical ischemic vascular dementia (SIVD), and lacunar infarcts (LI) can be challenging with routine MRI.
- Advanced image post-processing techniques are needed for precise characterization of white matter lesions.
Purpose of the Study:
- To evaluate the potential of 3D-tissue segmentation for identifying and quantitatively characterizing intracranial white matter lesions.
- To differentiate between MS, SIVD, and LI using a modified k-Nearest-Neighbor (k-NN) algorithm.
- To assess the technique's utility in measuring lesion load and its correlation with disease progression and brain atrophy.
Main Methods:
- Utilized a 3D-tissue segmentation technique based on T(1)-weighted, T(2)-weighted, and proton density MRI sequences.
- Employed a modified k-Nearest-Neighbor (k-NN) algorithm for rapid and high-quality image segmentation.
- Analyzed data from 40 subjects: 28 with MS, 6 with SIVD, and 6 with LI.
Main Results:
- The segmentation technique successfully classified MS lesions into subsets likely representing gliosis and edema/demyelination.
- SIVD lesions showed homogeneity, distinguishing them from heterogeneous MS lesions, consistent with histological findings.
- Lacunar infarcts (LI) segmentation correlated with pathological changes, differentiating acute and chronic stages and identifying necrosis and gliosis.
- Lesion load measurement in an MS patient demonstrated correlation with clinical evolution, brain atrophy, and increased cerebrospinal fluid (CSF) volume over six months.
Conclusions:
- 3D-tissue segmentation is a promising technique for characterizing white matter lesions with similar signal intensities on T(2)-weighted MRI.
- The method aids in differentiating various white matter pathologies and quantifying lesion burden.
- Further clinical investigation in larger patient cohorts is warranted to validate this advanced MRI post-processing approach.