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 with Brain Multiple Sclerosis (MS)
- 6 with SubCortical Ischemic Vascular Dementia (SIVD)
- 6 with White Matter Lacunar Infarcts (LI)
In routine MRI these pathologies may be almost indistinguishable.
The 3D-tissue segmentation technique used in this study was based on three input MR images (T1, T2-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 T2-weighted MR images.
The proposed methodology warrants further clinical investigation and evaluation in a large patient population.