Feature-based statistical analysis of structural MR data for automatic detection of focal cortical dysplastic lesions

We present a framework for automatic detection of focal cortical dysplastic (FCD) lesions from MR images of human brain. Our method extends, and improves the lesion detection specificity of a previously published voxel-based technique using cortical thickness and signal gradient as discriminating features of FCD lesions. In absence of any prior anatomical hypothesis regarding the spatial location of the lesion, the method examines each intracerebral voxel individually and simultaneously, and constructs a statistical parametric map indicating evidence against a null hypothesis of no effect in the patient versus a normal control group. Upon interrogation of the statistical map with an optimally selected threshold, the voxels demonstrating the improbability of the null hypothesis are reported as lesions. The method correctly detects 5 out of the 10 cases with a very high significance. The cases we did not detect were in deep gray matter regions, where the variance in the feature maps was high, decreasing the significance of the effect.

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