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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.

Objective: To determine changes in clinical manifestations and cerebral blood flow (CBF) before and after administration of ECT to patients with catatonia due to schizophrenia or mood disorders., Methods: A sample of nine patients who met DSM-IV criteria for catatonia was studied. Patients received between 5 and 15 sessions (thrice per week) of ECT. Severity of catatonia was measured with the Modified Rogers Scale (MRS). Changes in CBF were measured with a brain single positron emission computer tomography (SPECT) that was performed 1 week before the first ECT and 1 week after the last ECT., Results: Catatonia was due to schizophrenia in five patients and mood disorders in four patients. There were no significant clinical and brain SPECT differences between these two groups before treatment. Pre- vs. post-ECT comparisons showed significant reduction of catatonic symptoms in both groups. However, patients with mood disorders needed less ECT sessions and showed greater clinical improvement. Brain SPECT showed significant increase in CBF in parietal, temporal, and occipital regions in patients with mood disorder and no significant changes in patients with schizophrenia., Conclusions: These results support the efficacy of ECT for treatment of catatonic patients, especially secondary to mood disorders, which seem to be correlated with improvements in CBF.