Faculty Bibliography

We present a fully automatic mixture-based algorithm for segmentation of brain tissues (white and gray matters - WM and GM), cerebral spinal fluid (CSF) and brain lesion to quantitatively analyze multiple sclerosis. The method performs intensity-based tissue classification using multispectral magnetic resonance (MR) images based on a stochastic model. With the existence of white Gaussian noise and spatially invariant blurring in acquired MR images, a Karhunen-Loeve (K-L) domain Wiener filter is applied for an accurate noise reduction and resolution restoration on blurred and noisy images to minimize the partial volume effect (PVE), which is a major limiting factor for the quantitative analysis. Following that, we utilize a Markov random field Gibbs model to integrate the local spatial information into the established expectation-maximization model-fitting algorithm. Each voxel is then classified by a mixture-based maximum a posterior (MAP) criterion, indicating its probabilities of belonging to each class, i.e., each voxel is labeled as a mixel with different tissue percentages, leading to further minimization of the PVE. The volumes of WM, GM and CSF are extracted from the mixture-based segmentation and the corresponding brain atrophies are computed. In this study, we have investigated the accuracy and repeatability of the algorithm with inclusion of noise analysis and point spread function for image resolution enhancement. Experimental results on both phantom and healthy volunteer studies are presented.

We used quantitative magnetic resonance (MR) spectroscopic imaging with T1-based image segmentation to evaluate the subtypes of multiple sclerosis (MS) (eight patients each group of relapsing-remitting [RR], secondary progressive [SP] and primary progressive [PP]). There was no significant difference in age between the PP group with the RP, SP or control group. We found that the metabolite ratio of choline/NA from the periventricular white matter region was not significantly different between the RR and SP groups. Using an ANOVA, the ratios of periventricular choline/NA or creatine/NA of these combined groups were significantly higher than the PP and control groups. Quantification of these data suggest that the major cause of the elevation of these parameters is due to an increase in choline and creatine in the RR group while NA is decreased in the SP group. Thus, early PP disease appears to be relatively intact with respect to neuronal loss.