Optimized statistical modeling of MS lesions as MRI voxel outliers for monitoring the effect of drug therapy

This paper presents the results of applying the modified deterministic annealing (DA) algorithm to simulated and clinical magnetic resonance (MR) brain data with multiple sclerosis (MS) lesions. Modified deterministic annealing algorithm is a very efficient segmentation algorithm for isolating MS lesions in the MR images when utilizing all the information contained in all modalities. To fully utilize the information contained in all the modalities, vector segmentation is carried out instead of unimodal segmentation. The vectors to be clustered are formed by multi-modal MR brain data. Through some arithmetic manipulations synthesized image data can be obtained which greatly alleviate the effect of noise and intensity inhomogeneity. Isolated multiple sclerosis lesions are outliers to the brain tissues. Even with noise level up to 7% the MS MR brain data can still be satisfactorily segmented. This method does not need a prior model, and is conceptually very simple. It delineates not only large lesions but small ones as well. The whole process is completely automated without any intervention by an operator, which can be a very promising tool for MS follow-up studies. Comparison between the segmentation results from the simulated MS brain data and from the clinical MS brain data shows that with the current high quality MRI facilities, images with noise above 3% and intensity inhomogeneity above 20% will usually not be produced. Segmentation results for the clinical data are much better and easier to obtain than the simulated noisy data. To get even better results for the MS lesions, inverse problem techniques have to be applied. Noise model and intensity inhomogeneity model have to be established and improved using the given MRI data during iteration.