Project: Brain Lesion Modeling and Segmentation

Quantification, analysis, and display of brain pathology such as white- matter lesions, as observed in MRI, is important for diagnosis, monitoring of disease progression, improved understanding of pathological processes, and developing new therapies. Utah Center for Neuroimage Analysis develops new methodology for extraction of brain lesions from volumetric MRI scans and for characterization of lesion patterns over time.The images show white-matter lesions (yellow) displayed with ventricles (blue) and transparent brain surface in a patient with an autoimmune disease (lupus). Lesions in white matter and possible correlations with cognitive deficits are also studied in patients with multiple sclerosis (MS), chronic depression, Alzheimer’s disease (AD), and in older persons.

Segmentation of a lupus case with large lesions.

Segmentation of a 3T lupus case with small lesions.

In addition to the identification of the location and shape of lesions in 3D, we are interested in analyzing the longitudinal series of brain MRI showing lesions. For this purpose, we have developed a method for estimating a physical model for lesion formation. The model that we use is an approximation using a reaction-diffusion process that is based on expected diffusion properties (as observed through DTI). This approach gives a richer parametrization of lesion changes in addition to volume and location, as the model estimation provides descriptions of growth and spread for individual lesions. In the future, we plan to incorporate this approach for analyzing lesion MRI of a subject over time by characterizing the change patterns through the physical model parameters.

An example of the lesion model formation estimation result. Starting from an initial guess of the reaction-diffusion process, the method estimates a model that best fits the observed data. Left: initial guess. Center: final estimate. Right: observed patient data. Top row: the T2 intensities, bottom row: lesion probabilities.

Publications:

• Prastawa M, Bullitt E, Gerig G, Simulation of brain tumors in MR images, Medical Image Analysis 13 (2009), pp. 297-311, PMID: 19119055

• Marcel Prastawa and Guido Gerig, Brain Lesion Segmentation through Physical Model Estimation, In Lecture Notes in Computer Science, Vol. 5358, pp. 562--571. 2008.

• Marcel Prastawa, John H. Gilmore, Weili Lin, Guido Gerig, Automatic Segmentation of MR Images of the Developing Newborn Brain, Medical Image Analysis (MedIA). Vol 9, October 2005, pages 457-466

• Prastawa M., Gerig G. Brain Lesion Segmentation through Physical Model Estimation. Proceedings of the 4th International Symposium on Visual Computing 2008, LNCS 5358, pp. 562-571.

• Prastawa M., Gerig G. Automatic MS Lesion Segmentation by Outlier Detection and Information Theoretic Region Partitioning. Int Conf Med Image Comput Comput Assist Interv. 2008;11(WS). Proceedings of the Grand Challenge II Workshop.

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Related Research Themes

• Diffusion Analysis
• Segmentation
• Longitudinal Analysyis

PIs

• Guido Gerig
• Marcel Prastawa