Due to histological evidence of the fundamental role of the cerebral vessels in white matter abnormalities, recently there has been an increased interest in analyzing the relationship between brain white matter lesions in multiple sclerosis (MS) and brain vasculature. We developed a method for visualization and measurement of geometrical relationships between MS lesions and the brain vessels imaged with magnetic resonance (MR) imaging techniques. Using MR images we create surface models of lesions and vessels that constitute a base for quantitative analysis. In this work we analyze correlation between basic lesion geometrical characteristics and two features: 1) distances to vessels, and 2) vessel caliber. For the former, we compute a distance map from the vessel structure, such that each voxel stores its distance vector to the closest vessel. This allows the measurements of Euclidean distances to the closest vessels. For the latter, we compute a radius map in which each voxel stores the radius of its closest vessel. It is used to measure distribution of lesions with respect to the vessel caliber. We compute and analyze relations between the basic geometrical characteristics of lesions and the closest vessels locations and calibers. To demonstrate the feasibility of the developed technique we present results from the study of 3 MS cases.
A significant problem in 3D reconstruction of biological tissue from histological material is alignment of the individual sections. We are developing a method to determine the surface of the tissue prior to cryosectioning and then utilize that information to guide registration. Toward that end, we have developed a structured light techniuqe for imaging frozen rat brains. The imaging approach relies on a novel coding scheme for the projected light which is based on 2D perfect submaps. Perhaps submaps are r by v c-ary arrays in which every n by m c-ary submatrix is unique. This coding scheme offers two major advantages over previous structured light patterns critical in the present application. It permits rapid image capture and, because each subwindow is unique, is robust in the presence of partial occlusion. To examine the accuracy of this technique, we compare the points mapped using it to the surface produced by block-face imaging. In the later approach, the tissue block is imaged prior to collecting each of the tissue sections. Since the block can be accurately repositioned after each cutting stroke, reconstruction of the surface from the block-face images is straightforward.