Creating connected representations of cortical gray matter for functional MRI visualization

Abstract

We describe a system that is being used to segment gray matter from magnetic resonance imaging (MRI) and to create connected cortical representations for functional MRI visualization (fMRI). The method exploits knowledge of the anatomy of the cortex and incorporates structural constraints into the segmentation. First, the white matter and cerebral spinal fluid (CSF) regions in the MR volume are segmented using a novel techniques of posterior anisotropic diffusion. Then, the user selects the cortical white matter component of interest, and its structure is verified by checking for cavities and handles. After this, a connected representation of the gray matter is created by a constrained growing-out from the white matter boundary. Because the connectivity is computed, the segmentation can be used as input to several methods of visualizing the spatial pattern of cortical activity within gray matter. In our case, the connected representation of gray matter is used to create a flattened representation of the cortex. Then, fMRI measurements are overlaid on the flattened representation, yielding a representation of the volumetric data within a single image. The software is freely available to the research community.

CRUISE: Cortical reconstruction using implicit surface evolution

“…A major shortcoming of this method is that its accuracy is limited to the voxel level, and thus the method is not well suited for finding the pial surface. The same shortcoming is shared by another volumetric cortical reconstruction method proposed by Teo et al (1997). In addition, the tissue segmentation methods in both approaches are far from optimal.…”

“…Many approaches have been proposed in the literature for the reconstruction of the cortex from MR brain images (Dale et al, 1999;Davatzikos and Bryan, 1996;Joshi et al, 1999;Kriegeskorte and Goebel, 2001;MacDonald et al, 2000;Mangin et al, 1995;Sandor and Leahy, 1997;Shattuck and Leahy, 2002;Teo et al, 1997;Zeng et al, 1999;Xu et al, 1999). These approaches differ in their ability to capture the convoluted cortical geometry, reconstruction accuracy, robustness against imaging artifacts, computation time, topological correctness, and self-intersection avoidance.…”

Dynamic Adaptation of Cooperative Agents for MRI Brain Scans Segmentation

“…Segmentation is a crucial step for successfully unfolding: poor unfolding maps are usually caused by small errors in the segmentation phase [6]. For unfolding, the respect of topological relationships, no cavities (a tissue A is completely surrounded by a tissue B) or self-intersections, is more important that the precise determination of the boundaries between white matter (WM) and gray matter (GM) or gray matter and cerebro-spinal fluid (CSF) [7]. How this could be automatically achieved in a multi-agent environment is the main focus of this paper.…”

“…The histogram plus a peak-finding algorithm determine the main modes present in the image, one for CSF, one for GM and one for WM, which are in conventional anatomical images (T1 weighted) ordered by gray intensity levels with highest intensity for WM (implicit knowledge). Prior probabilities and Bayes' rule can be used to maximize a posteriori probabilities [7]. Adaptability can be obtained via user interactions that refine preliminary results (additional knowledge).…”

“…Several methods have been already proposed for MRI brain scans segmentation in the context of flattened representation [7,[9][10][11][12]. Segmentation process requires 1) the insertion of a priori knowledge, 2) the ability of adaptation to take into account of local gray level variations and 3) the control of the entire process.…”