Random Walk Small Intestine Models for Virtual Patient Populations

Abstract

We develop the XCAT series of phantoms for medical imaging research. The phantoms model different individuals over various ages, heights, and weights, but a current drawback is they do not include small intestine variability. Each phantom has a small intestine derived from a common anatomical template due to the difficulty of fitting a regular tubular model to patient segmentations. Building upon previous work, we develop a software pipeline to add realistic variability in the phantoms by generating tubular small intestine surface models with random length and diameter fit within the constraints of a given intestine segmentation. The pipeline first alpha wraps a given segmentation into a 3D mesh. Using a random walk algorithm, a path is then constructed within the constraints of the surface mesh while avoiding both path and surface collisions given a set diameter, start point, and end point that bounds the random walk of the passage line. After generating the passage line, the program smooths the path by fitting a cubic spline curve to it. A cubic NURBS cylinder is lofted along the path to create an initial target model. The cylinder is then grown radially, avoiding self-intersection and bounded by the mesh surface, to achieve a user-defined volume. The pipeline was tested on 45 sets of patient CT data. From the results, we find we can generate variable mean passage lengths and mean diameters within realistic ranges found for the general population.