While great advances are made toward making highly realistic, surface models of the human
anatomy, very little has been done to fill these bounded surfaces with models of anatomical
texture. We propose a method whereby realistic anatomically-based computed tomography (CT)
texture can be incorporated into voxelized versions of the 4D extended cardiac-torso (XCAT)
phantom. Our source of texture comes from patient CT scans from the Duke CT imaging
database. These image-sets were de-noised using anisotropic diffusion. Two organs were
selected from which texture was obtained, liver and lungs. From each organ, multiple regions of
interest (ROIs) were taken and tiled side-by-side to create a larger image. Textures for the liver
and lungs were extrapolated using ImageQuilting, based on the tiled images. Next, a NURBSbased
XCAT phantom was voxelized at the same resolution as the textures. The texture was then
placed in the voxelized phantoms. Finally, CT simulations of the phantoms with and without the
textures were compared against each other, using the power spectral density. This work shows
that there is a way whereby the XCAT phantoms can be textured to give more realistic
appearance in CT simulations. It is anticipated that this method would find great use in making
projections of the XCAT phantom look more realistic and allow for the phantoms to not only be
utilized in dosimetrical evaluations, but in image quality studies as well.
KEYWORDS: 3D modeling, Motion models, Computed tomography, Image segmentation, Medical imaging, Monte Carlo methods, Medical research, Imaging devices, Computer simulations, Data modeling
Computerized phantoms are finding an increasingly important role in medical imaging research. With the ability to
simulate various imaging conditions, they offer a practical means with which to quantitatively evaluate and improve
imaging devices and techniques. This is especially true in CT due to the high radiation levels involved with it. Despite
their utility, due to the time required to develop them, only a handful of computational models currently exist of varying
detail. Most phantoms available are limited to 3D and not capable of modeling patient motion. We have previously
developed a technique to rapidly create highly detailed 4D extended cardiac-torso (XCAT) phantoms based on patient
CT data [1].
In this study, we utilize this technique to generate 58 new adult XCAT phantoms to be added to our growing library of
virtual patients available for imaging research. These computerized patients provide a valuable tool for investigating
imaging devices and the effects of anatomy and motion in imaging. They also provide the essential tools to investigate
patient-specific dose estimation and optimization for adults undergoing CT procedures.
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