Computer-based simulation tools for high-resolution x-ray CT research
W. Paul Segars, M. Mahesh, T.J. Beck, E.C. Frey, and Benjamin M. W. Tsui
We develop and validate a set of computer-based simulation tools for use in high-resolution x-ray CT research. The tools are based on the 4D NURBS-based cardiac-torso (NCAT) phantom, a computer model of the human anatomy and physiology developed in our laboratory.
Fig. 1. Simulated chest x-ray CT images from the 4D NCAT phantom.
METHODS AND MATERIALS:
The organ models in the 4D NCAT are based on non-uniform rational b-spline (NURBS) surfaces, which are widely used in computer graphics. Unlike current phantoms in CT based on simple mathematical primitives, the 4D NCAT provides an accurate representation of the human anatomy and has the advantage, due to its design, that its organ shapes can be changed to realistically model anatomical variations and patient motion. A disadvantage to the spline basis of the NCAT, however, is that the line integrals through the phantom cannot be calculated analytically. They have to be calculated using iterative procedures; therefore, the calculation of CT projections is much slower than for simpler mathematical phantoms. To overcome this limitation, we used efficient ray tracing techniques from computer graphics, to develop a fast analytic projection algorithm (including scatter and quantum noise) to accurately calculate CT projections directly from the surface definition of the NCAT phantom given parameters defining the CT scanner and geometry. The projection data are reconstructed into CT images using algorithms developed in our laboratory. We validate our CT simulation tools and methods through a series of direct comparisons with data obtained experimentally using existing, simple physical phantoms at different doses and using different x-ray energy spectra.
Using the simulation tools developed in this work, high-resolution, patient quality CT images can be simulated from the 4D NCAT within a reasonable amount of time, Fig. 1. Compared to analytical methods, the projection algorithm is only approximately 3 times slower using a single computer processor. The projection algorithm can be run in parallel, spreading the projection angles over many processors, to generate data much faster. For each comparison to experimental data, the first-order simulations were found to produce comparable results (<12%). We reason that since the simulations produced equivalent results using simple test objects, they should be able to do the same in more anatomically realistic conditions.
We conclude that, with the ability to provide realistic simulated CT image data close to that from actual patients, the efficient simulation tools developed in this work will have applications in a broad range of CT imaging research. As x-ray CT evolves into many new applications and gains wider use, they can be used to develop image acquisition strategies, image processing and reconstruction methods, and image visualization and interpretation techniques. Also, the tools provide the necessary foundation to optimize clinical CT applications so as to obtain the highest possible image quality with the minimum possible radiation dose to the patient, an area of research that is becoming more significant with the proliferation of CT protocols.
NIH Research Grant RO1EB001838