Realistic CT Simulation Using the 4D XCAT Phantom

The authors develop a unique CT simulation tool based on the 4D extended cardiac-torso (XCAT) phantom, a whole-body computer model of the human anatomy and physiology based on NURBS surfaces. Unlike current phantoms in CT based on simple mathematical primitives, the 4D XCAT provides an accurate representation of the complex 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 NURBS basis of the XCAT, however, is that the mathematical complexity of the surfaces makes the calculation of line integrals through the phantom difficult. 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, the authors used efficient ray tracing techniques from computer graphics, to develop a fast analytic projection algorithm to accurately calculate CT projections directly from the surface definition of the XCAT phantom given parameters defining the CT scanner and geometry. Using this tool, realistic high-resolution 3D and 4D projection images can be simulated and reconstructed from the XCAT within a reasonable amount of time. In comparison with other simulators with geometrically defined organs, the XCAT-based algorithm was found to be only three times slower in generating a projection data set of the same anatomical structures using a single 3.2 GHz processor. To overcome this decrease in speed would, therefore, only require running the projection algorithm in parallel over three processors. With the ever decreasing cost of computers and the rise of faster processors and multi-processor systems and clusters, this slowdown is basically inconsequential, especially given the vast improvement the XCAT offers in terms of realism and the ability to generate 3D and 4D data from anatomically diverse patients. As such, the authors conclude that the efficient XCAT-based CT simulator developed in this work will have applications in a broad range of CT imaging research.