Reconstruction and visualization of model-based volume representations

In modern medical CT, the primary source of data is a set of X-ray projections acquired around the object, which are then used to reconstruct a discrete regular grid of sample points. Conventional volume rendering methods use this reconstructed regular grid to estimate unknown off-grid values via interpolation. However, these interpolated values may not match the values that would have been generated had they been reconstructed directly with CT. The consequence can be simple blurring, but also the omission of fine object detail which usually contains precious information. To avoid these problems, in the method we propose, instead of reconstructing a lattice of volume sample points, we derive a highfidelity object model directly from the reconstruction process, fitting a localized object model to the acquired raw data within tight tolerances. This model can then be easily evaluated both for slice-based viewing as well as in GPU 3D volume rendering, offering excellent detail preservation in zooming operations. Furthermore, the model-driven representation also supports high-precision analytical ray casting.