Analytical versus voxelized phantom representation for Monte Carlo simulation in radiological imaging

Monte Carlo simulations in nuclear medicine, with accurately modeled photon transport and high-quality random number generators, require precisely def ined and often detailed phantoms as an important component in the simulatio n process. Contemporary simulation models predominantly employ voxel-driven algorithms, but analytical models offer important advantages. We discuss t he implementation of ray-solid intersection algorithms in analytical superq uadric-based complex phantoms with additional speed-up rejection testing fo r use in nuclear medicine imaging simulations, and we make comparisons with voxelized counterparts. Comparisons are made with well-known cold rod:sphe re and anthropomorphic phantoms, For these complex phantoms, the analytical phantom representations are nominally several orders of magnitude smaller in memory requirements than are voxelized versions. Analytical phantoms fac ilitate constant distribution parameters. As a consequence of discretizing a continuous surface into finite bins, for example, time-dependent voxelize d phantoms can have difficulties preserving accurate volumes of a beating h eart. Although virtually no inaccuracy is associated with path calculations in analytical phantoms, the discretization can negatively impact the simul ation process and results. Discretization errors are apparent in reconstruc ted images of cold rod:sphere voxel-based phantoms because of a redistribut ion of the count densities in the simulated objects. These problems are ent irely avoided in analytical phantoms. Voxelized phantoms can accurately mod el detailed human shapes based on segmented computed tomography (CT) or mag netic resonance imaging (MRI) images, but analytical phantoms offer advanta ges in time and accuracy for evaluation and investigation of imaging physic s and reconstruction algorithms in a straightforward and efficient manner.