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

Area of Science:

• Medical Imaging Physics
• Computational Science

Background:

• Monte Carlo simulations in nuclear medicine require detailed phantoms for accurate modeling.
• Voxel-driven algorithms are common, but analytical models present distinct benefits.

Purpose of the Study:

• To implement and evaluate analytical superquadric-based phantoms using ray-solid intersection algorithms for nuclear medicine simulations.
• To compare the performance and accuracy of analytical phantoms against their voxelized counterparts.

Main Methods:

• Implementation of ray-solid intersection algorithms for analytical superquadric phantoms.
• Inclusion of speed-up rejection testing for enhanced efficiency.
• Comparative analysis with voxelized cold rod:sphere and anthropomorphic phantoms.

Main Results:

• Analytical phantoms exhibit significantly lower memory requirements (orders of magnitude) compared to voxelized versions.
• Analytical phantoms avoid discretization errors, preserving accurate volumes and count densities, unlike voxelized phantoms.
• Path calculations in analytical phantoms are virtually free of inaccuracy, leading to improved simulation fidelity.

Conclusions:

• Analytical phantoms provide superior accuracy and efficiency for nuclear medicine imaging simulations.
• They overcome limitations of voxelized phantoms, particularly in preserving dynamic volumetric data and avoiding reconstruction artifacts.
• Analytical models are advantageous for evaluating imaging physics and reconstruction algorithms.