FastCAT: Fast Cone Beam CT (CBCT) Simulation

TL;DR

FastCAT is a GPU-accelerated CBCT simulation tool that uses pre-calculated Monte Carlo data to significantly reduce computation time while maintaining high fidelity in imaging metrics, enabling rapid and accurate CBCT imaging simulations.

Contribution

FastCAT introduces a novel GPU-based CBCT simulation method utilizing pre-calculated scatter and detector response functions for rapid, high-fidelity imaging simulations.

Findings

FastCAT achieves simulation times of 61 seconds for large volumes.

Simulation results closely match measurements and detailed MC simulations.

FastCAT reduces computation time from years to minutes without sacrificing accuracy.

Abstract

The fastCAT application uses pre-calculated Monte Carlo (MC) CBCT phantom-specific scatter and detector response functions to reduce simulation time for megavoltage (MV) and kilovoltage (kV) CBCT imaging. Pre-calculated x-ray beam energy spectra, detector optical spread functions and energy deposition, and phantom scatter kernels are combined with GPU raytracing to produce CBCT volumes. MV x-ray beam spectra are simulated with EGSnrc for 2.5 and 6 MeV electron beams incident on a variety of target materials and kV x-ray beam spectra are calculated analytically for an x-ray tube with a tungsten anode. Detectors were modelled in Geant4 extended by Topas and included optical transport in the scintillators. Two MV detectors were modelled, a standard Varian AS1200 GOS detector and a novel CWO high detective quantum efficiency detector. A kV CsI detector was also modelled. Energy dependent scatter kernels were created in Topas for two 16-cm diameter phantoms: A Catphan 515 contrast phantom and an anthropomorphic head phantom. The Catphan phantom contained inserts of 1-5 mm in diameter of six different tissue types. FastCAT simulations retain high fidelity to measurements and MC simulations: MTF curves were within 3.5% and 1.2% of measured values for the CWO and GOS detectors, respectively. HU values and CNR in a fastCAT Catphan 515 simulation were seen to be within 95 % confidence intervals of an equivalent MC simulation for all of the tissues with root mean squared errors less than 16 HU and 1.6 in HU values and CNR comparisons, respectively. A fastCAT simulation of the Catphan 515 module with an image size of 1024x1024x10 voxels took 61 seconds on a GPU while the equivalent Topas MC was estimated to take more than 0.3 CPU years. The fastCAT application can be found at https://github.com/jerichooconnell/fastCATs.git.