A GPU Tool for Efficient, Accurate, and Realistic Simulation of Cone Beam CT Projections
Xun Jia, Hao Yan, Laura Cervino, Michael Folkerts, Steve B. Jiang

TL;DR
This paper introduces gDRR, a GPU-based tool that efficiently generates accurate, realistic CBCT projection images by combining ray-tracing, Monte Carlo simulation, and noise calibration, enabling improved research and development.
Contribution
The work presents a novel GPU-accelerated package that accurately simulates CBCT projections including primary, scatter, and noise components under clinical conditions.
Findings
Primary signal computation takes 1.2-2.3 seconds.
Monte Carlo scatter simulation agrees within 3.8% with EGSnrc.
Reconstructed images from simulated projections match real scanner noise levels.
Abstract
Simulation of x-ray projection images plays an important role in cone beam CT (CBCT) related research projects. A projection image contains primary signal, scatter signal, and noise. It is computationally demanding to perform accurate and realistic computations for all of these components. In this work, we develop a package on GPU, called gDRR, for the accurate and efficient computations of x-ray projection images in CBCT under clinically realistic conditions. The primary signal is computed by a tri-linear ray-tracing algorithm. A Monte Carlo (MC) simulation is then performed, yielding the primary signal and the scatter signal, both with noise. A denoising process is applied to obtain a smooth scatter signal. The noise component is then obtained by combining the difference between the MC primary and the ray-tracing primary signals, and the difference between the MC simulated scatter and…
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