Monte Carlo study of the effects of system geometry and antiscatter grids on cone-beam CT scatter distributions

Purpose: The proliferation of cone-beam CT (CBCT) has created interest in performance optimization,with x-ray scatter identifie among the main limitations to image quality. CBCT often contends with elevated scatter, but the wide variety of imaging geometry in different CBCT configuration suggests that not all configuration are affected to the same extent. Graphics processing unit (GPU) accelerated Monte Carlo (MC) simulations are employed over a range of imaging geometries to elucidate the factors governing scatter characteristics, effica y of antiscatter grids, guide system design, and augment development of scatter correction. Methods: A MC x-ray simulator implemented on GPU was accelerated by inclusion of variance reduction techniques (interaction splitting, forced scattering, and forced detection) and extended to include x-ray spectra and analytical models of antiscatter grids and flat-pane detectors. The simulator was applied to small animal (SA), musculoskeletal (MSK) extremity, otolaryngology (Head), breast, interventional C-arm, and on-board (kilovoltage) linear accelerator (Linac) imaging, with an axis-todetector distance (ADD) of 5, 12, 22, 32, 60, and 50 cm, respectively. Each configuratio was modeled with and without an antiscatter grid and with (i) an elliptical cylinder varying 70–280 mm in major axis; and (ii) digital murine and anthropomorphic models. The effects of scatter were evaluated in terms of the angular distribution of scatter incident upon the detector, scatter-to-primary ratio (SPR), artifact magnitude, contrast, contrast-to-noise ratio (CNR), and visual assessment. Results: Variance reduction yielded improvements in MC simulation efficien y ranging from ∼17-fold (for SA CBCT) to ∼35-fold (for Head and C-arm), with the most significan acceleration due to interaction splitting (∼6 to ∼10-fold increase in efficien y). The benefi of a more extended geometry was evident by virtue of a larger air gap—e.g., for a 16 cm diameter object, the SPR reduced from 1.5 for ADD = 12 cm (MSK geometry) to 1.1 for ADD = 22 cm (Head) and to 0.5 for ADD = 60 cm (C-arm). Grid efficien y was higher for configuration with shorter air gap due to a broader angular distribution of scattered photons—e.g., scatter rejection factor ∼0.8 for MSK geometry versus ∼0.65 for C-arm. Grids reduced cupping for all configuration but had limited improvement on scatterinduced streaks and resulted in a loss of CNR for the SA, Breast, and C-arm. Relative contribution of forward-directed scatter increased with a grid (e.g., Rayleigh scatter fraction increasing from ∼0.15 without a grid to ∼0.25 with a grid for the MSK configuration) resulting in scatter distributions with greater spatial variation (the form of which depended on grid orientation). Conclusions: A fast MC simulator combining GPU acceleration with variance reduction provided a systematic examination of a range of CBCT configuration in relation to scatter, highlighting the magnitude and spatial uniformity of individual scatter components, illustrating tradeoffs in CNR and artifacts and identifying the system geometries for which grids are more beneficia (e.g., MSK) from those in which an extended geometry is the better defense (e.g., C-arm head imaging). Compact geometries with an antiscatter grid challenge assumptions of slowly varying scatter distributions due to increased contribution of Rayleigh scatter.The research was supported by academic-industry partnership with Carestream Health Inc. (Rochester, NY) and National Institutes of Health (NIH) Grant No. 2R01-CA-112163. A. Sisniega is supported by FPU grant (Spanish Ministry of Education), AMIT project, RECAVA-RETIC Network, Project Nos. TEC2010-21619- C04-01, TEC2011-28972-C02-01, and PI11/00616 (Spanish Ministry of Science and Education), ARTEMIS program (Comunidad de Madrid), and PreDiCT-TB partnership.Publicad

Parallelized X-Ray Tracing with GPU Ray-Tracing Engine

X-ray diffraction tomography (XDT) is used to probe material composition of objects, providing improved contrast between materials compared to conventional transmission based computed tomography (CT). In this work, a small angle approximation to Bragg\u27s Equation of diffraction is coupled with parallelized computing using Graphics Processing Units (GPUs) to accelerate XDT simulations. The approximation gives rise to a simple yet useful proportionality between momentum transfer, radial distance of diffracted signal with respect to incoming beam\u27s location, and depth of material, so that ray tracing may be parallelized. NVIDIA\u27s OptiX ray-tracing engine, a parallelized pipeline for GPUs, is employed to perform XDT by tracing rays in a virtual space, (x,y,zv), where zv is a virtual distance proportional to momentum transfer. The advantage gained in this approach is that ray tracing in this domain requires only 3D surface meshes, yielding calculations without the need of voxels. The simulated XDT projections demonstrate high consistency with voxel models, with a normalized mean square difference less than 0.66%, and ray-tracing times two orders of magnitude less than previously reported voxel-based GPU ray tracing results. Due to an accelerated simulation time, XDT projections of objects with three spatial dimensions (4D tensor) have also been reported, demonstrating the feasibility for largescale high-dimensional tensor tomography simulations

Current status and new horizons in Monte Carlo simulation of X-ray CT scanners

With the advent of powerful computers and parallel processing including Grid technology, the use of Monte Carlo (MC) techniques for radiation transport simulation has become the most popular method for modeling radiological imaging systems and particularly X-ray computed tomography (CT). The stochastic nature of involved processes such as X-ray photons generation, interaction with matter and detection makes MC the ideal tool for accurate modeling. MC calculations can be used to assess the impact of different physical design parameters on overall scanner performance, clinical image quality and absorbed dose assessment in CT examinations, which can be difficult or even impossible to estimate by experimental measurements and theoretical analysis. Simulations can also be used to develop and assess correction methods and reconstruction algorithms aiming at improving image quality and quantitative procedures. This paper focuses mainly on recent developments and future trends in X-ray CT MC modeling tools and their areas of application. An overview of existing programs and their useful features will be given together with recent developments in the design of computational anthropomorphic models of the human anatomy. It should be noted that due to limited space, the references contained herein are for illustrative purposes and are not inclusive; no implication that those chosen are better than others not mentioned is intende