The proliferation of cone-beam CT (CBCT) has created interest in performance optimization, with x-ray scatter identified among the main limitations to image quality. CBCT often contends with elevated scatter, but the wide variety of imaging geometry in different CBCT configurations suggests that not all configurations 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, efficacy of antiscatter grids, guide system design, and augment development of scatter correction.
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-panel 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-to-detector distance (ADD) of 5, 12, 22, 32, 60, and 50 cm, respectively. Each configuration 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.
Variance reduction yielded improvements in MC simulation efficiency ranging from ∼17-fold (for SA CBCT) to ∼35-fold (for Head and C-arm), with the most significant acceleration due to interaction splitting (∼6 to ∼10-fold increase in efficiency). The benefit 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 efficiency was higher for configurations 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 configurations but had limited improvement on scatter-induced 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).
A fast MC simulator combining GPU acceleration with variance reduction provided a systematic examination of a range of CBCT configurations 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 beneficial (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 authors thank Yoshito Otake, Ph. D. (Johns Hopkins University) for assistance with GPU cone-beam reconstruction, and John Boone, Ph. D. (University of California Davis) for providing the digital breast phantom. 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.
II. MATERIALS AND METHODS
II.A. GPU-accelerated Monte Carlo simulation platform
II.A.1. Polyenergetic x-ray source model
II.A.2. Photon tracking and variance reduction techniques
II.A.3. Analytical model for the antiscatter grid and flat-panel detector
II.B. CBCT system and object models
II.C. Monte Carlo experiments
II.C.1. Validation of Monte Carlo variance reduction techniques
II.C.2. Effects of system geometry on scatter: Continuously varied SAD and SDD
II.C.3. Specific CBCT configurations: Scatter distributions, image artifacts, and efficacy of an antiscatter grid
II.D. Metrics of scatter assessment
II.D.2. Contrast reduction
II.D.3. Contrast-to-noise ratio
III.A. Validation of variance reduction techniques
III.B. Effects of system geometry on scatter: Continuously varied SAD and SDD
III.C. Scatter in various CBCT configurations: Cupping, streaks, contrast, and CNR
III.D. Scatter in various CBCT configurations: Scatter components and spatial distribution
III.E. Scatter in various CBCT configurations: Realistic anatomical phantoms
IV. DISCUSSION AND CONCLUSIONS
- X-ray scattering
- Cone beam computed tomography
- Multiple scattering
- Rayleigh scattering
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