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A significant challenge to the implementation of cone-beam computed tomography (CBCT) for high-resolution imaging is the high scatter to primary ratio. Scatter causes cupping and shading artifacts, increased noise and decreased contrast in reconstructed images. Methods to reduce the impact of scatter in CBCT are thus very desirable. We are investigating methods for computational scatter estimation and compensation for CBCT, with the goal of incorporating a scatter estimator within a statistical reconstruction algorithm. We have developed an analytical method for estimating single scatter, based on Klein-Nishina cross-sections. We have compared scatter estimates generated with this method with the results of high-count EGSnrc Monte Carlo simulations. The analytical estimates compare favorably with the Monte Carlo estimates. The paper will discuss our method for analytical estimation of single scatter, including the assumptions and simplifications required to render it computationally tractable, along with the results of the comparison between the analytical method and Monte Carlo simulations. The paper will extend previous results obtained with small (40 × 40 × 40 voxel) homogeneous computational phantoms to include results for larger, more clinically relevant phantoms (128 × 128 × 128 voxels, simulated 50/50 breast tissue with inserts of varying contrast). The paper will also discuss computational acceleration obtained through the use of parallel processing via the WestGrid High-Performance Computing network. |
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