A Hybrid Sparse Primary Sampling (SPS) Strategy for CBCT

Cone Beam Computed Tomography (CBCT) image quality is degraded by the increased scatter from the broad beam geometry, while conventional anti-scatter grid (ASG) only provides partial mitigation at the cost of elevated imaging dose. In this study, we exploit the smooth behavior of the scatter signal with a novel sparse anti-scatter grid to improve the image quality of CBCT. We achieve sparse sampling of the primary beam signal by sparsely inserting individual collimators focusing on the X-ray source into a template in front of the detector. The novel sparse primary sampling (SPS) grid is evaluated via Monte Carlo simulations with a synthetic CT phantom, patient head phantom, and patient pelvis phantom. Image reconstruction based on SPS was formulated as a constrained optimization problem with fidelity terms on the total signal and the sparsely sampled primary signals. Image quality improvement was benchmarked using ideal primary signal reconstructed images, worst-case scatter degraded reconstructed images, and the previously studied 3-D Richardson-Lucy fitting scatter correction method using low count Monte Carlo. The novel SPS grid and reconstruction method demonstrated recovery of HU values and image resolution with sampling densities under 0.1%. The hybrid hardware-software method supports flexible sampling density and pattern with minimal primary signal loss and image dose increase. A novel SPS grid was introduced, and a successful demonstration in the Monte Carlo study shows the feasibility of significantly improving CBCT image quality for interventional and radiotherapy procedures via the SPS strategy.