Modern CT image reconstruction algorithms rely on projection and back-projection operations to refine an image
estimate in iterative image reconstruction. A widely-used state-of-the-art technique is distance-driven projection and
back-projection. While the distance-driven technique yields superior image quality in iterative algorithms, it is a
computationally demanding process. This has a detrimental effect on the relevance of the algorithms in clinical settings.
A few methods have been proposed for enhancing the distance-driven technique in order to take advantage of modern
computer hardware. This paper explores a two-dimensional extension of the branchless method proposed by Samit Basu
and Bruno De Man. The extension of the branchless method is named “pre-integration” because it achieves a significant
performance boost by integrating the data before the projection and back-projection operations. It was written with
Nvidia’s CUDA platform and carefully designed for massively parallel GPUs. The performance and the image quality of
the pre-integration method were analyzed. Both projection and back-projection are significantly faster with preintegration.
The image quality was analyzed using cone beam image reconstruction algorithms within Jeffrey Fessler’s
Image Reconstruction Toolbox. Images produced from regularized, iterative image reconstruction algorithms using the
pre-integration method show no significant impact to image quality.