A Fully GPU-Based Ray-Driven Backprojector via a Ray-Culling Scheme with Voxel-Level Parallelization for Cone-Beam CT Reconstruction

Abstract

A ray-driven backprojector is based on ray-tracing, which computes the length of the intersection between the ray paths and each voxel to be reconstructed. To reduce the computational burden caused by these exhaustive intersection tests, we propose a fully graphics processing unit (GPU)-based ray-driven backprojector in conjunction with a ray-culling scheme that enables straightforward parallelization without compromising the high computing performance of a GPU. The purpose of the ray-culling scheme is to reduce the number of ray-voxel intersection tests by excluding rays irrelevant to a specific voxel computation. This rejection step is based on an axis-aligned bounding box (AABB) enclosing a region of voxel projection, where eight vertices of each voxel are projected onto the detector plane. The range of the rectangular-shaped AABB is determined by min/max operations on the coordinates in the region. Using the indices of pixels inside the AABB, the rays passing through the voxel can be identified and the voxel is weighted as the length of intersection between the voxel and the ray. This procedure makes it possible to reflect voxel-level parallelization, allowing an independent calculation at each voxel, which is feasible for a GPU implementation. To eliminate redundant calculations during ray-culling, a shared-memory optimization is applied to exploit the GPU memory hierarchy. In experimental results using real measurement data with phantoms, the proposed GPU-based ray-culling scheme reconstructed a volume of resolution 28032803176 in 77 seconds from 680 projections of resolution 10243768 , which is 26 times and 7.5 times faster than standard CPU-based and GPU-based ray-driven backprojectors, respectively. Qualitative and quantitative analyses showed that the ray-driven backprojector provides high-quality reconstruction images when compared with those generated by the Feldkamp-Davis-Kress algorithm using a pixel-driven backprojector, with an average of 2.5 times higher contrast-to-noise ratio, 1.04 times higher universal quality index, and 1.39 times higher normalized mutual information.

Examination of a deformable motion model for respiratory movements and 4D dose calculations using different driving surrogates

“…A total time of about 15 min is necessary to predict a full 4DCT consisting of eight CT images. Computational time improvements could be achieved using parallelized GPU‐based calculations and cropping the estimated volumes to the area encompassing the target and the surrounding organs at risk. First results with a reduced volume of interest could decrease the calculation time under a second.…”

Anisotropic Total Variation Denoising Technique for Low-Dose Cone-Beam Computed Tomography Imaging

“…Given that OpenMP was used to parallelize the proposed algorithm on the CPU platform, we can easily extend its use to the GPU. 18)…”

Low-dose CBCT reconstruction via joint non-local total variation denoising and cubic B-spline interpolation

“…www.nature.com/scientificreports/ In our implementation, OpenMP was used to parallelize the proposed algorithm on an Intel Xeon CPU with 24 physical cores and 48 logical processors. Therefore, the proposed algorithm can be extended and accelerated by using a GPU 28 . The number of iterations for the NLTV minimization was determined to be ten, however, this value may not be optimal.…”

“…In general, it has been observed that the better the initial guess image when using an iterative reconstruction algorithm, the faster the convergence and CBCT images that can be created with a high contrast. In addition, a ray-driven backprojector can be combined with NLTV 28 .…”