GPU-accelerated 3D Bayesian image reconstruction from Compton scattered data

Abstract

This paper describes the development of fast Bayesian reconstruction methods for Compton cameras using commodity graphics hardware. For fast iterative reconstruction, not only is it important to increase the convergence rate, but also it is equally important to accelerate the computation of time-consuming and repeated operations, such as projection and backprojection. Since the size of the system matrix for a typical Compton camera is intractably large, it is impractical to use a conventional caching scheme that stores the pre-calculated elements of a system matrix and uses them for the calculation of projection and backprojection. In this paper we propose GPU (graphics processing unit)-accelerated methods that can rapidly perform conical projection and backprojection on the fly. Since the conventional ray-based backprojection method is inefficient for parallel computing on GPUs, we develop voxel-based conical backprojection methods using two different approximation schemes. In the first scheme, we approximate the intersecting chord length of the ray passing through a voxel by the perpendicular distance from the center to the ray. In the second scheme, each voxel is regarded as a dimensionless point rather than a cube so that the backprojection can be performed without the need for calculating intersecting chord lengths or their approximations. Our simulation studies show that the GPU-based method dramatically improves the computational speed with only minor loss of accuracy in reconstruction. With the development of high-resolution detectors, the difference in the reconstruction accuracy between the GPU-based method and the CPU-based method will eventually be negligible.

Adaptation of filtered back-projection to compton imaging with non-uniform azimuthal geometry

“…The calculation times of iterative methods can be decreased by employing recently developed fast algorithms and accelerated processors such as advanced OSEMs and GPUs [19,20]. However, the iterative methods should collect data in a group and update the image iteratively; hence, they cannot reconstruct Compton images eventby-event during real-time measurements.…”

A New Compton Camera Imaging Model to Mitigate the Finite Spatial Resolution of Detectors and New Camera Designs for Implementation

“…This reduction will reduce the amount of data that need to be measured. A number of iterative algorithms have been proposed for reconstruction from Compton camera data [45][46][47][48][49]. The importance of reducing the amount of data that needs to be processed is especially important if an iterative algorithm is used for the reconstruction.…”

Probabilistic models and numerical calculation of system matrix and sensitivity in list-mode MLEM 3D reconstruction of Compton camera images

“…Reducing the calculation time and the memory burden is another important challenge in Compton imaging. Approximated calculation of the system matrix on GPU was proposed by Nguyen et al (2011). Arguing that the ray-tracing method is not adapted to fast implementation on GPU, the authors propose to approximate the cord length resulting from the intersection of a ray with a voxel by the orthogonal distance from the center of the voxel to the ray.…”