Program Information
Evaluation of Partial Storage of the System Matrix for Cone Beam Computed Tomography Using a GPU Platform
D Matenine1*, G Cote1 , J Mascolo-Fortin1 , Y Goussard2 , P Despres1,3 , (1) Universite Laval, Quebec, QC, (2) Ecole Polytechnique de Montreal, Montreal, QC, (3) Departement de radio-oncologie and Centre de recherche du CHU de Quebec, Quebec, QC
Presentations
SU-D-206-2 (Sunday, July 31, 2016) 2:05 PM - 3:00 PM Room: 206
Purpose: Iterative reconstruction algorithms in computed tomography (CT) require a fast method for computing the intersections between the photons' trajectories and the object, also called ray-tracing or system matrix computation. This work evaluates different ways to store the system matrix, aiming to reconstruct dense image grids in reasonable time.
Methods: We propose an optimized implementation of the Siddon's algorithm using graphics processing units (GPUs) with a novel data storage scheme. The algorithm computes a part of the system matrix on demand, typically, for one projection angle. The proposed method was enhanced with accelerating options: storage of larger subsets of the system matrix, systematic reuse of data via geometric symmetries, an arithmetic-rich parallel code and code configuration via machine learning. It was tested on geometries mimicking a cone beam CT acquisition of a human head. To realistically assess the execution time, the ray-tracing routines were integrated into a regularized Poisson-based reconstruction algorithm. The proposed scheme was also compared to a different approach, where the system matrix is fully pre-computed and loaded at reconstruction time.
Results: Fast ray-tracing of realistic acquisition geometries, which often lack spatial symmetry properties, was enabled via the proposed method. Ray-tracing interleaved with projection and backprojection operations required significant additional time. In most cases, ray-tracing was shown to use about 66 % of the total reconstruction time. In absolute terms, tracing times varied from 3.6 s to 7.5 min, depending on the problem size. The presence of geometrical symmetries allowed for non-negligible ray-tracing and reconstruction time reduction. Arithmetic-rich parallel code and machine learning permitted a modest reconstruction time reduction, in the order of 1 %.
Conclusion: Partial system matrix storage permitted the reconstruction of higher 3D image grid sizes and larger projection datasets at the cost of additional time, when compared to the fully pre-computed approach.
Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by the Fonds de recherche du Quebec - Nature et technologies (FRQ-NT). The authors acknowledge partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council of Canada (Grant No. 432290).
Contact Email: