Fast polyenergetic forward projection for image formation using OpenCL on a heterogeneous parallel computing platform

Simulated projection images of digital phantoms constructed from CT scans have been widely used for clinical and research applications but their quality and computation speed are not optimal for real-time comparison with the radiography acquired with an x-ray source of different energies. In this paper, the authors performed polyenergetic forward projections using open computing language (OpenCL) in a parallel computing ecosystem consisting of CPU and general purpose graphics processing unit (GPGPU) for fast and realistic image formation.

The proposed polyenergetic forward projection uses a lookup table containing the NIST published mass attenuation coefficients (μ∕ρ) for different tissue types and photon energies ranging from 1 keV to 20 MeV. The CT images of interested sites are first segmented into different tissue types based on the CT numbers and converted to a three-dimensional attenuation phantom by linking each voxel to the corresponding tissue type in the lookup table. The x-ray source can be a radioisotope or an x-ray generator with a known spectrum described as weight w(n) for energy bin E(n). The Siddon method is used to compute the x-ray transmission line integral for E(n) and the x-ray fluence is the weighted sum of the exponential of line integral for all energy bins with added Poisson noise. To validate this method, a digital head and neck phantom constructed from the CT scan of a Rando head phantom was segmented into three (air, gray∕white matter, and bone) regions for calculating the polyenergetic projection images for the Mohan 4 MV energy spectrum. To accelerate the calculation, the authors partitioned the workloads using the task parallelism and data parallelism and scheduled them in a parallel computing ecosystem consisting of CPU and GPGPU (NVIDIA Tesla C2050) using OpenCL only. The authors explored the task overlapping strategy and the sequential method for generating the first and subsequent DRRs. A dispatcher was designed to drive the high-degree parallelism of the task overlapping strategy. Numerical experiments were conducted to compare the performance of the OpenCL∕GPGPU-based implementation with the CPU-based implementation.

The projection images were similar to typical portal images obtained with a 4 or 6 MV x-ray source. For a phantom size of 512 × 512 × 223, the time for calculating the line integrals for a 512 × 512 image panel was 16.2 ms on GPGPU for one energy bin in comparison to 8.83 s on CPU. The total computation time for generating one polyenergetic projection image of 512 × 512 was 0.3 s (141 s for CPU). The relative difference between the projection images obtained with the CPU-based and OpenCL∕GPGPU-based implementations was on the order of 10(-6) and was virtually indistinguishable. The task overlapping strategy was 5.84 and 1.16 times faster than the sequential method for the first and the subsequent digitally reconstruction radiographies, respectively.

The authors have successfully built digital phantoms using anatomic CT images and NIST μ∕ρ tables for simulating realistic polyenergetic projection images and optimized the processing speed with parallel computing using GPGPU∕OpenCL-based implementation. The computation time was fast (0.3 s per projection image) enough for real-time IGRT (image-guided radiotherapy) applications.