DukeSim: A Realistic, Rapid, and Scanner-Specific Simulation Framework in Computed Tomography

Abstract

The purpose of this study was to develop a CT simulation platform that is: 1) compatible with voxel-based computational phantoms; 2) capable of modeling the geometry and physics of commercial CT scanners; and 3) computationally efficient. Such a simulation platform is designed to enable the virtual evaluation and optimization of CT protocols and parameters for achieving a targeted image quality while reducing radiation dose. Given a voxelized computational phantom and a parameter file describing the desired scanner and protocol, the developed platform DukeSim calculates projection images using a combination of ray-tracing and Monte Carlo techniques. DukeSim includes detailed models for the detector quantum efficiency, quantum and electronic noise, detector crosstalk, subsampling of the detector and focal spot areas, focal spot wobbling, and the bowtie filter. DukeSim was accelerated using GPU computing. The platform was validated using physical and computational versions of a phantom (Mercury phantom). Clinical and simulated CT scans of the phantom were acquired at multiple dose levels using a commercial CT scanner (Somatom Definition Flash; Siemens Healthcare). The real and simulated images were compared in terms of image contrast, noise magnitude, noise texture, and spatial resolution. The relative error between the clinical and simulated images was less than 1.4%, 0.5%, 2.6%, and 3%, for image contrast, noise magnitude, noise texture, and spatial resolution, respectively, demonstrating the high realism of DukeSim. The runtime, dependent on the imaging task and the hardware, was approximately 2-3 minutes per rotation in our study using a computer with 4 GPUs. DukeSim, when combined with realistic human phantoms, provides the necessary toolset with which to perform large-scale and realistic virtual clinical trials in a patient and scanner-specific manner.

Fig. 1.

The flowchart for DukeSim.

Fig. 2.

a) Illustration of a virtual clinical trial platform including a virtual scanner and a virtual patient. b) Illustration of the geometry of DukeSim.

Fig. 3.

Simulated CT images of a water phantom. Images on the left were reconstructed without any beam hardening correction (BHC). Images on the right were reconstructed with BHC. The images on the first row were acquired without any bowtie filter, whereas the images on the second row were acquired using a Siemens “body” filter. The results show that our BHC was able to suppress the beam hardening artifact in both cases.

Fig. 4.

Real (first row) and simulated (second row) images of a Mercury phantom at 50, 150, and 300 mAs, with the water inserts magnified, providing a closer look in the low contrast regions.

Fig. 5.

Noise magnitude measured in real and simulated Mercury phantom images at 50, 150, and 300 mAs.

Fig. 6.

Normalized noise power spectrum measured in real and simulated Mercury phantom images at 50, 150, and 300 mAs.

Fig. 7.

Modulation transfer functions measured in real and simulated Mercury phantom images.

Fig. 8.

First row: simulated CT images reconstructed with FBP (left) and iterative (right) algorithms. Second row: voxel-based noise magnitude maps in FBP and iterative images measured by 50 repetitive image acquisitions. Results suggest that the noise is locally stationary in FBP images but not stationary in the iterative images. Third row: noise reduction maps on the left and NPS curves, measured in the lungs, on the right, demonstrating that iterative images have less noise in uniform regions but higher noise in the edge voxels. The peak frequency of the NPS corresponding to lower noise (uniform regions) were lower in comparison with FBP images.