Development of a scanner-specific simulation framework for photon-counting computed tomography

Abstract

The aim of this study was to develop and validate a simulation platform that generates photon-counting CT images of voxelized phantoms with detailed modeling of manufacturer-specific components including the geometry and physics of the x-ray source, source filtrations, anti-scatter grids, and photon-counting detectors. The simulator generates projection images accounting for both primary and scattered photons using a computational phantom, scanner configuration, and imaging settings. Beam hardening artifacts are corrected using a spectrum and threshold dependent water correction algorithm. Physical and computational versions of a clinical phantom (ACR) were used for validation purposes. The physical phantom was imaged using a research prototype photon-counting CT (Siemens Healthcare) with standard (macro) mode, at four dose levels and with two energy thresholds. The computational phantom was imaged with the developed simulator with the same parameters and settings used in the actual acquisition. Images from both the real and simulated acquisitions were reconstructed using a reconstruction software (FreeCT). Primary image quality metrics such as noise magnitude, noise ratio, noise correlation coefficients, noise power spectrum, CT number, in-plane modulation transfer function, and slice sensitivity profiles were extracted from both real and simulated data and compared. The simulator was further evaluated for imaging contrast materials (bismuth, iodine, and gadolinium) at three concentration levels and six energy thresholds. Qualitatively, the simulated images showed similar appearance to the real ones. Quantitatively, the average relative error in image quality measurements were all less than 4% across all the measurements. The developed simulator will enable systematic optimization and evaluation of the emerging photon-counting computed tomography technology.

Keywords: computational phantoms; computed tomography; photon-counting; photon-counting computed tomography; simulation; virtual clinical trial.

Figure 1.

The framework of the developed PCD-CT simulator.

Figure 2.

The expectation, variance, and covariance values at energy thresholds of 25 and 75 keV, derived from a MC simulation [28].

Figure 3.

The real and simulated PCD images at 25 keV, 75 keV, and the bin between 25 and 75 keV. Two line profiles are shown comparing the intensity values. The images are shown with a window of 600 and level of 0 Hounsfield Units (HU), HU values were calculated setting the mass attenuation coefficient of water to be 0.0172 mm² g⁻¹.

Figure 4.

Noise magnitude (top left), noise ratio of 25 keV over 75 keV (top right), and noise correlation coefficient (bottom) measured at multiple dose levels for both real and simulated images.

Figure 5.

Noise power spectrum (NPS) measured in real (red) and simulated (blue) images at 25 keV (top-left), 75 keV (top-right), and 25–75 keV (bottom).

Figure 6.

Modulation transfer function measured in the ‘Air’ insert in the real (red) and simulated (blue) images at 25 keV (top-left), 75 keV (top-right), and 25–75 keV (bottom).

Figure 7.

Slice sensitivity profiles measured using the tungsten beads available in the center (left plot) and off-center (right plot) of the ACR phantom. Red plots are measured from the real data and blue plots are measured from simulations.

Figure 8.

Average CT numbers of Bismuth, Iodine, and Gadolinium contrast agents measured at 3 concentration levels and 6 energy thresholds in both real (red) and simulated (blue) images. For the bin images, the lower threshold was 20 keV. To convert the attenuation values to HU, the mass attenuation coefficient of water was assumed to be 0.0172 mm² g⁻¹.

Figure 9.

Photon-counting images an XCAT phantom with some inserted lesions, imaged with the developed simulator at images at 25 (left) and 75 (right) keV. Images are shown with a window/level of −400/1400 HU.