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.

A scanner-specific framework for simulating CT images with tube current modulation

“…MC techniques are often utilized for their accuracy in modeling x-ray interaction physics and estimating delivered radiation dose (Jiang and Paganetti 2004, Li et al 2011, Ding et al 2015. Since pure MC simulations are naturally slow, more recent platforms also utilize GPU-acceleration, ray-tracing, or hybrid (combined ray-tracing and MC) techniques to facilitate assessments of image quality and to use larger patient sample sizes (De Man et al 2007, Badal and Badano 2009, Fung et al 2011, Abadi et al 2019a. In this study, we utilized DukeSim, an established hybrid simulation platform (Abadi et al 2019a, 2019b, Sharma et al 2019, Sharma et al 2021.…”

“…To simulate the image acquisition process, DukeSim uses a GPU accelerated hybrid of ray-tracing and Monte Carlo (MC) techniques as described by Abadi et al (2019a). In summary, for each projection, the primary signal is calculated by solving for attenuation at each detector pixel from the Beer-Lambert law.…”

Virtual clinical trials in medical imaging: a review

“…They should ideally take place at multiple levels of granularity. They can be applied to a simulation in its subcomponents 41,369,370 (e.g., model of x-ray spectrum), whole component 141,142,262,371,372 (e.g., accuracy in creating realistic simulated images), or multicomponent 26,278 (e.g., accuracy in creating the complete human imaging process from the patient to the output of the imaging task). One approach for these validations has been through the simulation of IEC standard tests.…”

“…To include the scatter signal, hybrid approaches in which primary signal (using ray-tracing) is combined with scatter signal using either analytical scatter estimations or MC methods with limited number of histories have been developed. 141 – 143 The other limitation with the ray-tracing methods is that they do not account for the finite size of the focal spot and detector pixels, making the simulated images undersampled and unrealistically sharp. This can be remedied by a subsampling strategy in which each source-to-detector ray is replaced by multiple rays sampling the area of the focal spot and detector pixel.…”

“…To date, limited scanner models have been developed and validated. 141 – 143 For comprehensive virtual trials, models of more diverse scanners are needed. Further, as computational phantoms advance, they become more detailed (higher resolution) with more realistic capabilities (e.g., motion and perfusion models).…”