XCIST—an open access x-ray/CT simulation toolkit

Abstract

Objective: X-ray-based imaging modalities including mammography and computed tomography (CT) are widely used in cancer screening, diagnosis, staging, treatment planning, and therapy response monitoring. Over the past few decades, improvements to these modalities have resulted in substantially improved efficacy and efficiency, and substantially reduced radiation dose and cost. However, such improvements have evolved more slowly than would be ideal because lengthy preclinical and clinical evaluation is required. In many cases, new ideas cannot be evaluated due to the high cost of fabricating and testing prototypes. Wider availability of computer simulation tools could accelerate development of new imaging technologies. This paper introduces the development of a new open-access simulation environment for X-ray-based imaging. Approach: The X-ray-based Cancer Imaging Simulation Toolkit (XCIST) is developed in the context of cancer imaging, but can more broadly be applied. XCIST is physics-based, written in Python and C/C++, and currently consists of three major subsets: digital phantoms, the simulator itself (CatSim), and image reconstruction algorithms; planned future features include a fast dose-estimation tool and rigorous validation. To enable broad usage and to model and evaluate new technologies, XCIST is easily extendable by other researchers. To demonstrate XCIST’s ability to produce realistic images and to show the benefits of using XCIST for insight into the impact of separate physics effects on image quality, we present exemplary simulations by varying contributing factors such as noise and sampling. Main Results: The capabilities and flexibility of XCIST are demonstrated, showing easy applicability to specific simulation problems. Geometric and X-ray attenuation accuracy are shown, as well as XCIST’s ability to model multiple scanner and protocol parameters, and to attribute fundamental image quality characteristics to specific parameters. Significance: This work represents an important first step toward the goal of creating an open-access platform for simulating existing and emerging X-ray-based imaging systems.

Pediatric evaluations for deep learning CT denoising

“…These include monoenergetic source and infinitesimally small x‐ray focal spot, 2D fan‐beam, and not incorporating detector blur, bowtie filter, and the effects of scatter. Further planned iterations of the framework's simulations will leverage existing CT simulation frameworks such as XCIST 30 that include these factors as well as helical acquisition and recon with different slice thicknesses to better match real scanner data characteristics.…”

Development and tuning of models for accurate simulation of CT spatial resolution using CatSim

“…temporal response, low signal) were disabled. All simulated data were computed using CatSim within the XCIST package, publicly available from Wu et al (2022).…”

“…The concept of sub-sampling is especially important for simulation of spatial resolution. This is described in detail in Wu et al (2022). In summary, for each projection, x-ray attenuation line integrals are calculated for multiple user-specified samples of the focal spot (lateral and longitudinal subsamples, i.e.…”

“…The x-ray-based Cancer Imaging Simulation Toolkit (XCIST) is currently being developed in the context of cancer imaging but can more broadly be applied to develop improved imaging systems for many medical-imaging needs. (Wu et al 2022) XCIST includes CatSim (De Man et al 2007) as the core simulator, as well as numerical phantoms (Segars et al 2008) and image-reconstruction tools. XCIST is an open-source toolkit accessible to academic and commercial researchers; rigorous validation of its critical features will enable its wide application by scientists and engineers for whom such sophisticated tools may not be currently available.…”

“…The models that contribute to the spatial resolution in the simulated images include the focal spot (FS) (Papadakis and Damilakis 2021), x-ray transport process, detector, x-ray detection process, system architecture, scan trajectory, and reconstruction. Many of these are straightforward to model and have been validated previously (Wu et al 2022). However, two new, critical models that contribute to spatial resolution were developed (figure 1(A)) and evaluated for this work, including a realistic FS model and a detector temporal response model (Duclos et al 2003).…”