An end-to-end CT simulation framework with graphical user interface and sample scanner models

Virtual imaging trials (VIT) facilitate medical imaging experimentation through computational models of patients and scanning equipment. For broad adoption across the medical imaging community, VIT tools should not only be accurate but also robust and user-friendly.

To develop a validated, end-to-end CT simulation framework with script-based and graphical user interfaces (GUIs), packaged for simple installation and robust performance across diverse computing environments.

A previously-validated CT simulator (DukeSim) was packaged into an end-to-end framework with four major components: (1) a web-based GUI inspired by clinical scanner consoles, (2) a Python wrapper script serving as a flexible entry point, (3) a physics-based CT projector utilizing ray-tracing and Monte Carlo methods, and (4) a vendor-neutral reconstruction module (MCR Toolkit) supporting both filtered back-projection and iterative techniques. The web-based GUI was developed based on NodeJS and Express server configuration to select the protocol and scanner configurations and to initiate the CT simulations. The integrated DukeSim software was built in three types of packages, with rigorous version control, testing, bug tracking, and release processes. Further, the software's capabilities and potential utilities were demonstrated by developing sample scanner models mimicking the attributes of legacy CT scanners.

The integrated CT simulation framework was successfully developed, enabling seamless adoption and broad applicability in virtual imaging trials. Additionally, the modeling and validation of legacy scanners demonstrated the framework's capability to accurately represent a variety of clinically relevant scanners.

This work represents a major advancement in CT simulation tools, providing an end-to-end, validated, and robust solution for virtual imaging trials. The software's flexibility to model various CT technologies, combined with its user-friendly interface and validated accuracy, positions it as a valuable tool for advancing research in CT technology development, assessment, and optimization.