? TRD2: Virtual Scanners Virtual Imaging Trials (VITs) offer a powerful alternative to conducting studies of computed tomography (CT) technologies with human subjects. With the trial taking place in silico, virtual trials require a fast and realistic CT simulator. However, current CT simulators are inadequate to meet this need due to limited representation of the actual CT acquisition processes and slow speed. Simulators using Monte Carlo methods are optimal in accurately modeling the image acquisition process but too slow for simulating high resolution images. Alternative ray-tracing methods are faster but unable to provide realistic estimates of absorbed radiation dose, a factor of high importance in CT imaging. Most simulators are further limited in their ability to model specific CT makes and models, which would be essential to represent an actual clinical CT imaging scenario. This project develops and provides a new CT simulation platform to meet the desired throughput and realism of virtual imaging trials. The platform combines the benefits of high spatio/temporal details (provided by ray- tracing), precise radiation dose and scatter estimates (provided by Monte Carlo), speed (provided by GPU computing and proficient programing), and specificity (modeling CT subcomponents based on precise system specifications from CT manufacturers). Already prototyped for one CT scanner, this project will expand the prototype into a comprehensive CT simulator platform for multiple CT systems.
The Specific Aims of the project are (1) to model CT acquisition subcomponents in detail; (2) to model CT acquisition schemes for estimating primary signal, scatter, and radiation dose; (3) to implement processes for integration, image formation, and validation; and (4) to build a modular interface to enable effective use of the simulator. The simulation will include manufacturer-specific, user-defined, and generic (i.e., manufacturer- neutral) CT systems and reconstruction algorithms, detector geometry and models (including photon-counting detectors), full user-control over acquisition specifications (i.e., virtual patient input from TRD1, CT scanner, protocol, kV, mA, recon, etc.), and a user-friendly modular interface with both GUI and script-based utility. This work will provide a first-of-its-kind rapid and accurate CT simulator with scanner-specific, user- customizable, and generic 3D and 4D modeling capabilities, which can simulate both reconstructed images and absorbed radiation dose. Users will be able to utilize the simulator to study a variety of CT technologies and applications, such as those pertaining to radiation dose optimization, image quality assessment, and image deformation from cardiac and respiratory motion. The simulator would enable task-based design and evaluation of new CT systems and artificial intelligence (AI)-based training through generating large-scale realistic image datasets that replicate the realism of clinical images with the added advantage of known ground truth. The CT simulation platform, combined with the suite of virtual patients (TRD1) and virtual readers (TRD3) offered by the Center, form the essential toolset to enable virtual imaging trials in CT.
