# TR&D Project 2: Virtual Scanners

> **NIH NIH P41** · DUKE UNIVERSITY · 2022 · $294,150

## Abstract

ABSTRACT – 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 ...

## Key facts

- **NIH application ID:** 10372910
- **Project number:** 5P41EB028744-02
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Ehsan Samei
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $294,150
- **Award type:** 5
- **Project period:** 2021-04-01 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10372910

## Citation

> US National Institutes of Health, RePORTER application 10372910, TR&D Project 2: Virtual Scanners (5P41EB028744-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10372910. Licensed CC0.

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