# TR&D Project 3: Virtual Readers

> **NIH NIH P41** · DUKE UNIVERSITY · 2021 · $283,002

## Abstract

ABSTRACT – TRD3: Virtual Readers
The Center proposes virtual imaging trials (VITs), a new paradigm to evaluate rapidly advancing imaging
technologies, including computed tomography (CT). VITs offer a computational alternative to the evaluation of
these technologies through clinical trials, which are slow, expensive, and often lack ground truth, while
exposing subjects to ionizing radiation. The Center will develop a VIT platform to emulate key elements of the
imaging chain from virtual patients (TRD1) to virtual scanners (TRD2) to virtual readers (TRD3). The virtual
reader, the focus of this TRD, are defined as image analysis tools that emulate and extend the clinical reading
of images for specific tasks or needs such as lesion detection, classification, or measurement. Specifically, the
virtual readers comprise three representative categories: observer models, radiomics, and machine learning.
Virtual readers can efficiently and effectively analyze the vast amounts of data in imaging trials, be they clinical
or simulated. To date, most virtual reader approaches have been limited by their narrow focus, uncertainty of
ground truth (normal anatomy and disease), or lack of interoperability. As a result, these technologies have not
yet been translated broadly. To address this unmet need, TRD3 will codify a suite of easy-to-use virtual reader
tools to enable not only VITs but also a wide range of other medical image evaluation needs.
This work will proceed in three Specific Aims: (1) implement an observer model and radiomics toolset for task-
based assessment of CT images, (2) create deep learning resources for analysis and processing of CT
images, and (3) integrate virtual reader utilities into a unified VIT platform and validate it against studies with
real images and radiologists. While TRD3 focuses primarily on virtual readers, as the final technology
development project of the Center, it will also validate Center resources as a whole.
The deliverables of TRD3 include the following: (1) virtual reader tools that go beyond niche applications and
generalize to different subjects, systems, and tasks; (2) performance assessment that is informed by
controllable ground truth for both normal anatomy and disease; (3) “estimability index” to assess bias and
precision of virtual reader metrics; (4) machine learning tools that perform disease detection and classification
as well as data augmentation, all of which are crucial to VITs; (5) resources for medical imaging that transcend
VITs with applications including clinical evaluation and education, and (6) benchmark databases and
performance levels that facilitate a culture of open science where technology assessment becomes fair and
reproducible. TRD3 will have a significant impact on clinical imaging science and practice by not only enabling
effective ways of evaluating imaging technology but also spurring new developments in data science for
medical imaging. The virtual reader resources combined with myria...

## Key facts

- **NIH application ID:** 10089804
- **Project number:** 1P41EB028744-01A1
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** JOSEPH Y LO
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $283,002
- **Award type:** 1
- **Project period:** 2021-04-01 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10089804

## Citation

> US National Institutes of Health, RePORTER application 10089804, TR&D Project 3: Virtual Readers (1P41EB028744-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10089804. Licensed CC0.

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