# Modeling observer performance in low-dose CT assessments

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA SANTA BARBARA · 2020 · $416,868

## Abstract

Project Summary/Abstract
 X-ray computed tomography (CT) has become a mainstay of diagnostic imaging in many areas of
medicine because of its ability to render internal structures of the body with high accuracy. This has
resulted in a substantial increase in the use of CT imaging in the United States. As a result, there has been
sustained interest in dose reduction in CT imaging. However, demonstrating effective dose reduction is
challenging. By definition, such techniques seek to retain diagnostic quality with little or no measureable
effect on diagnostic performance. Clinical reader studies using receiver operating characteristic (ROC)
methodology are the accepted standard for evaluating diagnostic performance effects. However, these
studies are expensive and time consuming, and identifying small effects requires prohibitively large sets of
readers and cases. This has led to the development of “model-observers” for dose reduction claims at the US
Food and Drug Administration (FDA). At this time, at least three dose reduction claims at FDA have used
model observer studies to substantiate their claim of CT dose reduction using iterative reconstruction
algorithms.
 The basis for this project is our recognition that such models have had relatively little validation,
given the complexity of both the human visual system and the images being evaluated. We propose an in-depth characterization of human observer responses in tasks related to dose reduction in CT. The purpose
of this research is to develop and validate a model (or models) of observer performance for use in
assessments of image reconstruction for CT dose reduction. For a model observer to be of use in this area, it
must be accepted as a reasonable predictor of human-observer performance for some range of relevant
tasks. This motivates our general approach, and many specifics of our research plan.
 Our plan is to collect an initial set of psychophysical data, use this data to develop our model, and
then predict performance in CT reconstructions from simulations at a variety of doses. We then collect the
psychophysical data on these images to quantify predictive accuracy and to compare it to the accuracy of
other models. Specific Aim 1 involves the collection of psychophysical data in tasks with noise statistics
similar to CT dose assessments. Specific Aim 2 seeks to develop models of task performance by fitting
model parameter for several candidate models to the data from Aim 1. Specific Aim 3 proposes a
prospective prediction of observer performance in a new set of psychophysical data from images that have
been reconstructed using modern iterative methods. At the conclusion of the project period, we expect to
have a better understanding of how observers perform difficult localization and discrimination tasks in
noisy CT images, and how this process in influenced by the dose associated with images.

## Key facts

- **NIH application ID:** 9860920
- **Project number:** 5R01EB025829-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA SANTA BARBARA
- **Principal Investigator:** Craig Kendall Abbey
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $416,868
- **Award type:** 5
- **Project period:** 2018-04-01 → 2022-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9860920

## Citation

> US National Institutes of Health, RePORTER application 9860920, Modeling observer performance in low-dose CT assessments (5R01EB025829-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9860920. Licensed CC0.

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