# FAIR-CT: a practical approach to enable ultra-low dose CT for longitudinal disease and treatment monitoring

> **NIH NIH R21** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2020 · $265,345

## Abstract

Project Abstract/Summary
Ultra-low dose CT, defined as sub-millisievert (sub-mSv) imaging of the entire chest, abdomen or pelvis, is
critically needed for healthcare of patients with chronic diseases and cancer. Unfortunately, photon starvation
and electronic noise make imaging at such dose levels challenging. Photon starvation refers to the number of
transmitted photons. When no photons are transmitted, the measurement is essentially useless. If few photons
are transmitted, the measurement carries information, but its interpretation and value are confounded by
electronic noise. Solutions with encouraging results have been offered for sub-mSv chest imaging, but these
are not widely available and not easily generalizable across anatomical sites, vendors and scanner models.
We propose a novel, robust solution for ultra-low dose CT that will overcome these issues. We refer to our
solution as FAIR-CT, which stands for Finite-Angle Integrated-Ray CT. FAIR-CT operates under the principle
that photon starvation and the confounding effect of electronic noise are best handled by avoiding them, which
is made possible by increasing the data integration time during the source-detector rotation. FAIR-CT data
strongly deviate from the classical CT data model and share the streak artifact problem of sparse view
sampling. FAIR-CT data acquisition also affects azimuthal resolution. We anticipate that these issues can be
suitably handled using advanced image reconstruction techniques. Once available, FAIR-CT will allow
improvements in longitudinal monitoring of patients with chronic diseases such as COPD, urolithiasis and
diabetes, thereby reducing mortality and co-morbidities. FAIR-CT will also allow advancing cancer therapy
treatments by enabling adjustments in radiation therapy plans between dose fractions without
increasing CT radiation exposure, and by facilitating early detection of inflammations in drug-based
therapies. To bring FAIR-CT towards fruition, we will work on two specific aims: (1) Creation of a
comprehensive collection of FAIR-CT data sets enabling rigorous development, validation and evaluation of
image reconstruction algorithms; (2) Development, validation and evaluation of advanced image reconstruction
algorithms. The FAIR-CT data sets will involve the utilization of state-of-the-art scanners and include real
patient data synthesized from high dose scans acquired for standard of care. Two complementary image
reconstruction approaches will be investigated. Namely, model-based iterative reconstruction with non-linear
forward model and dedicated compressed sensing regularization; and deep learning-based refinement of FBP
reconstructions using target images with task-adapted image quality. Image quality evaluation will account for
critical biological variables and involve objective metrics such as structure similarity and contrast-to-noise ratio
for clinically-proven lesions, as well as task-based performance metrics involving human readers.

## Key facts

- **NIH application ID:** 9877188
- **Project number:** 1R21EB029179-01
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Frederic Noo
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $265,345
- **Award type:** 1
- **Project period:** 2020-05-15 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9877188

## Citation

> US National Institutes of Health, RePORTER application 9877188, FAIR-CT: a practical approach to enable ultra-low dose CT for longitudinal disease and treatment monitoring (1R21EB029179-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9877188. Licensed CC0.

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