# Dissemination of a Software Platform for Efficient CT Radiation Dose Optimization and Diagnostic Performance Assessment

> **NIH NIH U24** · MAYO CLINIC ROCHESTER · 2022 · $310,796

## Abstract

PROJECT SUMMARY/ABSTRACT
With the introduction of many novel techniques to minimize radiation dose in CT, there is still a large variation
in terms of radiation dose levels prescribed in CT exams and therefore a large variation of diagnostic
performance. Some patients may receive higher dose than necessary. Some may be under-dosed and mis-
diagnosed as a result of insufficient image quality. In order to determine the appropriate amount of radiation
dose reduction in each exam, accurate quantification of diagnostic performance is needed so that the dose
reduction can be achieved without sacrificing important diagnostic information. However, currently there is a
lack of efficient and quantitative tools for objective assessment of diagnostic performance, particularly for many
of the novel dose reduction methods involving non-linear processing of the data such as iterative
reconstruction and deep-learning-based noise reduction methods.
The specific goal of this application is to disseminate a highly automated solution, CT Protocol optimization
(CTPro) software, to a wide CT community. This quantitative tool provides an efficient implementation of
diagnostic performance assessment and CT radiation dose optimization. This tool is based on channelized
Hotelling observer (CHO), which itself was developed decades ago to mimic human observer visual responses
in signal detection tasks. However, the use of CHO in clinical CT is quite limited because of a lack of rigorous
validation and efficient and robust implementation in practice. We were the first to demonstrate its correlation
with human observer performance in low-contrast detection, classification and localization tasks in clinical CT.
The main objective of the current proposal is to optimize this tool for simplicity and robustness, and
disseminate it to CT researchers and clinical users, which will be accomplished through 3 specific aims:
Aim 1: Optimize CTPro for simplicity, robustness, and generalizability.
Aim 2: Develop an open-source web-based platform for software dissemination.
Aim 3: Build use cases and disseminate CTPro.
The proposed work is significant because the software tool will allow any CT users and researchers to perform
CT radiation dose optimization and diagnostic performance evaluation in an efficient, quantitative, and
objective manner. This work is innovative in that the automated tool will use quantitative measures of
diagnostic performance to systematically guide the complex task of CT dose optimization, moving beyond
traditional metrics that are inappropriate for many novel dose reduction techniques. The software tool, once
widely employed, will facilitate a paradigm shift in how dose optimization and the evaluation of dose reduction
techniques are performed, and will allow a more rapid and consistent adoption of dose reduction technology
into clinical practice, which will benefit millions of CT patients.

## Key facts

- **NIH application ID:** 10445244
- **Project number:** 5U24EB028936-04
- **Recipient organization:** MAYO CLINIC ROCHESTER
- **Principal Investigator:** Lifeng Yu
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $310,796
- **Award type:** 5
- **Project period:** 2019-09-20 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10445244

## Citation

> US National Institutes of Health, RePORTER application 10445244, Dissemination of a Software Platform for Efficient CT Radiation Dose Optimization and Diagnostic Performance Assessment (5U24EB028936-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10445244. Licensed CC0.

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