# Diagnostic performance assessment and dose optimization using patient CT images: Application to deep-learning CT reconstruction and denoising technologies

> **NIH NIH R01** · MAYO CLINIC ROCHESTER · 2024 · $587,337

## Abstract

PROJECT SUMMARY/ABSTRACT
Half of the man-made radiation exposure to the U.S. population can be attributed to CT. Thus, stakeholders
have invested heavily in the reduction of CT doses. Recently, CT Deep Learning Reconstruction or Denoising
(DLRD) techniques have become available that claim to enable dose reduction in CT, just as CT iterative
reconstruction (IR) claimed before it. Like DLRD, IR showed dramatic apparent increases in image quality by
reducing image noise. Subsequent clinical studies found that true dose reductions capabilities for IR were only
20-25% in soft tissues and organs. Greater dose reductions resulted in compromised signal detectability for
low-contrast lesions (e.g., liver metastases). Now, DLRD algorithms are being used in clinical practice and
appear to yield image quality superior to IR. But again, we are finding that excessive radiation dose reduction
can introduce new artifacts and compromise lesion detectability. Our overall objective is to develop and
validate methods that can quantitatively determine CT protocols that deliver the needed diagnostic
performance at the lowest patient dose for any scanner model or reconstruction algorithm.
 In our first two competitive award periods, we developed robust Channelized Hotelling model observers
(MOs) to quantify diagnostic performance using low contrast objects within a uniform phantom and validated
the work with large scale reader studies for both filtered back projection and IR images. We are deploying
these tools under EB028936 to allow robust dose optimization by CT users. However, there remain challenges.
First, low-contrast phantoms used with most MOs are too simple. Second, highly realistic lesion and noise
insertion tools require use of proprietary projection data and access to manufacturer software tools. Third, MOs
must be extended to work with DLRD methods and be generalizable to any scanner. The proposed work will
use patient image data, obviating the need for projection data, use our developed deep learning (DL)-MOs to
achieve a scalable solution for performance assessment, and be generalizable to any algorithm or scanner.
 The goal of this renewal application is to develop robust DL-MO tools to efficiently predict diagnostic
performance for images created with DLRD methods. We will accomplish this through three specific aims:
1. Develop and validate disease-insertion and low-dose-simulation tools for CT DLRD techniques.
2. Develop and validate DL-MO tools to quantify mean diagnostic performance for DLRD methods.
3. Extend DL-MO methods to estimate performance variations across readers.
 This work is the first to develop and use DL-MOs as accurate and efficient surrogates of human readers to
characterize task-specific performance of DLRD methods in CT using patient images. The resultant performance
assessment engine will facilitate training / testing of DL-based noise reduction algorithms and optimizing CT
protocols and doses. These significant capabilities wil...

## Key facts

- **NIH application ID:** 10801843
- **Project number:** 2R01EB017095-10
- **Recipient organization:** MAYO CLINIC ROCHESTER
- **Principal Investigator:** Hao Gong
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $587,337
- **Award type:** 2
- **Project period:** 2012-09-20 → 2027-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10801843

## Citation

> US National Institutes of Health, RePORTER application 10801843, Diagnostic performance assessment and dose optimization using patient CT images: Application to deep-learning CT reconstruction and denoising technologies (2R01EB017095-10). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10801843. Licensed CC0.

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