# Computational Toolkit for Normalizing the Impact of CT Acquisition and Reconstruction on Quantitative Image Features

> **NIH NIH R56** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2021 · $611,968

## Abstract

Quantitative image features (QIFs) such as radiomic and deep features hold enormous potential to improve the
detection, diagnosis, and treatment assessment of a wide range of diseases. Generated from clinically acquired
Computed Tomography (CT) scans, QIFs represent small pixel-wise changes that may be early indicators of
disease progression. However, detecting these changes is complicated by variations in the way that CT scans
are performed, including variations in acquisition and reconstruction parameters. Ensuring reproducible QIFs is
a prerequisite for developing machine learning (ML) models that achieve consistent performance across different
clinical settings. This project's premise is that QIFs are sensitive to CT parameters such as radiation dose level,
slice thickness, reconstruction kernel, and reconstruction method. The combined interactions among these
parameters result in unique image conditions, each yielding its own QIF value. Moreover, some clinical tasks
and algorithms are more sensitive to differences in QIF values than others. We hypothesize that a systematic,
task-dependent framework to normalize scans and mitigate the impact of variability in CT parameters will identify
reproducible QIFs and yield more consistent ML models. Three interrelated innovations will be pursued in this
work: 1) a novel framework for characterizing the impact of different acquisition and reconstruction parameters
on QIFs and ML models using patient scans with known clinical outcomes in multiple domains; 2) a systematic
approach for selecting an optimal mitigation technique and evaluating the impact of normalization; and 3) an
open-source software toolkit that formalizes the process of CT normalization, addressing real-world use cases
developed by academic and industry collaborators. In Aim 1, we will evaluate how multiple CT parameters
influence QIF values and model performance. Utilizing metrics of agreement and a heat map-based visualization,
we will determine under which image acquisition and reconstruction conditions the QIFs and model performance
are consistent. In Aim 2, we will develop and validate a generative adversarial network-based approach to
normalization. Our investigation will focus on targeted mitigation of the set of imaging conditions that are most
relevant to a clinical task and on the optimization of how these models are trained. In Aim 3, we will engage a
spectrum of external stakeholders to guide the development and adoption of a software toolkit called CT-NORM.
Three distinct clinical domains will drive our efforts: lung nodule detection (which relies on identifying small
regions of high contrast differences to identify nodules), interstitial lung disease quantification (which depends
on characterizing texture differences), and ischemic core assessment (which relies on detecting low contrast
differences in brain tissue). CT-NORM will provide the scientific community with an approach and a unified toolkit
to characterize and mit...

## Key facts

- **NIH application ID:** 10426507
- **Project number:** 1R56EB031993-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** William Hsu
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $611,968
- **Award type:** 1
- **Project period:** 2021-09-13 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10426507

## Citation

> US National Institutes of Health, RePORTER application 10426507, Computational Toolkit for Normalizing the Impact of CT Acquisition and Reconstruction on Quantitative Image Features (1R56EB031993-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10426507. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*