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  1. Home
  3. Radiomics-informed Ct Protocol Optimization Through Feature-level Trade-off Analysis In A Phantom Study.
  1. Home
  3. Radiomics-informed Ct Protocol Optimization Through Feature-level Trade-off Analysis In A Phantom Study.

Related Experiment Video

Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics

Published on: January 8, 2018

Radiomics-informed CT protocol optimization through feature-level trade-off analysis in a phantom study.

Mehrnoosh Karimipourfard1, David Stocker1, Christian Sommer1

  • 1ZHAW School of Engineering, 8401 Winterthur, Switzerland.

Physics in Medicine and Biology
|June 18, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Optimizing CT protocols using radiomics improves feature reproducibility and low-contrast detectability. A new framework balances feature sensitivity and detectability for better quantitative CT analysis.

Keywords:
computed tomography (CT)low-contrast detectabilityphantom studyprotocol optimizationradiomic features

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Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement
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Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement

Published on: July 29, 2013

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Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics

Published on: January 8, 2018

Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement
06:33

Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement

Published on: July 29, 2013

Area of Science:

  • Medical Imaging
  • Radiology
  • Quantitative CT Analysis

Background:

  • Reproducibility of radiomic features in quantitative CT analysis is challenged by acquisition protocol variations.
  • Identifying robust features is crucial for reliable diagnostic performance.

Purpose of the Study:

  • To identify radiomic features predicting low-contrast detectability.
  • To quantify feature sensitivity to CT acquisition parameters.
  • To develop a framework for CT protocol optimization based on feature trade-offs.

Main Methods:

  • Extraction of 107 radiomic features from phantom scans across multiple CT protocols.
  • Assessment of feature robustness using coefficient of variation (CV) and intraclass correlation coefficient (ICC).
  • Development of a random forest model for detectability prediction and sensitivity analysis using Spearman's correlation.

Main Results:

  • ComBat harmonization significantly improved feature reproducibility.
  • First-order features demonstrated the highest predictive power for detectability (AUC = 0.94, accuracy = 0.90).
  • Texture and shape features showed varying sensitivities to protocol parameters and dose, respectively. A trade-off exists between detectability and feature sensitivity.

Conclusions:

  • A quantitative framework for radiomics-informed CT protocol optimization was established.
  • Moderate acquisition settings offer a more balanced and robust performance.
  • The findings provide a foundation for optimizing CT protocols in clinical settings.