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[A method for determining spatial resolution of phantom based on automatic contour delineation].

Ying Liu1, Minghao Sun1, Haowei Zhang1

  • 1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|April 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method using an AFRCNN model for precise spatial resolution measurement in CT phantom images. The technique enhances efficiency and accuracy, offering potential for widespread clinical application.

Keywords:
Automatic contour delineationModulation transfer functionSelf-made automatic tube current modulation phantomSpatial resolution

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Area of Science:

  • Medical Imaging Physics
  • Artificial Intelligence in Medical Diagnostics
  • Image Processing Algorithms

Context:

  • Accurate spatial resolution measurement is crucial for evaluating Computer Tomography (CT) performance.
  • Traditional methods for phantom analysis can be time-consuming and prone to manual inaccuracies.
  • Developing automated solutions is essential for improving efficiency and reproducibility in medical imaging quality control.

Purpose:

  • To propose and validate an automated contour outlining method for measuring the spatial resolution of homemade automatic tube current modulation (ATCM) phantoms.
  • To leverage deep learning, specifically an automated fast region convolutional neural network (AFRCNN) model, for accurate phantom edge segmentation.
  • To establish a robust pipeline for calculating the modulation transfer function (MTF) and spatial resolution index (RI) from CT phantom images.

Summary:

  • An automated contour outlining method was developed using an AFRCNN model to segment phantom images.
  • The method calculates the edge spread function (ESF) and line spread function (LSF) from binarized phantom images.
  • Spatial resolution index (RI) and modulation transfer function (MTF) are derived via Fourier transform for automated measurement, validated against PMMA-based methods.

Impact:

  • The AFRCNN model significantly improves the efficiency and accuracy of phantom contour outlining.
  • The proposed algorithm provides a more accurate spatial resolution value through automated segmentation.
  • This method demonstrates potential for broad application in clinical CT image quality assessment and quality assurance programs.