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X-ray CT image denoising with MINF: A modularized iterative network framework for data from multiple dose levels.

Qiang Du1, Yufei Tang2, Jiping Wang3

  • 1Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.

Computers in Biology and Medicine
|December 17, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel iterative network framework (MINF) for robust low-dose CT (LDCT) image denoising. The MINF model offers gradual noise reduction, enhancing diagnostic confidence for physicians across various noise levels.

Keywords:
Image denoisingLow-dose CTModularized iterative network frameworkMulti-dose

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Computed Tomography (CT) imaging often involves multi-dose protocols, leading to variable noise levels in clinical images.
  • Existing Low-Dose CT (LDCT) denoising networks may not adequately address the range of noise encountered in clinical practice, potentially hindering diagnosis.
  • The need for robust denoising solutions that adapt to varying noise levels is critical for accurate medical image interpretation.

Purpose of the Study:

  • To develop a novel and efficient modularized iterative network framework (MINF) capable of handling multi-noise level CT image denoising.
  • To enable gradual denoising, providing physicians with clinical images at adjustable noise reduction levels for improved diagnostic confidence.
  • To enhance feature extraction and preserve image quality, including contrast and details, during the denoising process.

Main Methods:

  • Proposed a modularized iterative network framework (MINF) where features from initial modules are reused in subsequent ones.
  • Integrated a multi-scale convolutional neural network (MCNN) module for comprehensive feature extraction.
  • Conducted extensive experiments on public and private clinical datasets, comparing performance against state-of-the-art methods and a modularized adaptive processing neural network (MAP-NN).

Main Results:

  • The MINF framework demonstrated satisfactory noise suppression for LDCT images.
  • MINF exhibited superior gradual denoising performance compared to MAP-NN.
  • The proposed method effectively preserved image contrast and details as the denoising level increased.

Conclusions:

  • The MINF framework offers a robust solution for multi-dose level CT image denoising.
  • Gradual denoising capabilities enhance physician confidence and diagnostic accuracy.
  • The network's ability to maintain image quality across increasing denoising levels highlights its potential for clinical application.