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Material decomposition using dual-energy CT with unsupervised learning.

Hui-Yu Chang1, Chi-Kuang Liu2, Hsuan-Ming Huang3

  • 1Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, No.1, Sec. 1, Jen Ai Rd., Zhongzheng Dist., Taipei City, 100, Taiwan.

Physical and Engineering Sciences in Medicine
|September 11, 2023
PubMed
Summary
This summary is machine-generated.

Deep image prior (DIP) enhances material decomposition (MD) from dual-energy CT (DECT) images. This unsupervised method improves image quality and noise reduction for bone and soft tissue imaging.

Keywords:
Deep image priorDual-energy computed tomographyMaterial decomposition

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Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Artificial Intelligence in Medicine

Background:

  • Material decomposition (MD) from dual-energy computed tomography (DECT) is crucial for quantitative imaging.
  • Conventional direct inversion methods amplify noise, degrading the quality of decomposed material images.

Purpose of the Study:

  • To introduce and evaluate an unsupervised, image-domain MD method using deep image prior (DIP).
  • To assess the feasibility of DIP-based MD for decomposing DECT images into two (bone, soft tissue) and three (bone, soft tissue, fat) basis materials.

Main Methods:

  • Retrospective analysis of non-contrast brain DECT scans from patients.
  • Application of a novel DIP-based algorithm for unsupervised material decomposition.
  • Evaluation of decomposed images using signal-to-noise ratio (SNR) and modulation transfer function (MTF).

Main Results:

  • The DIP-based method significantly improved SNR in soft-tissue images compared to direct inversion and iterative methods.
  • Similar MTF curves were observed for both two- and three-material decompositions.
  • The DIP method demonstrated superior material separation capabilities in three-material decomposition.

Conclusions:

  • The proposed DIP-based method effectively generates high-quality basis material images from DECT data without requiring training datasets.
  • This unsupervised approach offers a promising solution for noise reduction and improved image quality in DECT material decomposition.