Multi-Energy Computed Tomography and its Applications

Submission Deadline:  01 May 2021

IEEE Access invites manuscript submissions in the area of Multi-Energy Computed Tomography and its Applications.

X-ray Computed Tomography (CT) can reconstruct the internal image of an object by passing x-rays through it and measuring the information. However, the conventional CT not only has poor performance in tissue contrast and spatial resolution, but also fails to provide quantitative analysis results and specific material components. To avoid these limitations, as a natural extension of the well-known dual-energy CT, the multi-energy CT (MECT) has emerged and is attracting increasing attention. A typical MECT system has great potential in reducing x-ray radiation doses, improving spatial resolution, enhancing material discrimination ability and providing quantitative results by collecting several projections from different energy windows (e.g. photon-counting detector technique) or spectra (e.g. fast kV-switching technique) either sequentially or simultaneously. It is a great achievement in terms of tissue characterization, lesion detection and material decomposition, etc. This can enhance the capabilities of imaging internal structures for accurate diagnosis and optimized treatments.

On the one hand, the limited photons within the narrow energy windows can result in energy response inconsistency. On the other hand, due to spectral distortions (e.g., charge sharing, K-escape, fluorescence x-ray emission and pulse pileups), the projections of MECT are tarnished by complicated noise. In this case, it is a challenge to find meaningful insights by utilizing these projections for practical applications. Therefore, there are new research opportunities to overcome this issue for higher levels of MECT imaging and applications.

This Special Section on IEEE Access aims to capture the state-of-the-art advances in imaging techniques for MECT and other related research.

The topics of interest include, but are not limited to:

  • MECT image reconstruction
  • MECT image denoising
  • MECT material decomposition
  • MECT hardware development
  • MECT system design
  • MECT image analysis
  • MECT image quality assessment
  • Applications of machine learning in MECT
  • X-ray spectrum estimation for MECT
  • Clinical diagnosis using MECT technique
  • Multi-contrast contrast agent imaging
  • K-edge imaging technique
  • Simulation software package for MECT imaging
  • Scattering correction for MECT
  • Artifacts removal of MECT image
  • Noise estimation models for MECT imaging

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Associate Editor:  Hengyong Yu, University of Massachusetts Lowell, USA

Guest Editors:

    1. Yuemin Zhu, CNRS, University of Lyon, France
    2. Raja Aamir Younis, Khalifa University of Science and Technology UAE


Relevant IEEE Access Special Sections:

  1. Deep Learning Algorithms for Internet of Medical Things
  2. Millimeter-Wave and Terahertz Propagation, Channel Modeling and Applications
  3. Trends and Advances in Bio-Inspired Image-Based Deep Learning Methodologies and Applications


IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:

For inquiries regarding this Special Section, please contact: