Systematic Review on Learning-based Spectral CT.

Journal: IEEE transactions on radiation and plasma medical sciences
Published Date: Sep 12, 2023

Abstract

Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.

Authors

  • Alexandre Bousse
    LaTIM, Inserm UMR 1101, Université de Bretagne Occidentale, 29238 Brest, France.
  • Venkata Sai Sundar Kandarpa
    LaTIM, Inserm UMR 1101, Université de Bretagne Occidentale, 29238 Brest, France.
  • Simon Rit
    Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Étienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F-69373, Lyon, France.
  • Alessandro Perelli
    Department of Biomedical Engineering, School of Science and Engineering, University of Dundee, DD1 4HN, UK.
  • Mengzhou Li
    Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, New York, USA.
  • Guobao Wang
    Department of Radiology, University of California Davis Health, Sacramento, USA.
  • Jian Zhou
    CTIQ, Canon Medical Research USA, Inc., Vernon Hills, 60061, USA.
  • Ge Wang
    Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, New York, USA.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals