Systematic review on learning-based spectral CT

Alexandre Bousse (Lead / Corresponding author), Venkata Sai Sundar Kandarpa, Simon Rit, Alessandro Perelli, Mengzhou Li, Guobao Wang, Jian Zhou, Ge Wang

Research output: Contribution to journalArticlepeer-review

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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.
Original languageEnglish
Pages (from-to)113-137
Number of pages26
JournalIEEE Transactions on Radiation and Plasma Medical Sciences
Issue number2
Early online date12 Sept 2023
Publication statusPublished - Feb 2024


  • Artificial Intelligence (AI)
  • Biomedical imaging
  • Computed tomography
  • Deep Learning
  • Detectors
  • Dual-energy CT (DECT)
  • Image reconstruction
  • Machine Learning
  • Photon-counting CT (PCCT)
  • Photonics
  • Switches
  • X-ray imaging

ASJC Scopus subject areas

  • Instrumentation
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


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