Systematic Review on Learning-based Spectral CT - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Radiation and Plasma Medical Sciences Année : 2024

Systematic Review on Learning-based Spectral CT

Venkata Sai Sundar Kandarpa
  • Fonction : Auteur
Simon Rit
Alessandro Perelli
  • Fonction : Auteur
Mengzhou Li
  • Fonction : Auteur
Guobao Wang
  • Fonction : Auteur
Jian Zhou
  • Fonction : Auteur
Ge Wang
  • Fonction : Auteur

Résumé

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.

Dates et versions

hal-04073231 , version 1 (18-04-2023)

Identifiants

Citer

Alexandre Bousse, Venkata Sai Sundar Kandarpa, Simon Rit, Alessandro Perelli, Mengzhou Li, et al.. Systematic Review on Learning-based Spectral CT. IEEE Transactions on Radiation and Plasma Medical Sciences, 2024, 8 (2), pp.133--137. ⟨10.1109/TRPMS.2023.3314131⟩. ⟨hal-04073231⟩
29 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More