Systematic Review on Learning-Based Spectral CT

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: 1) dual-energy CT (DECT) and 2) photon-counting CT (PCCT), which offer image improvement, material decomposition, an...

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Bibliographic Details
Published in:IEEE transactions on radiation and plasma medical sciences 2024-02, Vol.8 (2), p.113-137
Main Authors: Bousse, Alexandre, Kandarpa, Venkata Sai Sundar, Rit, Simon, Perelli, Alessandro, Li, Mengzhou, Wang, Guobao, Zhou, Jian, Wang, Ge
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial intelligence (AI)
Biomedical imaging
Computed tomography
Computer Science
Deep learning
dual-energy CT (DECT)
Image reconstruction
Image synthesis
Machine learning
Medical Imaging
photon-counting CT (PCCT)
State-of-the-art reviews
X-ray imaging
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Summary: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: 1) dual-energy CT (DECT) and 2) photon-counting CT (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.
ISSN:2469-7311
2469-7303
DOI:10.1109/TRPMS.2023.3314131