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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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Published in: | IEEE transactions on radiation and plasma medical sciences 2024-02, Vol.8 (2), p.113-137 |
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container_title | IEEE transactions on radiation and plasma medical sciences |
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creator | Bousse, Alexandre Kandarpa, Venkata Sai Sundar Rit, Simon Perelli, Alessandro Li, Mengzhou Wang, Guobao Zhou, Jian Wang, Ge |
description | 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. |
doi_str_mv | 10.1109/TRPMS.2023.3314131 |
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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 |
title | Systematic Review on Learning-Based Spectral CT |
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