Systematic Review on Learning-Based Spectral CT

被引：1

作者：

Bousse, Alexandre ^{[1
]}

Kandarpa, Venkata Sai Sundar ^{[1
]}

Rit, Simon ^{[2
]}

Perelli, Alessandro ^{[3
]}

Li, Mengzhou ^{[4
]}

Wang, Guobao ^{[5
]}

Zhou, Jian ^{[6
]}

Wang, Ge ^{[4
]}

机构：

[1] Univ Bretagne Occidentale, LaTIM, Inserm, UMR 1101, F-29238 Brest, France

[2] Univ Claude Bernard Lyon 1, Univ Lyon, INSA Lyon, UJM St Etienne,CNRS,CREATIS,UMR 5220,U1294, F-69373 Lyon, France

[3] Univ Dundee, Sch Sci & Engn, Dept Biomed Engn, Dundee DD1 4HN, Scotland

[4] Rensselaer Polytech Inst, Biomed Imaging Ctr, Troy, NY 12180 USA

[5] Univ Calif Davis Hlth, Dept Radiol, Sacramento, CA 95817 USA

[6] Canon Med Res USA Inc, CTIQ, Vernon Hills, IL 60061 USA

来源：

IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES | 2024年 / 8卷 / 02期

关键词：

Artificial intelligence (AI); deep learning; dual-energy CT (DECT); machine learning; photon-counting CT (PCCT); LOW-DOSE CT; DUAL-ENERGY CT; X-RAY CT; STATISTICAL IMAGE-RECONSTRUCTION; GENERATIVE ADVERSARIAL NETWORK; COMPUTED-TOMOGRAPHY; MULTIMATERIAL DECOMPOSITION; OVERCOMPLETE DICTIONARIES; ALGORITHM; PERFORMANCE;

D O I：

10.1109/TRPMS.2023.3314131

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

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.

引用

页码：113 / 137

页数：25

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