Systematic review on learning-based spectral CT

Alexandre Bousse; Venkata Sai Sundar Kandarpa; Simon Rit; Alessandro Perelli; Mengzhou Li; Guobao Wang; Jian Zhou; Ge Wang

doi:10.1109/TRPMS.2023.3314131

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

Biomedical Engineering

Research output: Contribution to journal › Article › peer-review

24 Citations (Scopus)

388 Downloads (Pure)

Abstract

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 language	English
Pages (from-to)	113-137
Number of pages	26
Journal	IEEE Transactions on Radiation and Plasma Medical Sciences
Volume	8
Issue number	2
Early online date	12 Sept 2023
DOIs	https://doi.org/10.1109/TRPMS.2023.3314131
Publication status	Published - Feb 2024

Keywords

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

Access to Document

10.1109/TRPMS.2023.3314131

Author Accepted ManuscriptAccepted author manuscript, 9.39 MBLicence: CC BY

End-to-End Model-Based Deep Learning for Dual-Energy Computed Tomography Material Decomposition
Wang, J. & Perelli, A., 22 Aug 2024, 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 5 p. (Proceedings - International Symposium on Biomedical Imaging).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Cite this

@article{3aa6782a48df496eb268e26e8409ed09,

title = "Systematic review on learning-based spectral CT",

abstract = "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.",

keywords = "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",

author = "Alexandre Bousse and Kandarpa, \{Venkata Sai Sundar\} and Simon Rit and Alessandro Perelli and Mengzhou Li and Guobao Wang and Jian Zhou and Ge Wang",

note = "Copyright: {\textcopyright} 2023 IEEE.",

year = "2024",

month = feb,

doi = "10.1109/TRPMS.2023.3314131",

language = "English",

volume = "8",

pages = "113--137",

journal = "IEEE Transactions on Radiation and Plasma Medical Sciences",

issn = "2469-7303",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "2",

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TY - JOUR

T1 - Systematic review on learning-based spectral CT

AU - Bousse, Alexandre

AU - Kandarpa, Venkata Sai Sundar

AU - Rit, Simon

AU - Perelli, Alessandro

AU - Li, Mengzhou

AU - Wang, Guobao

AU - Zhou, Jian

AU - Wang, Ge

PY - 2024/2

Y1 - 2024/2

N2 - 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.

AB - 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.

KW - Artificial Intelligence (AI)

KW - Biomedical imaging

KW - Computed tomography

KW - Deep Learning

KW - Detectors

KW - Dual-energy CT (DECT)

KW - Image reconstruction

KW - Machine Learning

KW - Photon-counting CT (PCCT)

KW - Photonics

KW - Switches

KW - X-ray imaging

U2 - 10.1109/TRPMS.2023.3314131

DO - 10.1109/TRPMS.2023.3314131

M3 - Article

SN - 2469-7303

VL - 8

SP - 113

EP - 137

JO - IEEE Transactions on Radiation and Plasma Medical Sciences

JF - IEEE Transactions on Radiation and Plasma Medical Sciences

IS - 2

ER -

Systematic review on learning-based spectral CT

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

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Research output

End-to-End Model-Based Deep Learning for Dual-Energy Computed Tomography Material Decomposition

Cite this