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

共 298 条

[1] Nonlinear material decomposition using a regularized iterative scheme based on the Bregman distance
Abascal, J. F. P. J.
Ducros, N.
Peyrin, F.
[J]. INVERSE PROBLEMS, 2018, 34 (12)
[2] Material Decomposition in Spectral CT Using Deep Learning: A Sim2Real Transfer Approach
Abascal, Juan F. P. J.
Ducros, Nicolas
Pronina, Valeriya
Rit, Simon
Rodesch, Pierre-Antoine
Broussaud, Thomas
Bussod, Suzanne
Douek, Philippe C.
Hauptmann, Andreas
Arridge, Simon
Peyrin, Francoise
[J]. IEEE ACCESS, 2021, 9 : 25632 - 25647
[3] Spectral CT of the abdomen: Where are we now?
Adam, Sharon Z.
Rabinowich, Aviad
Kessner, Rivka
Blachar, Arye
[J]. INSIGHTS INTO IMAGING, 2021, 12 (01)
[4] Learned Primal-Dual Reconstruction
Adler, Jonas
Oktem, Ozan
[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING, 2018, 37 (06) : 1322 - 1332
[5] K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation
Aharon, Michal
Elad, Michael
Bruckstein, Alfred
[J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2006, 54 (11) : 4311 - 4322
[6] ENERGY-SELECTIVE RECONSTRUCTIONS IN X-RAY COMPUTERIZED TOMOGRAPHY
ALVAREZ, RE
MACOVSKI, A
[J]. PHYSICS IN MEDICINE AND BIOLOGY, 1976, 21 (05) : 733 - 744
[7] Dimensionality and noise in energy selective x-ray imaging
Alvarez, Robert E.
[J]. MEDICAL PHYSICS, 2013, 40 (11)
[8] [Anonymous], 2009, P 26 ANN INT C MACH, DOI DOI 10.1145/1553374.1553463
[9] Solving inverse problems using data-driven models
Arridge, Simon
Maass, Peter
Oktem, Ozan
Schonlieb, Carola-Bibiane
[J]. ACTA NUMERICA, 2019, 28 : 1 - 174
[10] (An overview of) Synergistic reconstruction for multimodality/multichannel imaging methods
Arridge, Simon R.
Ehrhardt, Matthias J.
Thielemans, Kris
[J]. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 2021, 379 (2200):

← 1 2 3 4 5 6 7 8 9 10 →