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Bousse A, Kandarpa VSS, Rit S, Perelli A, Li M, Wang G, Zhou J, Wang G. Systematic Review on Learning-based Spectral CT. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2024; 8:113-137. [PMID: 38476981 PMCID: PMC10927029 DOI: 10.1109/trpms.2023.3314131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
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.
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Affiliation(s)
- Alexandre Bousse
- LaTIM, Inserm UMR 1101, Université de Bretagne Occidentale, 29238 Brest, France
| | | | - Simon Rit
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Étienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F-69373, Lyon, France
| | - Alessandro Perelli
- Department of Biomedical Engineering, School of Science and Engineering, University of Dundee, DD1 4HN, UK
| | - Mengzhou Li
- Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, New York, USA
| | - Guobao Wang
- Department of Radiology, University of California Davis Health, Sacramento, USA
| | - Jian Zhou
- CTIQ, Canon Medical Research USA, Inc., Vernon Hills, 60061, USA
| | - Ge Wang
- Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, New York, USA
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Arridge SR, Ehrhardt MJ, Thielemans K. (An overview of) Synergistic reconstruction for multimodality/multichannel imaging methods. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200205. [PMID: 33966461 DOI: 10.1098/rsta.2020.0205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Imaging is omnipresent in modern society with imaging devices based on a zoo of physical principles, probing a specimen across different wavelengths, energies and time. Recent years have seen a change in the imaging landscape with more and more imaging devices combining that which previously was used separately. Motivated by these hardware developments, an ever increasing set of mathematical ideas is appearing regarding how data from different imaging modalities or channels can be synergistically combined in the image reconstruction process, exploiting structural and/or functional correlations between the multiple images. Here we review these developments, give pointers to important challenges and provide an outlook as to how the field may develop in the forthcoming years. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.
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Affiliation(s)
- Simon R Arridge
- Department of Computer Science, University College London, London, UK
| | - Matthias J Ehrhardt
- Department of Mathematical Sciences, University of Bath, Bath, UK
- Institute for Mathematical Innovation, University of Bath, Bath, UK
| | - Kris Thielemans
- Institute of Nuclear Medicine, University College London, London, UK
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Chen Z, Jamadar SD, Li S, Sforazzini F, Baran J, Ferris N, Shah NJ, Egan GF. From simultaneous to synergistic MR-PET brain imaging: A review of hybrid MR-PET imaging methodologies. Hum Brain Mapp 2018; 39:5126-5144. [PMID: 30076750 DOI: 10.1002/hbm.24314] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Revised: 06/25/2018] [Accepted: 07/02/2018] [Indexed: 12/17/2022] Open
Abstract
Simultaneous Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) scanning is a recent major development in biomedical imaging. The full integration of the PET detector ring and electronics within the MR system has been a technologically challenging design to develop but provides capacity for simultaneous imaging and the potential for new diagnostic and research capability. This article reviews state-of-the-art MR-PET hardware and software, and discusses future developments focusing on neuroimaging methodologies for MR-PET scanning. We particularly focus on the methodologies that lead to an improved synergy between MRI and PET, including optimal data acquisition, PET attenuation and motion correction, and joint image reconstruction and processing methods based on the underlying complementary and mutual information. We further review the current and potential future applications of simultaneous MR-PET in both systems neuroscience and clinical neuroimaging research. We demonstrate a simultaneous data acquisition protocol to highlight new applications of MR-PET neuroimaging research studies.
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Affiliation(s)
- Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia.,Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Victoria, Australia
| | - Sharna D Jamadar
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia.,Monash Institute of Cognitive and Clinical Neuroscience, Monash University, Clayton, Victoria, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Monash University, Clayton, Victoria, Australia
| | - Shenpeng Li
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia.,Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Victoria, Australia
| | | | - Jakub Baran
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia.,Department of Biophysics, Faculty of Mathematics and Natural Sciences, University of Rzeszów, Rzeszów, Poland
| | - Nicholas Ferris
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia.,Monash Imaging, Monash Health, Clayton, Victoria, Australia
| | - Nadim Jon Shah
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia.,Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum, Jülich, Germany
| | - Gary F Egan
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia.,Monash Institute of Cognitive and Clinical Neuroscience, Monash University, Clayton, Victoria, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Monash University, Clayton, Victoria, Australia
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