Multi-energy CT material decomposition using graph model improved CNN

Shi, Zaifeng; Kong, Fanning; Cheng, Ming; Cao, Huaisheng; Ouyang, Shunxin; Cao, Qingjie

doi:10.1007/s11517-023-02986-w

Cited by 2 publications

(1 citation statement)

References 47 publications

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“…Moreover, current material decomposition techniques suffer from excessive image noise and artifacts due to the dose limit in CT scanning and the noise magnification of the material decomposition process. DLR provides better noise containment for low keV images and AI techniques have been demonstrated that may improve material decomposition performance and detectability of low iodine concentrations [69,71,72]. Additionally, in clinical practice, AI has also shown to be useful in tumor detection, characterization, staging, and prognosis [70,[73][74][75][76].…”

Section: The Future Of Dect Imagingmentioning

confidence: 99%

What to Expect (and What Not) from Dual-Energy CT Imaging Now and in the Future?

García-Figueiras,

Oleaga,

Broncano

et al. 2024

J. Imaging

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Dual-energy CT (DECT) imaging has broadened the potential of CT imaging by offering multiple postprocessing datasets with a single acquisition at more than one energy level. DECT shows profound capabilities to improve diagnosis based on its superior material differentiation and its quantitative value. However, the potential of dual-energy imaging remains relatively untapped, possibly due to its intricate workflow and the intrinsic technical limitations of DECT. Knowing the clinical advantages of dual-energy imaging and recognizing its limitations and pitfalls is necessary for an appropriate clinical use. The aims of this paper are to review the physical and technical bases of DECT acquisition and analysis, to discuss the advantages and limitations of DECT in different clinical scenarios, to review the technical constraints in material labeling and quantification, and to evaluate the cutting-edge applications of DECT imaging, including artificial intelligence, qualitative and quantitative imaging biomarkers, and DECT-derived radiomics and radiogenomics.

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Section: The Future Of Dect Imagingmentioning

confidence: 99%

What to Expect (and What Not) from Dual-Energy CT Imaging Now and in the Future?

García-Figueiras,

Oleaga,

Broncano

et al. 2024

J. Imaging

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Reviewing Material-Sensitive Computed Tomography: From Handcrafted Algorithms to Modern Deep Learning

Weiss,

Meisen

2024

NDT

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Computed tomography (CT) is a widely utilised imaging technique in both clinical and industrial applications. CT scan results, presented as a volume revealing linear attenuation coefficients, are intricately influenced by scan parameters and the sample’s geometry and material composition. Accurately mapping these coefficients to specific materials is a complex task. Traditionally, material decomposition in CT relied on classical algorithms using handcrafted features based on X-ray physics. However, there is a rising trend towards data-driven approaches, particularly deep learning, which offer promising improvements in accuracy and efficiency. This survey explores the transition from classical to data-driven approaches in material-sensitive CT, examining a comprehensive corpus of literature identified through a detailed and reproducible search using Scopus. Our analysis addresses several key research questions: the origin and generation of training datasets, the models and architectures employed, the extent to which deep learning methods reduce the need for domain-specific expertise, and the hardware requirements for training these models. We explore the implications of these findings on the integration of deep learning into CT practices and the potential reduction in the necessity for extensive domain knowledge. In conclusion, this survey highlights a significant shift towards deep learning in material-resolving CT and discusses the challenges and opportunities this presents. The transition suggests a future where data-driven approaches may dominate, offering enhanced precision and robustness in material-resolving CT while potentially transforming the role of domain experts in the field.

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Multi-energy CT material decomposition using graph model improved CNN

Cited by 2 publications

References 47 publications

What to Expect (and What Not) from Dual-Energy CT Imaging Now and in the Future?

What to Expect (and What Not) from Dual-Energy CT Imaging Now and in the Future?

Reviewing Material-Sensitive Computed Tomography: From Handcrafted Algorithms to Modern Deep Learning

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