ArticleComputational and mathematical methods in medicine2018
Image Decomposition Algorithm for Dual-Energy Computed Tomography via Fully Convolutional Network.
Article in Computational and mathematical methods in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Four-Dimensional Dual-Energy Computed Tomography-Derived Parameters and Their Correlation with Thyroid Gland Functional Status.Tomography (Ann Arbor, Mich.) · 2025Article
- Systematic Review on Learning-based Spectral CT.ArXiv · 2024Article
- Systematic Review on Learning-based Spectral CT.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Opportunistic screening for osteoporosis using enhanced images based on dual-energy computed tomography material decomposition: a comparison with quantitative computed tomography.Quantitative imaging in medicine and surgery · 2024Article
- Tunable Neural Networks for Multi-Material Image Formation from Spectral CT Measurements.Proceedings of SPIE--the International Society for Optical Engineering · 2022Article
- [A nonlocal spectral similarity-induced material decomposition method for noise reduction of dual-energy CT images].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2022Article
- One half-scan dual-energy CT imaging using the Dual-domain Dual-way Estimated Network (DoDa-Net) model.Quantitative imaging in medicine and surgery · 2022Article
- Advances in micro-CT imaging of small animals.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2021Review
- Image-domain Material Decomposition for Spectral CT using a Generalized Dictionary Learning.IEEE transactions on radiation and plasma medical sciences · 2021Article
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6 authors.
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Abstract
backgroundDual-energy computed tomography (DECT) has been widely used due to improved substances identification from additional spectral information. The quality of material-specific image produced by DECT attaches great importance to the elaborated design of the basis material decomposition method.
objectiveThe aim of this work is to develop and validate a data-driven algorithm for the image-based decomposition problem.
methodsA deep neural net, consisting of a fully convolutional net (FCN) and a fully connected net, is proposed to solve the material decomposition problem. The former net extracts the feature representation of input reconstructed images, and the latter net calculates the decomposed basic material coefficients from the joint feature vector. The whole model was trained and tested using a modified clinical dataset.
resultsThe proposed FCN delivers image with about 60% smaller bias and 70% lower standard deviation than the competing algorithms, suggesting its better material separation capability. Moreover, FCN still yields excellent performance in case of photon noise.
conclusionsOur deep cascaded network features high decomposition accuracies and noise robust property. The experimental results have shown the strong function fitting ability of the deep neural network. Deep learning paradigm could be a promising way to solve the nonlinear problem in DECT.
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