ArticleScientific reports2019
Physics-informed Deep Learning for Dual-Energy Computed Tomography Image Processing.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- A Physics-Informed Neural Network forResearch square · 2026Article
- Artificial intelligence and its potential integration with the clinical practice of diagnostic imaging medical physicists: a review.Physical and engineering sciences in medicine · 2025Review
- A Review of Photon-Counting Computed Tomography (PCCT) in the Diagnosis of Cardiovascular Diseases.Cureus · 2024Review
- 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
- Non-contrast dual-energy CT using X-map for acute ischemic stroke: region-specific comparison with simulated 120-kVp CT and diffusion-weighted MR images.Japanese journal of radiology · 2024Article
- PhysVENeT: a physiologically-informed deep learning-based framework for the synthesis of 3D hyperpolarized gas MRI ventilation.Scientific reports · 2023Article
- Di-CNN: Domain-Knowledge-Informed Convolutional Neural Network for Manufacturing Quality Prediction.Sensors (Basel, Switzerland) · 2023Article
- Multi-energy CT material decomposition using Bayesian deep convolutional neural network with explicit penalty of uncertainty and bias.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Deep learning-based virtual noncalcium imaging in multiple myeloma using dual-energy CT.Medical physics · 2022Article
- Tunable Neural Networks for Multi-Material Image Formation from Spectral CT Measurements.Proceedings of SPIE--the International Society for Optical Engineering · 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
- Photon Counting CT: Clinical Applications and Future Developments.IEEE transactions on radiation and plasma medical sciences · 2021Article
- Evaluating renal lesions using deep-learning based extension of dual-energy FoV in dual-source CT-A retrospective pilot study.European journal of radiology · 2021Article
- Noise reduction approach in pediatric abdominal CT combining deep learning and dual-energy technique.European radiology · 2021Article
- Deep learning based spectral extrapolation for dual-source, dual-energy x-ray computed tomography.Medical physics · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dual-energy CT (DECT) was introduced to address the inability of conventional single-energy computed tomography (SECT) to distinguish materials with similar absorbances but different elemental compositions. However, material decomposition algorithms based purely on the physics of the underlying attenuation process have several limitations, leading to low signal-to-noise ratio (SNR) in the derived material-specific images. To overcome these, we trained a convolutional neural network (CNN) to develop a framework to reconstruct non-contrast SECT images from DECT scans. We show that the traditional physics-based decomposition algorithms do not bring to bear the full information content of the image data. A CNN that leverages the underlying physics of the DECT image generation process as well as the anatomic information gleaned via training with actual images can generate higher fidelity processed DECT images.
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Registered trials
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