ArticlePatterns (New York, N.Y.)2020
Virtual Monoenergetic CT Imaging via Deep Learning.
Article in Patterns (New York, N.Y.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 47 citations in OpenAlex.
- Deep Learning-based Monoenergetic Imaging for Calcified Coronary Stenosis Assessment at Energy-integrating Detector CT.Radiology. Cardiothoracic imaging · 2026Article
- Canadian radiology: 2025 update.Japanese journal of radiology · 2025Review
- Dual-energy CT-based virtual monoenergetic imaging via unsupervised learning.Physical and engineering sciences in medicine · 2025Article
- Synthetic Low-Energy Monochromatic Image Generation in Single-Energy Computed Tomography System Using a Transformer-Based Deep Learning Model.Journal of imaging informatics in medicine · 2024Article
- Systematic Review on Learning-based Spectral CT.ArXiv · 2024Article
- Dual-energy computed tomography with new virtual monoenergetic image reconstruction enhances prostate lesion image quality and improves the diagnostic efficacy for prostate cancer.BMC medical imaging · 2024Article
- Synthesis of virtual monoenergetic images from kilovoltage peak images using wavelet loss enhanced CycleGAN for improving radiomics features reproducibility.Quantitative imaging in medicine and surgery · 2024Article
- Artificial intelligence-based image-domain material decomposition in single-energy computed tomography for head and neck cancer.International journal of computer assisted radiology and surgery · 2024Article
- Development of A Micro-CT Scanner with Dual-Energy Option and Endovascular Contrast Agent Administration Protocol for Fetal and Neonatal Virtual Autopsy.Journal of imaging · 2024Article
- Systematic Review on Learning-based Spectral CT.IEEE transactions on radiation and plasma medical sciences · 2024Article
- A quality-checked and physics-constrained deep learning method to estimate material basis images from single-kV contrast-enhanced chest CT scans.Medical physics · 2023Article
- A multienergy computed tomography method without image segmentation or prior knowledge of X-ray spectra or materials.Heliyon · 2022Article
- Stabilizing deep tomographic reconstruction: Part A. Hybrid framework and experimental results.Patterns (New York, N.Y.) · 2022Article
- Jointly Optimized Deep Neural Networks to Synthesize Monoenergetic Images from Single-Energy CT Angiography for Improving Classification of Pulmonary Embolism.Diagnostics (Basel, Switzerland) · 2022Article
- Estimating optical parameters of biological tissues with photon-counting micro-CT.Journal of the Optical Society of America. A, Optics, image science, and vision · 2022Article
- Deep Learning and Domain-Specific Knowledge to Segment the Liver from Synthetic Dual Energy CT Iodine Scans.Diagnostics (Basel, Switzerland) · 2022Article
- Projection decomposition via univariate optimization for dual-energy CT.Journal of X-ray science and technology · 2022Article
- Feasibility analysis on simultaneous electron density and attenuation coefficient reconstruction.Medical physics · 2021Article
- 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
- Random Search as a Neural Network Optimization Strategy for Convolutional-Neural-Network (CNN)-based Noise Reduction in CT.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Conventional single-spectrum computed tomography (CT) reconstructs a spectrally integrated attenuation image and reveals tissues morphology without any information about the elemental composition of the tissues. Dual-energy CT (DECT) acquires two spectrally distinct datasets and reconstructs energy-selective (virtual monoenergetic [VM]) and material-selective (material decomposition) images. However, DECT increases system complexity and radiation dose compared with single-spectrum CT. In this paper, a deep learning approach is presented to produce VM images from single-spectrum CT images. Specifically, a modified residual neural network (ResNet) model is developed to map single-spectrum CT images to VM images at pre-specified energy levels. This network is trained on clinical DECT data and shows excellent convergence behavior and image accuracy compared with VM images produced by DECT. The trained model produces high-quality approximations of VM images with a relative error of less than 2%. This method enables multi-material decomposition into three tissue classes, with accuracy comparable with DECT.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.