ArticleIEEE transactions on medical imaging2017
Iterative Low-Dose CT Reconstruction With Priors Trained by Artificial Neural Network.
Article in IEEE transactions on medical imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 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
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Who cites it
73 citing papers in PubMed.
- Privacy-Preserving Latent Diffusion-Based Synthetic Medical Image Generation.IEEE transactions on medical imaging · 2026Article
- ZS4D: Zero-Shot Self-Similarity-Steered Denoiser for Volumetric Photon-Counting CT.IEEE transactions on radiation and plasma medical sciences · 2026Article
- Enhancing Image Quality in Dental-Maxillofacial CBCT: The Impact of Iterative Reconstruction and AI on Noise Reduction-A Systematic Review.Journal of clinical medicine · 2025Review
- ADMM-TransNet: ADMM-Based Sparse-View CT Reconstruction Method Combining Convolution and Transformer Network.Tomography (Ann Arbor, Mich.) · 2025Article
- A low-dose CT reconstruction method using sub-pixel anisotropic diffusion.Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- HybridFormer: a convolutional neural network-Transformer architecture for low dose computed tomography image denoising.PeerJ. Computer science · 2025Article
- Self-supervised learning for CT image denoising and reconstruction: a review.Biomedical engineering letters · 2024Review
- Learned Tensor Neural Network Texture Prior for Photon-Counting CT Reconstruction.IEEE transactions on medical imaging · 2024Article
- [Artificial intelligence in diagnostic radiology for dose management : Advances and perspectives using the example of computed tomography].Radiologie (Heidelberg, Germany) · 2024Review
- Systematic Review on Learning-based Spectral CT.ArXiv · 2024Article
- CT reconstruction using diffusion posterior sampling conditioned on a nonlinear measurement model.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm.Physics in medicine and biology · 2024Article
- Estimate and compensate head motion in non-contrast head CT scans using partial angle reconstruction and deep learning.Medical physics · 2024Article
- Diffusion Posterior Sampling for Nonlinear CT Reconstruction.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Advancing equitable and personalized cancer care: Novel applications and priorities of artificial intelligence for fairness and inclusivity in the patient care workflow.European journal of cancer (Oxford, England : 1990) · 2024Review
- Systematic Review on Learning-based Spectral CT.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Transferring U-Net between low-dose CT denoising tasks: a validation study with varied spatial resolutions.Quantitative imaging in medicine and surgery · 2024Article
- Proj2Proj: self-supervised low-dose CT reconstruction.PeerJ. Computer science · 2024Article
- Deep Filtered Back Projection for CT Reconstruction.IEEE access : practical innovations, open solutions · 2024Article
- Machine Learned Texture Prior From Full-Dose CT Database via Multi-Modality Feature Selection for Bayesian Reconstruction of Low-Dose CT.IEEE transactions on medical imaging · 2023Article
13 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Dose reduction in computed tomography (CT) is essential for decreasing radiation risk in clinical applications. Iterative reconstruction algorithms are one of the most promising way to compensate for the increased noise due to reduction of photon flux. Most iterative reconstruction algorithms incorporate manually designed prior functions of the reconstructed image to suppress noises while maintaining structures of the image. These priors basically rely on smoothness constraints and cannot exploit more complex features of the image. The recent development of artificial neural networks and machine learning enabled learning of more complex features of image, which has the potential to improve reconstruction quality. In this letter, K-sparse auto encoder was used for unsupervised feature learning. A manifold was learned from normal-dose images and the distance between the reconstructed image and the manifold was minimized along with data fidelity during reconstruction. Experiments on 2016 Low-dose CT Grand Challenge were used for the method verification, and results demonstrated the noise reduction and detail preservation abilities of the proposed method.
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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.