ArticleIEEE signal processing magazine2023
Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey.
Article in IEEE signal processing magazine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
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Who cites it
7 citing papers in PubMed.
- Dual-Domain Self-Supervised Deep Learning with Graph Convolution for Low-Dose Computed Tomography Reconstruction.Journal of imaging informatics in medicine · 2025Article
- Low-dose computed tomography image denoising using pixel level non-local self-similarity prior with non-local means for healthcare informatics.Scientific reports · 2025Article
- Dual-Domain deep prior guided sparse-view CT reconstruction with multi-scale fusion attention.Scientific reports · 2025Article
- Self-supervised learning for CT image denoising and reconstruction: a review.Biomedical engineering letters · 2024Review
- Systematic Review on Learning-based Spectral CT.ArXiv · 2024Article
- [A dual-domain cone beam computed tomography reconstruction framework with improved differentiable domain transform for cone-angle artifact correction].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- Systematic Review on Learning-based Spectral CT.IEEE transactions on radiation and plasma medical sciences · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Since 2016, deep learning (DL) has advanced tomographic imaging with remarkable successes, especially in low-dose computed tomography (LDCT) imaging. Despite being driven by big data, the LDCT denoising and pure end-to-end reconstruction networks often suffer from the black box nature and major issues such as instabilities, which is a major barrier to apply deep learning methods in low-dose CT applications. An emerging trend is to integrate imaging physics and model into deep networks, enabling a hybridization of physics/model-based and data-driven elements. In this paper, we systematically review the physics/model-based data-driven methods for LDCT, summarize the loss functions and training strategies, evaluate the performance of different methods, and discuss relevant issues and future directions.
Identifiers
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.