ArticleJournal of imaging2021
Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications.
Article in Journal of imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction.Tomography (Ann Arbor, Mich.) · 2023Pooled it
- Integrating CT image reconstruction, segmentation, and large language models for enhanced diagnostic insight.Medical & biological engineering & computing · 2026Article
- Learned denoising with simulated and experimental low-dose CT data.Scientific reports · 2025Article
- No Reproducibility, No Progress: Rethinking CT Benchmarking.Journal of imaging · 2025Article
- Data Reconstruction Using Smart Sensor Placement.Sensors (Basel, Switzerland) · 2024Article
- Low-dose CT reconstruction using dataset-free learning.PloS one · 2024Article
- Sinogram domain angular upsampling of sparse-view micro-CT with dense residual hierarchical transformer and attention-weighted loss.Computer methods and programs in biomedicine · 2023Article
- Conditional Invertible Neural Networks for Medical Imaging.Journal of imaging · 2021Article
- Efficient Strike Artifact Reduction Based on 3D-Morphological Structure Operators from Filtered Back-Projection PET Images.Sensors (Basel, Switzerland) · 2021Article
- Improving reproducibility in synchrotron tomography using implementation-adapted filters.Journal of synchrotron radiation · 2021Article
- Deep Learning in Medical Image Analysis.Journal of imaging · 2021Article
- Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications.Journal of imaging · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
The reconstruction of computed tomography (CT) images is an active area of research. Following the rise of deep learning methods, many data-driven models have been proposed in recent years. In this work, we present the results of a
Indexed as
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.