ArticleProceedings of the IEEE. Institute of Electrical and Electronics Engineers2020
Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning.
Article in Proceedings of the IEEE. Institute of Electrical and Electronics Engineers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 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
65 citing papers in PubMed, 276 citations in OpenAlex.
- Advances in photoacoustic imaging reconstruction and quantitative analysis for biomedical applications.Visual computing for industry, biomedicine, and art · 2026Review
- Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO).IEEE transactions on computational imaging · 2026Article
- A Convergent Generalized Krylov Subspace Method for Compressed Sensing MRI Reconstruction with Gradient-Driven Denoisers.IEEE transactions on computational imaging · 2026Article
- Learning robust parameter inference and density reconstruction in flyer plate impact experiments.Scientific reports · 2025Article
- Sliding volume-based streak artifact reduction network (S-STAR Net) for ultra-sparse-view computed tomography.BMC medical imaging · 2025Article
- A dynamic reconstruction and motion estimation framework for cardiorespiratory motion-resolved real-time volumetric MR imaging (DREME-MR).Physics in medicine and biology · 2025Article
- Prostate MRI Using Deep Learning Reconstruction in Response to Cancer Screening Demands-A Systematic Review and Meta-Analysis.Journal of personalized medicine · 2025Review
- Spatial Adaptive Selection using Binary Conditional Autoregressive Model with Application to Brain-Computer Interface.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025Article
- CNN-Transformer gated fusion network for medical image super-resolution.Scientific reports · 2025Article
- Fast MRI Techniques of the Liver and Pancreaticobiliary Tract: Overview and Application.Journal of the Korean Society of Radiology · 2025Review
- A foundation model for enhancing magnetic resonance images and downstream segmentation, registration and diagnostic tasks.Nature biomedical engineering · 2025Article
- MRI recovery with self-calibrated denoisers without fully-sampled data.Magma (New York, N.Y.) · 2025Article
- Limited-Angle Tomography Using a Neural Network as the Objective Function.International journal of biomedical research & practice · 2025Article
- Sparse-View CT Joint Reconstruction Strategy with Sparse Sampling Encoding LayerCurrent medical imaging · 2025Article
- A review of deep learning-based reconstruction methods for accelerated MRI using spatiotemporal and multi-contrast redundancies.Biomedical engineering letters · 2024Review
- Non-coplanar CBCT image reconstruction using a generative adversarial network for non-coplanar radiotherapy.Journal of applied clinical medical physics · 2024Article
- The Adaption of Recent New Concepts in Neural Radiance Fields and Their Role for High-Fidelity Volume Reconstruction in Medical Images.Sensors (Basel, Switzerland) · 2024Article
- Motion-robust free-running volumetric cardiovascular MRI.Magnetic resonance in medicine · 2024Article
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
- Task-based assessment for neural networks: evaluating undersampled MRI reconstructions based on human observer signal detection.Journal of medical imaging (Bellingham, Wash.) · 2024Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
Abstract
The field of medical image reconstruction has seen roughly four types of methods. The first type tended to be analytical methods, such as filtered back-projection (FBP) for X-ray computed tomography (CT) and the inverse Fourier transform for magnetic resonance imaging (MRI), based on simple mathematical models for the imaging systems. These methods are typically fast, but have suboptimal properties such as poor resolution-noise trade-off for CT. A second type is iterative reconstruction methods based on more complete models for the imaging system physics and, where appropriate, models for the sensor statistics. These iterative methods improved image quality by reducing noise and artifacts. The FDA-approved methods among these have been based on relatively simple regularization models. A third type of methods has been designed to accommodate modified data acquisition methods, such as reduced sampling in MRI and CT to reduce scan time or radiation dose. These methods typically involve mathematical image models involving assumptions such as
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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.