ArticleIEEE signal processing magazine2020
Compressed Sensing: From Research to Clinical Practice with Deep Neural Networks.
Article in IEEE signal processing magazine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
50 citing papers in PubMed.
- 3D and 4D Free-Breathing Abdominal T1-Weighted MRI in Clinical Practice Using Deep Learning Auto-Navigation and Reconstruction.Magnetic resonance in medicine · 2026Article
- Optimization of Respiratory Training Methods for Cardiac Magnetic Resonance Imaging.Journal of magnetic resonance imaging : JMRI · 2026Article
- Comparison of Deep-Learning-Based Reconstruction of Knee MRI with Conventional Sequences in Patients with Knee Pain.Diagnostics (Basel, Switzerland) · 2026Article
- FlowVN Trained on a Single Dataset Enables Rapid Reconstruction of Highly Accelerated 4D Flow MRI Across Multiple Sites.Magnetic resonance in medicine · 2026Article
- Deep Learning and Cardiovascular Diseases: An Updated Narrative Review.Journal of clinical medicine · 2026Review
- Deep learning‑based ultra-high-resolution CT imaging of viral pneumonia at admission and after discharge.BMC medical imaging · 2026Article
- Few-shot learning for highly accelerated 3D time-of-flight MRA reconstruction.Magnetic resonance in medicine · 2026Article
- Convolutional recurrent U-net for cardiac cine MRI reconstruction via effective spatio-temporal feature exploitation.Medical physics · 2026Article
- Using deep feature distances for evaluating the perceptual quality of MR image reconstructions.Magnetic resonance in medicine · 2025Article
- Incorporating Radiologist Knowledge Into MRI Quality Metrics for Machine Learning Using Rank-Based Ratings.Journal of magnetic resonance imaging : JMRI · 2025Article
- Compressed Sensing-Accelerated Free-Breathing Liver MRI at 7 T.NMR in biomedicine · 2025Article
- High-Resolution 3D Turbo Spin-Echo Wrist MRI at 7T Accelerated by Compressed Sensing.NMR in biomedicine · 2025Article
- SwinTCS: A Swin Transformer Approach to Compressive Sensing with Non-Local Denoising.Journal of imaging · 2025Article
- Qualitative and Quantitative Evaluation of a Deep Learning-Based Reconstruction for Accelerated Cardiac Cine Imaging.Bioengineering (Basel, Switzerland) · 2025Article
- Let UNet Play an Adversarial Game: Investigating the Effect of Adversarial Training in Enhancing Low-Resolution MRI.Journal of imaging informatics in medicine · 2025Article
- AutoSamp: Autoencoding k-Space Sampling via Variational Information Maximization for 3D MRI.IEEE transactions on medical imaging · 2025Article
- Deep-learning-based image reconstruction with limited data: generating synthetic raw data using deep learning.Magma (New York, N.Y.) · 2024Article
- Advanced MRI techniques in abdominal imaging.Abdominal radiology (New York) · 2024Review
- Enhancing gadoxetic acid-enhanced liver MRI: a synergistic approach with deep learning CAIPIRINHA-VIBE and optimized fat suppression techniques.European radiology · 2024Article
- The "hidden noise" problem in MR image reconstruction.Magnetic resonance in medicine · 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
6 authors.
Funding
Abstract
Compressed sensing (CS) reconstruction methods leverage sparse structure in underlying signals to recover high-resolution images from highly undersampled measurements. When applied to magnetic resonance imaging (MRI), CS has the potential to dramatically shorten MRI scan times, increase diagnostic value, and improve overall patient experience. However, CS has several shortcomings which limit its clinical translation such as: 1) artifacts arising from inaccurate sparse modelling assumptions, 2) extensive parameter tuning required for each clinical application, and 3) clinically infeasible reconstruction times. Recently, CS has been extended to incorporate deep neural networks as a way of learning complex image priors from historical exam data. Commonly referred to as unrolled neural networks, these techniques have proven to be a compelling and practical approach to address the challenges of sparse CS. In this tutorial, we will review the classical compressed sensing formulation and outline steps needed to transform this formulation into a deep learning-based reconstruction framework. Supplementary open source code in Python will be used to demonstrate this approach with open databases. Further, we will discuss considerations in applying unrolled neural networks in the clinical setting.
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.