ReviewThe British journal of radiology2015
Compressed sensing MRI: a review of the clinical literature.
Review in The British journal of radiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 153 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
153 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of compressed-sensing cardiac accelerated cine MR in evaluating left ventricular function: a systematic review and meta-analysis.BMC medical imaging · 2026Pooled it
- Highly accelerated knee magnetic resonance imaging using deep neural network (DNN)-based reconstruction: prospective, multi-reader, multi-vendor study.Scientific reports · 2023Trial
- Validation of Compressed Sensing Accelerated Magnetization Prepared Rapid Acquisition Gradient Echo for Fast and Reliable Brain Volume Measurement.Tomography (Ann Arbor, Mich.) · 2026Article
- Characterizing electronic noise interference from the external laser positioning system on a radiation therapy MRI simulation scanner.Journal of applied clinical medical physics · 2026Article
- Deep learning reconstruction versus conventional parallel imaging for sagittal T2-weighted MRI in cervical cancer: a comparison of acceleration factors.Abdominal radiology (New York) · 2026Article
- Hybrid learning: a combination of self-supervised and supervised learning for joint MRI reconstruction and denoising in low-field MRI.Physics in medicine and biology · 2026Article
- Feasibility of deep learning-accelerated ultrafast T1-weighted VIBE Dixon imaging of the pelvis for screening of metastases in prostate MRI.European radiology experimental · 2026Article
- The effect of head coil configuration and channel count on the quality of double inversion recovery (DIR) MRI images.BMC medical imaging · 2026Article
- Clinical evaluation of deep learning accelerated lumbar T2-weighted and fat-suppressed MRI sequences.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Ultrafast MRI in BI-RADS 4 Masses: The Fast Lane to Clarity.The Indian journal of radiology & imaging · 2026Article
- The comparison of MRI and CT protocol examination times for mechanical thrombectomy in acute ischemic stroke.Radiological physics and technology · 2025Article
- Applying artificial intelligence to cardiac MRI to diagnose congenital heart disease in low-resource settings such as Sub-Saharan Africa.Communications medicine · 2025Review
- Robust multi-coil MRI reconstruction via self-supervised denoising.Magnetic resonance in medicine · 2025Article
- A multinational study of deep learning-based image enhancement for multiparametric glioma MRI.Scientific reports · 2025Article
- Fast 3D wheel acquisition versus conventional parallel imaging in cerebral magnetic resonance angiography for the evaluation of intracranial aneurysms : Original Article.Neuroradiology · 2025Article
- Deep learning MRI halves scan time and preserves image quality across routine neuroradiologic examinations.Radiology advances · 2025Article
- Hemodynamic Characterization of Peripheral Arterio-Venous Malformations Using Rapid Contrast-Enhanced MR Imaging: An In Vitro and In Vivo Study.Annals of biomedical engineering · 2025Article
- Accelerated Abdominal MRI: A Review of Current Methods and Applications.Journal of magnetic resonance imaging : JMRI · 2025Review
- Compressed Sensing Reconstruction with Zero-Shot Self-Supervised Learning for High-Resolution MRI of Human Embryos.Tomography (Ann Arbor, Mich.) · 2025Article
- Artificial intelligence-assisted compressed sensing CINE enhances the workflow of cardiac magnetic resonance in challenging patients.World journal of cardiology · 2025Article
93 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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MRI is one of the most dynamic and safe imaging techniques available in the clinic today. However, MRI acquisitions tend to be slow, limiting patient throughput and limiting potential indications for use while driving up costs. Compressed sensing (CS) is a method for accelerating MRI acquisition by acquiring less data through undersampling of k-space. This has the potential to mitigate the time-intensiveness of MRI. The limited body of research evaluating the effects of CS on MR images has been mostly positive with regards to its potential as a clinical tool. Studies have successfully accelerated MRI with this technology, with varying degrees of success. However, more must be performed before its diagnostic efficacy and benefits are clear. Studies involving a greater number radiologists and images must be completed, rating CS based on its diagnostic efficacy. Also, standardized methods for determining optimal imaging parameters must be developed.
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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.