ReviewJournal of magnetic resonance imaging : JMRI2012
Parallel MR imaging.
Review in Journal of magnetic resonance imaging : JMRI, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 241 papers, 2 of them syntheses 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
241 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Systematic Review and Identification of the Challenges of Deep Learning Techniques for Undersampled Magnetic Resonance Image Reconstruction.Sensors (Basel, Switzerland) · 2024Pooled it
- A Review of Translational Magnetic Resonance Imaging in Human and Rodent Experimental Models of Small Vessel Disease.Translational stroke research · 2021Pooled it
- Highly accelerated knee magnetic resonance imaging using deep neural network (DNN)-based reconstruction: prospective, multi-reader, multi-vendor study.Scientific reports · 2023Trial
- Clinical validation of MR imaging time reduction for substitute/synthetic CT generation for prostate MRI-only treatment planning.Physical and engineering sciences in medicine · 2023Trial
- Deep learning reconstruction in prostate MRI: technical principles, clinical applications, and future directions.Abdominal radiology (New York) · 2026Review
- Deep learning reconstruction enables accelerated T2-weighted MRI for rectal cancer staging: a prospective study of diagnostic consistency across NEX value reduction.Abdominal radiology (New York) · 2026Article
- Large-Vessel Venous Signal Confounds Apparent Amygdala Activation in BOLD fMRI.bioRxiv : the preprint server for biology · 2026Article
- Best fast MRI protocols for the knee: advantages and limitations.Skeletal radiology · 2026Review
- 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
- Metamaterial Antennas Enhance MRI of the Eye and Occipital Brain.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Biophysical Diffusion MRI Models Better Identify White Matter Tracts in Edema.Tomography (Ann Arbor, Mich.) · 2026Article
- Dynamic breast MRI with Flexible Temporal Resolution Aided by Deep Learning.Nature communications · 2026Article
- Reducing respiratory motion artifacts in gadoxetate disodium-enhanced magnetic resonance imaging (EOB-MRI): assessing assisted breath-holding effectiveness.Quantitative imaging in medicine and surgery · 2026Article
- AgentMRI: A Vison Language Model-Powered AI System for Self-regulating MRI Reconstruction with Multiple Degradations.Journal of imaging informatics in medicine · 2026Article
- Advances in cartilage imaging techniques.Nature reviews. Rheumatology · 2026Review
- On the Impact of Artifacts Induced by Mismatches Between Auto-Calibration Signal and Accelerated 3D GRE Data at 11.7T.Magnetic resonance in medicine · 2026Article
- Prospective study assessing the validity of accelerated 2D Fast Spin Echo (2D FSE) based high-resolution knee MRI and T2 mapping using deep learning reconstruction.BMC musculoskeletal disorders · 2026Article
- Enhanced muscle MRI using deep learning: shorter acquisition time with improved image quality.PeerJ · 2026Article
- Hemodynamic forces derived from cine cardiac magnetic resonance imaging: technical aspects, applications in various clinical scenarios, and in athlete's heart.Frontiers in cardiovascular medicine · 2026Review
- Comparative evaluation of zoomed and parallel diffusion-weighted imaging: A phantom study.Journal of clinical imaging science · 2026Article
181 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
4 authors.
Funding
Abstract
Parallel imaging is a robust method for accelerating the acquisition of magnetic resonance imaging (MRI) data, and has made possible many new applications of MR imaging. Parallel imaging works by acquiring a reduced amount of k-space data with an array of receiver coils. These undersampled data can be acquired more quickly, but the undersampling leads to aliased images. One of several parallel imaging algorithms can then be used to reconstruct artifact-free images from either the aliased images (SENSE-type reconstruction) or from the undersampled data (GRAPPA-type reconstruction). The advantages of parallel imaging in a clinical setting include faster image acquisition, which can be used, for instance, to shorten breath-hold times resulting in fewer motion-corrupted examinations. In this article the basic concepts behind parallel imaging are introduced. The relationship between undersampling and aliasing is discussed and two commonly used parallel imaging methods, SENSE and GRAPPA, are explained in detail. Examples of artifacts arising from parallel imaging are shown and ways to detect and mitigate these artifacts are described. Finally, several current applications of parallel imaging are presented and recent advancements and promising research in parallel imaging are briefly reviewed.
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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.