ReviewQuantitative imaging in medicine and surgery2018
A survey of GPU-based acceleration techniques in MRI reconstructions.
Review in Quantitative imaging in medicine and surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- Non-Subtractive Arterial Spin Labeling-Based (NSASL) Renal Magnetic Resonance Angiography (MRA): Development and Clinical Feasibility Evaluation.Journal of magnetic resonance imaging : JMRI · 2026Article
- Accelerated susceptibility-driven positive contrast MRI reconstruction based on primal-dual optimization with minimal parameter tuning.Quantitative imaging in medicine and surgery · 2026Article
- Volumetric thermometry in moving tissues using stack-of-radial MRI and an image-navigated multi-baseline proton resonance frequency shift method.Magnetic resonance in medicine · 2026Article
- Modeling macroscopic brain dynamics with brain-inspired computing architecture.Nature communications · 2025Article
- A review of deep learning-based reconstruction methods for accelerated MRI using spatiotemporal and multi-contrast redundancies.Biomedical engineering letters · 2024Review
- Acceleration of Hyperspectral Skin Cancer Image Classification through Parallel Machine-Learning Methods.Sensors (Basel, Switzerland) · 2024Article
- Multi-Device Parallel MRI Reconstruction: Efficient Partitioning for Undersampled 5D Cardiac CINE.Sensors (Basel, Switzerland) · 2024Article
- Retrospective motion correction through multi-average k-space data elimination (REMAKE) for free-breathing cardiac cine imaging.Magnetic resonance in medicine · 2023Article
- Energy-efficient high-fidelity image reconstruction with memristor arrays for medical diagnosis.Nature communications · 2023Article
- Real-time radial reconstruction with domain transform manifold learning for MRI-guided radiotherapy.Medical physics · 2023Article
- Current applications and future directions of deep learning in musculoskeletal radiology.Skeletal radiology · 2020Review
- Diagnosability of Synthetic Retinal Fundus Images for Plus Disease Detection in Retinopathy of Prematurity.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Demystifying the Jargon: The Bridge between Ophthalmology and Artificial Intelligence.Ophthalmology. Retina · 2019Article
- Respiratory-Correlated (RC) vs. Time-Resolved (TR) Four-Dimensional Magnetic Resonance Imaging (4DMRI) for Radiotherapy of Thoracic and Abdominal Cancer.Frontiers in oncology · 2019Review
- Simultaneous multi slice (SMS) balanced steady state free precession first-pass myocardial perfusion cardiovascular magnetic resonance with iterative reconstruction at 1.5 T.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2018Article
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
No grant is acknowledged in the PubMed record.
Abstract
Image reconstruction in magnetic resonance imaging (MRI) clinical applications has become increasingly more complicated. However, diagnostic and treatment require very fast computational procedure. Modern competitive platforms of graphics processing unit (GPU) have been used to make high-performance parallel computations available, and attractive to common consumers for computing massively parallel reconstruction problems at commodity price. GPUs have also become more and more important for reconstruction computations, especially when deep learning starts to be applied into MRI reconstruction. The motivation of this survey is to review the image reconstruction schemes of GPU computing for MRI applications and provide a summary reference for researchers in MRI community.
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.