ArticleMagnetic resonance in medicine2019
Rapid T
Article in Magnetic resonance in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed.
- Phase-Pole-Free Images and Smooth Coil Sensitivity Maps by Regularized Nonlinear Inversion.Magnetic resonance in medicine · 2026Article
- Dynamic Transitions for Fast Joint Acquisition and Reconstruction of CEST-Magnetic resonance in medicine · 2026Article
- Compressed sensing acceleration of radial 3-D alternating Look-LockerMagnetic resonance in medicine · 2025Article
- Diffusion Modeling With Domain-Conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction.IEEE transactions on medical imaging · 2025Article
- Focused ultrasound-induced blood-brain barrier opening: A comparative analysis of permeability quantification based onMagnetic resonance in medicine · 2025Article
- MR-zero meets FLASH - controlling the transient signal decay in gradient- and RF-spoiled gradient echo sequences.Magnetic resonance in medicine · 2025Article
- POSE: POSition Encoding for accelerated quantitative MRI.Magnetic resonance imaging · 2024Article
- FastMagnetic resonance letters · 2024Article
- Fast Whole-Brain MR Multi-Parametric Mapping with Scan-Specific Self-Supervised Networks.ArXiv · 2024Article
- Improving quantitative MRI using self-supervised deep learning with model reinforcement: Demonstration for rapid T1 mapping.Magnetic resonance in medicine · 2024Article
- Joint MAPLE: Accelerated joint T1 and T2∗ mapping with scan-specific self-supervised networksMagnetic resonance in medicine · 2024Article
- Model-based reconstructions for intravoxel incoherent motion and diffusion tensor imaging parameter map estimations.NMR in biomedicine · 2023Article
- MP-RAVE: IR-Prepared TMagnetic resonance in medicine · 2023Article
- Free-breathing myocardial TMagnetic resonance in medicine · 2023Article
- A 3-slice cardiac quantitative native and post-contrast T1 and T2 MRI protocol requiring only four BHs using a 72-channel receive array coil.Frontiers in cardiovascular medicine · 2023Article
- Repeatability and robustness of MP-GRASP TMagnetic resonance in medicine · 2022Article
- A review and experimental evaluation of deep learning methods for MRI reconstruction.The journal of machine learning for biomedical imaging · 2022Article
- Sparse precontrast TMagnetic resonance in medicine · 2021Article
- Joint multi-field TMagnetic resonance in medicine · 2021Article
- Fat-saturated image generation from multi-contrast MRIs using generative adversarial networks with Bloch equation-based autoencoder regularization.Medical image analysis · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
purposeMagnetic resonance imaging protocols for the assessment of quantitative information suffer from long acquisition times since multiple measurements in a parametric dimension are required. To facilitate the clinical applicability, accelerating the acquisition is of high importance. To this end, we propose a model-based optimization framework in conjunction with undersampling 3D radial stack-of-stars data. THEORY AND
methodsHigh resolution 3D T
resultsEvaluation of the proposed method using numerical simulations and phantom scans shows excellent quantitative agreement and image quality. T
conclusionsThe proposed algorithm is able to recover T
Indexed as
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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.