ArticleNMR in biomedicine2017
Biexponential T
Article in NMR in biomedicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 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
40 citing papers in PubMed, 55 citations in OpenAlex.
- Grading knee cartilage degeneration and meniscal injury with combined ACS-accelerated T1ρ and T2 mapping.BMC medical imaging · 2026Article
- Detection of Early Knee Osteoarthritis Using Multi-Component TJournal of magnetic resonance imaging : JMRI · 2026Article
- Early Knee Osteoarthritis Detection by Multi-Component TBioengineering (Basel, Switzerland) · 2026Article
- Feasibility of a UTE Stack-of-Spirals Sequence for TNMR in biomedicine · 2025Article
- HSGDNet: Hybrid Synthetic-Data-Guided Deep Learning With NLS Refinement for Fast Multi-Component T1ρ Knee Mapping.NMR in biomedicine · 2025Article
- Qualitative and Quantitative MR Imaging of the Cartilaginous Endplate: A Review.Journal of magnetic resonance imaging : JMRI · 2025Review
- Optimized MR pulse sequence for high-resolution brain 3D-T1ρ mapping with weighted spin-lock acquisitions.Magnetic resonance in medicine · 2025Article
- Feasibility of a UTE Stack-of-Spirals Sequence for Biexponential TNMR in biomedicine · 2025Article
- Performance of MR learned pulse sequences for 3D bi-exponential, stretched-exponential, and mono-exponential TMagnetic resonance in medicine · 2025Article
- HDNLS: Hybrid Deep-Learning and Non-Linear Least Squares-Based Method for Fast Multi-Component T1ρ Mapping in the Knee Joint.Bioengineering (Basel, Switzerland) · 2024Article
- Novel spin-lock time sampling strategies for improved reproducibility in quantitative T1ρ mapping.NMR in biomedicine · 2024Article
- UltrafastAnalytical chemistry · 2024Article
- Bi-Exponential 3D UTE-T1ρ Relaxation Mapping of Ex Vivo Human Knee Patellar Tendon at 3T.Bioengineering (Basel, Switzerland) · 2024Article
- Optimizing variable flip angles in magnetization-prepared gradient-echo sequences for efficient 3D-T1ρ mapping.Magnetic resonance in medicine · 2023Article
- SuperMAP: Deep ultrafast MR relaxometry with joint spatiotemporal undersampling.Magnetic resonance in medicine · 2023Article
- Data-driven optimization of sampling patterns for MR brain TMagnetic resonance in medicine · 2023Article
- 3D magnetic resonance fingerprinting for rapid simultaneous T1, T2, and T1ρ volumetric mapping of human articular cartilage at 3 T.NMR in biomedicine · 2022Article
- Optimization of spin-lock times for TScientific reports · 2022Article
- Simultaneous bilateral TNMR in biomedicine · 2022Article
- Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends.NMR in biomedicine · 2022Review
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 at 1 institution in 1 country.
Funding
Abstract
The purpose of this study was to demonstrate the feasibility of biexponential T
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.