ArticleiScience2024
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- An Interpretable Deep-Learning Approach for Efficient CEST Parameter Quantification: Importance-Ranked Saturation Transfer MRI Protocol.Magnetic resonance in medicine · 2026Article
- Optimization of Pulsed Saturation Transfer MR Fingerprinting (ST MRF) Acquisition Using the Cramér-Rao Bound and Sequential Quadratic Programming.Magnetic resonance in medicine · 2026Article
- Quantitative multi-metabolite imaging of Parkinson's disease using AI boosted molecular MRI.Npj imaging · 2025Article
- Harnessing Dynamic Supramolecular Interactions for Lanthanide Detection via Computational Pattern Recognition of Magnetic Resonance Fingerprints.Journal of the American Chemical Society · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Noninvasive magnetic resonance imaging (MRI) of the relayed nuclear Overhauser effect (rNOE) constitutes a promising approach for gaining biological insights into various pathologies, including brain cancer, kidney injury, ischemic stroke, and liver disease. However, rNOE imaging is time-consuming and prone to biases stemming from the water T1 and the semisolid magnetization transfer (MT) contrasts. Here, we developed a rapid rNOE quantification approach, combining magnetic resonance fingerprinting (MRF) acquisition with deep-learning-based reconstruction. The method was systematically validated using tissue-mimicking phantoms, wild-type mice (
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.