ArticleMagnetic resonance in medicine2022
Multiparametric mapping in the brain from conventional contrast-weighted images using deep learning.
Article in Magnetic resonance in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Two-Minute Deep Learning-Powered Brain Quantitative Mapping: Accelerating Clinical Imaging With Synthetic Magnetic Resonance Imaging.JMIR medical informatics · 2026Article
- Generating Synthetic MR Spectroscopic Imaging Data with Generative Adversarial Networks to Train Machine Learning Models.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- Whole Brain 3D T1 Mapping in Multiple Sclerosis Using Standard Clinical Images Compared to MP2RAGE and MR Fingerprinting.NMR in biomedicine · 2025Article
- Brain tumor enhancement prediction from pre-contrast conventional weighted images using synthetic multiparametric mapping and generative artificial intelligence.Quantitative imaging in medicine and surgery · 2025Article
- DeepEMC-TMagnetic resonance in medicine · 2024Article
- Learning to deep learning: statistics and a paradigm test in selecting a UNet architecture to enhance MRI.Magma (New York, N.Y.) · 2024Article
- A Physics-Based Algorithm to Universally Standardize Routinely Obtained Clinical TAcademic radiology · 2024Article
- Direct synthesis of multi-contrast brain MR images from MR multitasking spatial factors using deep learning.Magnetic resonance in medicine · 2023Article
- Retrospective T2 quantification from conventional weighted MRI of the prostate based on deep learning.Frontiers in radiology · 2023Article
- Three-dimensional simultaneous brain mapping of T1, T2,Magnetic resonance in medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
purposeTo develop a deep-learning-based method to quantify multiple parameters in the brain from conventional contrast-weighted images.
methodsEighteen subjects were imaged using an MR Multitasking sequence to generate reference T
resultsThe deep-learning T
conclusionA deep-learning-based technique was developed to estimate 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.