ArticleMagnetic resonance in medicine2019
MANTIS: Model-Augmented Neural neTwork with Incoherent k-space Sampling for efficient MR parameter mapping.
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 60 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
60 citing papers in PubMed.
- DeepRelaxo: Fast Mono-Exponential Magnitude Brain R2* Mapping With Reduced Echoes Using Self-Supervised Deep Learning.Magnetic resonance in medicine · 2026Article
- Hybrid learning: a combination of self-supervised and supervised learning for joint MRI reconstruction and denoising in low-field MRI.Physics in medicine and biology · 2026Article
- Accelerating Multiparametric Quantitative MRI Using Self-Supervised Scan-Specific Implicit Neural Representation With Model Reinforcement.Magnetic resonance in medicine · 2026Article
- Domain-Conditioned and Temporal-Guided Diffusion Modeling for Accelerated Dynamic MRI Reconstruction.NMR in biomedicine · 2026Article
- Multi-contrast generation and quantitative MRI using a transformer-based framework with RF excitation embeddings.Communications biology · 2025Article
- Model-based deep learning with fully connected neural networks for accelerated magnetic resonance parameter mapping.International journal of computer assisted radiology and surgery · 2025Article
- Accelerated free-breathing abdominal T2 mapping with deep learning reconstruction of radial turbo spin-echo data.Magnetic resonance in medicine · 2025Article
- Fast and High-Resolution luminal water imaging for prostate cancer diagnosis.Magnetic resonance in medicine · 2025Article
- Diffusion Modeling With Domain-Conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction.IEEE transactions on medical imaging · 2025Article
- HSGDNet: Hybrid Synthetic-Data-Guided Deep Learning With NLS Refinement for Fast Multi-Component T1ρ Knee Mapping.NMR in biomedicine · 2025Article
- Simultaneous 3D quantitative magnetization transfer imaging and susceptibility mapping.Magnetic resonance in medicine · 2025Article
- Metrology for MRI: the field you've never heard of.Magma (New York, N.Y.) · 2025Review
- CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping.Journal of mathematical imaging and vision · 2025Article
- SuperMRF: deep robust reconstruction for highly accelerated magnetic resonance fingerprinting.Quantitative imaging in medicine and surgery · 2025Article
- Ground-truth-free deep learning approach for accelerated quantitative parameter mapping with memory efficient learning.PloS one · 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
- DeepEMC-TMagnetic resonance in medicine · 2024Article
- Deep Convolutional Neural Network for Dedicated Regions-of-Interest Based Multi-Parameter Quantitative Ultrashort Echo Time (UTE) Magnetic Resonance Imaging of the Knee Joint.Journal of imaging informatics in medicine · 2024Article
- Fast Whole-Brain MR Multi-Parametric Mapping with Scan-Specific Self-Supervised Networks.ArXiv · 2024Article
- Improved quantitative parameter estimation for prostate TMagma (New York, N.Y.) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
purposeTo develop and evaluate a novel deep learning-based image reconstruction approach called MANTIS (Model-Augmented Neural neTwork with Incoherent k-space Sampling) for efficient MR parameter mapping.
methodsMANTIS combines end-to-end convolutional neural network (CNN) mapping, incoherent k-space undersampling, and a physical model as a synergistic framework. The CNN mapping directly converts a series of undersampled images straight into MR parameter maps using supervised training. Signal model fidelity is enforced by adding a pathway between the undersampled k-space and estimated parameter maps to ensure that the parameter maps produced synthesized k-space consistent with the acquired undersampling measurements. The MANTIS framework was evaluated on the T
resultsMANTIS achieved high-quality T
conclusionThe MANTIS framework, with a combination of end-to-end CNN mapping, signal model-augmented data consistency, and incoherent k-space sampling, is a promising approach for efficient and robust estimation of quantitative MR parameters.
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.