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Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/jmri.21090openalex-snapshot
Multiecho reconstruction for simultaneous water‐fat decomposition and T2* estimation
inferredcites →https://doi.org/10.1002/jmri.23741openalex-snapshot
Proton density fat‐fraction: A standardized mr‐based biomarker of tissue fat concentration
inferredcites →https://doi.org/10.1002/jmri.26632openalex-snapshot
Rapid automated liver quantitative susceptibility mapping
inferredcites →https://doi.org/10.1002/jmri.26658openalex-snapshot
Water–fat separation and parameter mapping in cardiac MRI via deep learning with a convolutional neural network
inferredcites →https://doi.org/10.1002/mrm.10675openalex-snapshot
Multicoil Dixon chemical species separation with an iterative least‐squares estimation method
inferredcites →https://doi.org/10.1002/mrm.22177openalex-snapshot
Robust water/fat separation in the presence of large field inhomogeneities using a graph cut algorithm
inferredcites →https://doi.org/10.1002/mrm.24369openalex-snapshot
ISMRM workshop on fat–water separation: Insights, applications and progress in MRI
inferredcites →https://doi.org/10.1002/mrm.24441openalex-snapshot
Hierarchical IDEAL: Fast, robust, and multiresolution separation of multiple chemical species from multiple echo times
inferredcites →https://doi.org/10.1002/mrm.25054openalex-snapshot
Liver fat quantification using a multi‐step adaptive fitting approach with multi‐echo GRE imaging
inferredcites →https://doi.org/10.1002/mrm.25163openalex-snapshot
Improving chemical shift encoded water–fat separation using object‐based information of the magnetic field inhomogeneity
inferredcites →https://doi.org/10.1002/mrm.25448openalex-snapshot
Quantitative susceptibility mapping in the abdomen as an imaging biomarker of hepatic iron overload
inferredcites →https://doi.org/10.1002/mrm.27786openalex-snapshot
Separation of water and fat signal in whole‐body gradient echo scans using convolutional neural networks
inferredcites →https://doi.org/10.1002/nbm.4156openalex-snapshot
Quantitative susceptibility mapping of the spine using in‐phase echoes to initialize inhomogeneous field and R2* for the nonconvex optimization problem of fat‐water separation
inferredcites →https://doi.org/10.1016/j.neuroimage.2020.116579openalex-snapshot
Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction
inferredcites →https://doi.org/10.1109/icip.2017.8297024openalex-snapshot
Better than real: Complex-valued neural nets for MRI fingerprinting
inferredcites →https://doi.org/10.1109/tkde.2019.2946162openalex-snapshot
A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective
inferredcites →https://doi.org/10.1109/tmi.2014.2361764openalex-snapshot
Simultaneous Phase Unwrapping and Removal of Chemical Shift (SPURS) Using Graph Cuts: Application in Quantitative Susceptibility Mapping
inferredcites →https://doi.org/10.1109/tmi.2021.3084288openalex-snapshot
Time-Dependent Deep Image Prior for Dynamic MRI
inferredcites →https://doi.org/10.1148/radiol.2017170550openalex-snapshot
Linearity, Bias, and Precision of Hepatic Proton Density Fat Fraction Measurements by Using MR Imaging: A Meta-Analysis
inferredcites →https://doi.org/10.1148/rg.2018170079openalex-snapshot
Liver Iron Quantification with MR Imaging: A Primer for Radiologists
inferredcites →https://doi.org/10.1182/blood-2004-10-3982openalex-snapshot
MRI R2 and R2* mapping accurately estimates hepatic iron concentration in transfusion-dependent thalassemia and sickle cell disease patients
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot
Adam: A Method for Stochastic Optimization
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4740619openalex-extracted
Deep Complex Convolutional Network in Ad-Hoc Reconstruction of Fat and Water Maps Using Multi-Echo GRE in Magnetic Resonance Imaging
inferred← citeshttps://doi.org/10.1002/mp.70101openalex-snapshot
A robust MRI water‐fat separation algorithm based on field map and fat fraction map smoothness
inferred← citeshttps://doi.org/10.1002/mrm.29057openalex-snapshot
QQ‐NET – using deep learning to solve quantitative susceptibility mapping and quantitative blood oxygen level dependent magnitude (QSM+qBOLD or QQ) based oxygen extraction fraction (OEF) mapping
inferred← citeshttps://doi.org/10.1002/mrm.29172openalex-snapshot
Ultrafast water–fat separation using deep learning–based single‐shot MRI
inferred← citeshttps://doi.org/10.1002/mrm.29176openalex-snapshot
Subsecond accurate myelin water fraction reconstruction from FAST‐T2 data with 3D UNET
inferred← citeshttps://doi.org/10.1002/mrm.29265openalex-snapshot
Deep learning–based quantitative susceptibility mapping (QSM) in the presence of fat using synthetically generated multi‐echo phase training data
inferred← citeshttps://doi.org/10.1002/mrm.29525openalex-snapshot
Uncertainty‐aware physics‐driven deep learning network for free‐breathing liver fat and R2* quantification using self‐gated stack‐of‐radial MRI
inferred← citeshttps://doi.org/10.1002/mrm.29644openalex-snapshot
Data adaptive regularization with reference tissue constraints for liver quantitative susceptibility mapping
inferred← citeshttps://doi.org/10.1002/mrm.29774openalex-snapshot
Reducing the ambiguity of field inhomogeneity and chemical shift effect for fat–water separation by field factor
inferred← citeshttps://doi.org/10.1002/mrm.29854openalex-snapshot
mcLARO : Multi‐contrast learned acquisition and reconstruction optimization for simultaneous quantitative multi‐parametric mapping
inferred← citeshttps://doi.org/10.1002/mrm.29860openalex-snapshot
Comparative review of algorithms and methods for chemical‐shift‐encoded quantitative fat‐water imaging
inferred← citeshttps://doi.org/10.1002/mrm.29885openalex-snapshot
Real‐time water/fat imaging at 0. 55T with spiral out‐in‐out‐in sampling
inferred← citeshttps://doi.org/10.1002/mrm.29925openalex-snapshot
Unsupervised deep learning model for correcting Nyquist ghosts of single‐shot spatiotemporal encoding
inferred← citeshttps://doi.org/10.1002/mrm.30401openalex-snapshot
Estimation of fatty acid composition in mammary adipose tissue using deep neural network with unsupervised training
inferred← citeshttps://doi.org/10.1002/nbm.4861openalex-snapshot
GRASPNET: Fast spatiotemporal deep learning reconstruction of golden‐angle radial data for free‐breathing dynamic contrast‐enhanced magnetic resonance imaging
inferred← citeshttps://doi.org/10.1002/nbm.70216openalex-snapshot
HighRes‐Net: Physics‐Informed Deep Learning Framework for High‐Resolution Fat–Water Imaging
inferred← citeshttps://doi.org/10.1002/nbm.70413openalex-snapshot
Fat‐Water Swap Artifact Correction in MRI‐Based Fat Quantification Using Physics‐Informed Deep Learning With Branch Constrained Optimization
inferred← citeshttps://doi.org/10.1007/s00330-023-09576-2openalex-snapshot
Liver PDFF estimation using a multi-decoder water-fat separation neural network with a reduced number of echoes
inferred← citeshttps://doi.org/10.1007/s00330-024-11164-xopenalex-snapshot
Unbiased and reproducible liver MRI-PDFF estimation using a scan protocol-informed deep learning method
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A low-rank deep image prior reconstruction for free-breathing ungated spiral functional CMR at 0.55 T and 1.5 T
inferred← citeshttps://doi.org/10.1007/s10334-025-01268-wopenalex-snapshot
Fat–water MRI separation using deep complex convolution network
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Advanced Wavelet-Driven Neuro-heuristic Computational Scheme for Thermal Analysis of Magnetohydrodynamic Williamson Nanofluid Flow
inferred← citeshttps://doi.org/10.1016/b978-0-443-14109-6.00010-9openalex-snapshot
Map the image: Machine learning for quantitative MR mapping
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A novel radial base artificial intelligence structures with sequential quadratic programming for magnetohydrodynamic nanofluidic model with gold nanoparticles in a stenotic artery
inferred← citeshttps://doi.org/10.1016/j.media.2025.103811openalex-snapshot
Non-iterative and uncertainty-aware MRI-based liver fat estimation using an unsupervised deep learning method
inferred← citeshttps://doi.org/10.1016/j.neuroimage.2023.119886openalex-snapshot
LARO: Learned acquisition and reconstruction optimization to accelerate quantitative susceptibility mapping
inferred← citeshttps://doi.org/10.1038/s41598-021-95972-xopenalex-snapshot
Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions
inferred← citeshttps://doi.org/10.1088/1361-6560/ac46ddopenalex-snapshot
Generation of quantification maps and weighted images from synthetic magnetic resonance imaging using deep learning network
inferred← citeshttps://doi.org/10.1088/1361-6560/ae0efaopenalex-snapshot
Fast water/fat T 2 and PDFF mapping via multiple overlapping-echo detachment acquisition and deep learning reconstruction
inferred← citeshttps://doi.org/10.1109/icipca61593.2024.10709316openalex-snapshot
A Large Model of Medical Neurosurgery Diagnosis and Dreatment Dialogue
inferred← citeshttps://doi.org/10.1109/jbhi.2024.3365784openalex-snapshot
Adaptive Knowledge Distillation for High-Quality Unsupervised MRI Reconstruction With Model-Driven Priors
inferred← citeshttps://doi.org/10.1109/tmi.2022.3180302openalex-snapshot
Hierarchical Multi-Resolution Graph-Cuts for Water-Fat-Silicone Separation in Breast MRI
inferred← citeshttps://doi.org/10.1155/2022/7979523openalex-snapshot
Computed Tomography Image Features under Deep Learning Algorithm Applied in Staging Diagnosis of Bladder Cancer and Detection on Ceramide Glycosylation
inferred← citeshttps://doi.org/10.1155/2023/7626478openalex-snapshot
Neuro‐Heuristic Computational Intelligence Approach for Optimization of Electro‐Magneto‐Hydrodynamic Influence on a Nano Viscous Fluid Flow
inferred← citeshttps://doi.org/10.1186/s40537-022-00677-1openalex-snapshot
Artifact-free fat-water separation in Dixon MRI using deep learning
inferred← citeshttps://doi.org/10.22328/2079-5343-2025-16-3-100-108openalex-snapshot
Studying the capabilities of IDEAL MR-sequence and fat fraction in osteoporosis diagnostics: a retrospective descriptive single-center study
inferred← citeshttps://doi.org/10.3390/app142411924openalex-snapshot
Assessing Pancreatic Fat and Its Correlation with Liver Fat in Suspected MASLD Patients Using Advanced Deep Learning Techniques from MRI Images
inferred← citeshttps://doi.org/10.3390/diagnostics13152582openalex-snapshot
What Is Machine Learning, Artificial Neural Networks and Deep Learning?—Examples of Practical Applications in Medicine
inferred← citeshttps://doi.org/10.6084/m9.figshare.26560955openalex-snapshot
Additional file 1 of Artifact-free fat-water separation in Dixon MRI using deep learning
inferred← citeshttps://doi.org/10.6084/m9.figshare.26560958openalex-snapshot
Additional file 2 of Artifact-free fat-water separation in Dixon MRI using deep learning
inferred← citeshttps://doi.org/10.6084/m9.figshare.26560961openalex-snapshot
Additional file 3 of Artifact-free fat-water separation in Dixon MRI using deep learning
inferred← citeshttps://doi.org/10.6084/m9.figshare.26560964openalex-snapshot
Additional file 4 of Artifact-free fat-water separation in Dixon MRI using deep learning
inferred← citeshttps://doi.org/10.6084/m9.figshare.26560967openalex-snapshot
Additional file 5 of Artifact-free fat-water separation in Dixon MRI using deep learning
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