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Quantitative susceptibility mapping via deep neural networks with iterative reverse concatenations and recurrent modules
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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/cmr.b.20034openalex-snapshot
Application of a Fourier-based method for rapid calculation of field inhomogeneity due to spatial variation of magnetic susceptibility
inferredcites →https://doi.org/10.1002/hbm.25569openalex-snapshot
R2 * and quantitative susceptibility mapping in deep gray matter of 498 healthy controls from 5 to 90 years
inferredcites →https://doi.org/10.1002/jmri.26004openalex-snapshot
Discriminative analysis of regional evolution of iron and myelin/calcium in deep gray matter of multiple sclerosis and healthy subjects
inferredcites →https://doi.org/10.1002/jmri.26850openalex-snapshot
Rapid quantitative susceptibility mapping of intracerebral hemorrhage
inferredcites →https://doi.org/10.1002/mrm.21710openalex-snapshot
Quantitative MR susceptibility mapping using piece‐wise constant regularized inversion of the magnetic field
inferredcites →https://doi.org/10.1002/mrm.21828openalex-snapshot
Calculation of susceptibility through multiple orientation sampling (COSMOS): A method for conditioning the inverse problem from measured magnetic field map to susceptibility source image in MRI
inferredcites →https://doi.org/10.1002/mrm.22135openalex-snapshot
Magnetic susceptibility mapping of brain tissue in vivo using MRI phase data
inferredcites →https://doi.org/10.1002/mrm.24765openalex-snapshot
Background field removal using spherical mean value filtering and Tikhonov regularization
inferredcites →https://doi.org/10.1002/mrm.25358openalex-snapshot
Quantitative susceptibility mapping (QSM): Decoding MRI data for a tissue magnetic biomarker
inferredcites →https://doi.org/10.1002/mrm.27907openalex-snapshot
Cerebral OEF quantification: A comparison study between quantitative susceptibility mapping and dual‐gas calibrated BOLD imaging
inferredcites →https://doi.org/10.1002/mrm.28185openalex-snapshot
The 2016 QSM Challenge: Lessons learned and considerations for a future challenge design
inferredcites →https://doi.org/10.1002/mrm.28716openalex-snapshot
QSM reconstruction challenge 2.0: A realistic in silico head phantom for MRI data simulation and evaluation of susceptibility mapping procedures
inferredcites →https://doi.org/10.1002/mrm.28754openalex-snapshot
QSM reconstruction challenge 2.0: Design and report of results
inferredcites →https://doi.org/10.1002/mrm.30271openalex-snapshot
XSIM : A structural similarity index measure optimized for MRI QSM
inferredcites →https://doi.org/10.1002/nbm.3383openalex-snapshot
Streaking artifact reduction for quantitative susceptibility mapping of sources with large dynamic range
inferredcites →https://doi.org/10.1002/nbm.3569openalex-snapshot
Overview of quantitative susceptibility mapping
inferredcites →https://doi.org/10.1002/nbm.3619openalex-snapshot
Structural and functional quantitative susceptibility mapping from standard fMRI studies
inferredcites →https://doi.org/10.1002/nbm.4292openalex-snapshot
Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities
inferredcites →https://doi.org/10.1002/nbm.4461openalex-snapshot
xQSM: quantitative susceptibility mapping with octave convolutional and noise‐regularized neural networks
inferredcites →https://doi.org/10.1007/978-3-030-00889-5_1openalex-snapshot
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
inferredcites →https://doi.org/10.1007/978-3-030-59713-9_13openalex-snapshot
Learned Proximal Networks for Quantitative Susceptibility Mapping
inferredcites →https://doi.org/10.1016/j.media.2022.102477openalex-snapshot
Unsupervised resolution-agnostic quantitative susceptibility mapping using adaptive instance normalization
inferredcites →https://doi.org/10.1016/j.media.2024.103160openalex-snapshot
Plug-and-Play latent feature editing for orientation-adaptive quantitative susceptibility mapping neural networks
inferredcites →https://doi.org/10.1016/j.neuroimage.2011.08.077openalex-snapshot
MRI estimates of brain iron concentration in normal aging using quantitative susceptibility mapping
inferredcites →https://doi.org/10.1016/j.neuroimage.2011.08.082openalex-snapshot
Morphology enabled dipole inversion for quantitative susceptibility mapping using structural consistency between the magnitude image and the susceptibility map
inferredcites →https://doi.org/10.1016/j.neuroimage.2012.05.049openalex-snapshot
Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study
inferredcites →https://doi.org/10.1016/j.neuroimage.2014.06.011openalex-snapshot
Functional quantitative susceptibility mapping (fQSM)
inferredcites →https://doi.org/10.1016/j.neuroimage.2014.11.010openalex-snapshot
Validation of quantitative susceptibility mapping with Perls' iron staining for subcortical gray matter
inferredcites →https://doi.org/10.1016/j.neuroimage.2014.12.043openalex-snapshot
A method for estimating and removing streaking artifacts in quantitative susceptibility mapping
inferredcites →https://doi.org/10.1016/j.neuroimage.2017.01.007openalex-snapshot
Deep grey matter iron accumulation in alcohol use disorder
inferredcites →https://doi.org/10.1016/j.neuroimage.2018.06.030openalex-snapshot
Quantitative susceptibility mapping using deep neural network: QSMnet
inferredcites →https://doi.org/10.1016/j.neuroimage.2018.06.036openalex-snapshot
Whole head quantitative susceptibility mapping using a least-norm direct dipole inversion method
inferredcites →https://doi.org/10.1016/j.neuroimage.2019.03.060openalex-snapshot
DeepQSM - using deep learning to solve the dipole inversion for quantitative susceptibility mapping
inferredcites →https://doi.org/10.1016/j.neuroimage.2019.116064openalex-snapshot
Learning-based single-step quantitative susceptibility mapping reconstruction without brain extraction
inferredcites →https://doi.org/10.1016/j.neuroimage.2019.116389openalex-snapshot
QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with increased receptive field
inferredcites →https://doi.org/10.1016/j.neuroimage.2020.116619openalex-snapshot
Exploring linearity of deep neural network trained QSM: QSMnet+
inferredcites →https://doi.org/10.1016/j.neuroimage.2021.118376openalex-snapshot
MoDL-QSM: Model-based deep learning for quantitative susceptibility mapping
inferredcites →https://doi.org/10.1016/j.neuroimage.2022.119410openalex-snapshot
Instant tissue field and magnetic susceptibility mapping from MR raw phase using Laplacian enabled deep neural networks
inferredcites →https://doi.org/10.1016/j.neuroimage.2022.119522openalex-snapshot
Towards in vivo ground truth susceptibility for single-orientation deep learning QSM: A multi-orientation gradient-echo MRI dataset
inferredcites →https://doi.org/10.1016/j.neuroimage.2022.119842openalex-snapshot
Affine transformation edited and refined deep neural network for quantitative susceptibility mapping
inferredcites →https://doi.org/10.1016/j.neuroimage.2024.120583openalex-snapshot
Quantitative susceptibility mapping through model-based deep image prior (MoDIP)
inferredcites →https://doi.org/10.1016/j.physd.2019.132306openalex-snapshot
Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network
inferredcites →https://doi.org/10.1088/0031-9155/51/24/007openalex-snapshot
Rapid calculations of susceptibility-induced magnetostatic field perturbations forin vivomagnetic resonance
inferredcites →https://doi.org/10.1088/1361-6560/ad237fopenalex-snapshot
A spatially adaptive regularization based three-dimensional reconstruction network for quantitative susceptibility mapping
inferredcites →https://doi.org/10.1093/brain/aww278openalex-snapshot
The whole-brain pattern of magnetic susceptibility perturbations in Parkinson’s disease
inferredcites →https://doi.org/10.1093/brain/awx137openalex-snapshot
Cerebral quantitative susceptibility mapping predicts amyloid-β-related cognitive decline
inferredcites →https://doi.org/10.1109/icce-taiwan58799.2023.10226714openalex-snapshot
Estimation of Amyloid-β Positivity Using QSM Images Considering Age Information
inferredcites →https://doi.org/10.1109/iccv.2019.00223openalex-snapshot
Recurrent U-Net for Resource-Constrained Segmentation
inferredcites →https://doi.org/10.1109/jbhi.2022.3156548openalex-snapshot
S2Q-Net: Mining the High-Pass Filtered Phase Data in Susceptibility Weighted Imaging for Quantitative Susceptibility Mapping
inferredcites →https://doi.org/10.1109/tmi.2011.2182523openalex-snapshot
Accuracy of the Morphology Enabled Dipole Inversion (MEDI) Algorithm for Quantitative Susceptibility Mapping in MRI
inferredcites →https://doi.org/10.1109/tmi.2014.2358552openalex-snapshot
Quantitative Susceptibility Mapping by Inversion of a Perturbation Field Model: Correlation With Brain Iron in Normal Aging
inferredcites →https://doi.org/10.1109/tmi.2016.2544958openalex-snapshot
Quantitative Susceptibility Mapping Using Structural Feature Based Collaborative Reconstruction(SFCR) in the Human Brain
inferredcites →https://doi.org/10.1148/radiol.2018171918openalex-snapshot
Quantitative Susceptibility Mapping for Following Intracranial Hemorrhage
inferredcites →https://doi.org/10.1148/radiol.2020201603openalex-snapshot
Quantitative Susceptibility Mapping of Brain Iron and β-Amyloid in MRI and PET Relating to Cognitive Performance in Cognitively Normal Older Adults
inferredcites →https://doi.org/10.1161/strokeaha.113.001638openalex-snapshot
Hematoma Volume Measurement in Gradient Echo MRI Using Quantitative Susceptibility Mapping
inferredcites →https://doi.org/10.1364/ao.46.006623openalex-snapshot
Fast and robust three-dimensional best path phase unwrapping algorithm
inferredcites →https://doi.org/10.1371/journal.pone.0081093openalex-snapshot
In Vivo Quantitative Susceptibility Mapping (QSM) in Alzheimer's Disease
inferredcites →https://doi.org/10.1371/journal.pone.0162460openalex-snapshot
Quantitative Susceptibility Mapping in Parkinson's Disease
inferredcites →https://doi.org/10.18383/j.tom.2015.00136openalex-snapshot
Quantitative Susceptibility Mapping: Contrast Mechanisms and Clinical Applications
inferredcites →https://doi.org/10.48550/arxiv.1802.06955openalex-snapshot
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s12194-026-01131-0openalex-snapshot
Does quantitative susceptibility mapping elucidate age-related alterations in deep gray matter iron deposition? A systematic review
inferred← citeshttps://doi.org/10.1109/access.2025.3620427openalex-snapshot
A Fair Comparison in Deep Learning-Based QSM Reconstruction
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