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xQSM: quantitative susceptibility mapping with octave convolutional and noise‐regularized neural networks
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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/jmri.25143openalex-snapshot
Quantitative Susceptibility Mapping of Intracerebral Hemorrhages at Various Stages
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/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.26331openalex-snapshot
Preconditioned total field inversion (TFI) method for quantitative susceptibility mapping
inferredcites →https://doi.org/10.1002/nbm.1670openalex-snapshot
A novel background field removal method for MRI using projection onto dipole fields (PDF)
inferredcites →https://doi.org/10.1002/nbm.3064openalex-snapshot
Background field removal by solving the Laplacian boundary value problem
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.3540openalex-snapshot
Susceptibility tensor imaging (STI) of the brain
inferredcites →https://doi.org/10.1002/nbm.4271openalex-snapshot
Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM)
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/10.1007/978-3-319-24574-4_28openalex-snapshot
U-Net: Convolutional Networks for Biomedical Image Segmentation
inferredcites →https://doi.org/10.1016/j.msard.2019.05.028openalex-snapshot
Five year iron changes in relapsing-remitting multiple sclerosis deep gray matter compared to healthy controls
inferredcites →https://doi.org/10.1016/j.neuroimage.2010.10.070openalex-snapshot
Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: An approach to in vivo brain iron metabolism?
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.2014.12.043openalex-snapshot
A method for estimating and removing streaking artifacts in quantitative susceptibility mapping
inferredcites →https://doi.org/10.1016/j.neuroimage.2015.02.041openalex-snapshot
Fast quantitative susceptibility mapping using 3D EPI and total generalized variation
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/10.1038/nature25988openalex-snapshot
Image reconstruction by domain-transform manifold learning
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/10.1109/cvpr.2016.182openalex-snapshot
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.634openalex-snapshot
Aggregated Residual Transformations for Deep Neural Networks
inferredcites →https://doi.org/10.1109/iccv.2019.00353openalex-snapshot
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks With Octave Convolution
inferredcites →https://doi.org/10.1109/jproc.2019.2936204openalex-snapshot
Image Reconstruction: From Sparsity to Data-Adaptive Methods and Machine Learning
inferredcites →https://doi.org/10.1109/msp.2017.2739299openalex-snapshot
Convolutional Neural Networks for Inverse Problems in Imaging: A Review
inferredcites →https://doi.org/10.1109/tip.2017.2662206openalex-snapshot
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
inferredcites →https://doi.org/10.1109/tip.2017.2713099openalex-snapshot
Deep Convolutional Neural Network for Inverse Problems in Imaging
inferredcites →https://doi.org/10.1109/tmi.2019.2927101openalex-snapshot
${k}$ -Space Deep Learning for Accelerated MRI
inferredcites →https://doi.org/10.1109/tmm.2019.2947352openalex-snapshot
A Dilated Inception Network for Visual Saliency Prediction
inferredcites →https://doi.org/10.1148/radiol.12120707openalex-snapshot
Quantitative Susceptibility Mapping in Multiple Sclerosis
inferredcites →https://doi.org/10.1148/radiol.2018171918openalex-snapshot
Quantitative Susceptibility Mapping for Following Intracranial Hemorrhage
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/10.18383/j.tom.2015.00136openalex-snapshot
Quantitative Susceptibility Mapping: Contrast Mechanisms and Clinical Applications
inferredcites →https://doi.org/10.3156/jsoft.29.5_177_2openalex-snapshot
GAN（Generative Adversarial Nets）
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot
Adam: A Method for Stochastic Optimization
inferredcites →https://doi.org/10.7939/r32j68j2qopenalex-snapshot
Background field removal using spherical mean value filtering and Tikhonov regularization
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/mp.17747openalex-snapshot
Quantitative susceptibility mapping via deep neural networks with iterative reverse concatenations and recurrent modules
inferred← citeshttps://doi.org/10.1002/mrm.28754openalex-snapshot
QSM reconstruction challenge 2.0: Design and report of results
inferred← citeshttps://doi.org/10.1002/mrm.29684openalex-snapshot
Distortion‐corrected image reconstruction with deep learning on an MRI‐Linac
inferred← citeshttps://doi.org/10.1002/mrm.70108openalex-snapshot
Rapid whole‐brain T 2 * and susceptibility mapping using 3D multiple overlapping‐echo detachment acquisition and missing modality synthesis embedded simulation
inferred← citeshttps://doi.org/10.1002/mrm.70358openalex-snapshot
Advancing Quantitative Susceptibility Mapping With 2.5D Diffusion Models for Rapid Intracranial Hemorrhage Quantification
inferred← citeshttps://doi.org/10.1002/nbm.5163openalex-snapshot
DF‐QSM: Data Fidelity based Hybrid Approach for Improved Quantitative Susceptibility Mapping of the Brain
inferred← citeshttps://doi.org/10.1002/nbm.70152openalex-snapshot
ISDU‐QSMNet: Iteration Specific Denoising With Unshared Weights for Improved QSM Reconstruction
inferred← citeshttps://doi.org/10.1007/s10334-024-01158-7openalex-snapshot
Spinet-QSM: model-based deep learning with schatten p-norm regularization for improved quantitative susceptibility mapping
inferred← citeshttps://doi.org/10.1016/j.bspc.2026.111273openalex-snapshot
SUSEP-Net: Simulation-supervised and contrastive regularization-based deep neural networks for susceptibility source separation
inferred← citeshttps://doi.org/10.1016/j.media.2022.102477openalex-snapshot
Unsupervised resolution-agnostic quantitative susceptibility mapping using adaptive instance normalization
inferred← citeshttps://doi.org/10.1016/j.media.2022.102700openalex-snapshot
NeXtQSM—A complete deep learning pipeline for data-consistent Quantitative Susceptibility Mapping trained with hybrid data
inferred← citeshttps://doi.org/10.1016/j.media.2023.102829openalex-snapshot
DeepSTI: Towards tensor reconstruction using fewer orientations in susceptibility tensor imaging
inferred← citeshttps://doi.org/10.1016/j.media.2024.103160openalex-snapshot
Plug-and-Play latent feature editing for orientation-adaptive quantitative susceptibility mapping neural networks
inferred← citeshttps://doi.org/10.1016/j.media.2024.103173openalex-snapshot
A subject-specific unsupervised deep learning method for quantitative susceptibility mapping using implicit neural representation
inferred← citeshttps://doi.org/10.1016/j.mri.2022.01.018openalex-snapshot
A data-driven deep learning pipeline for quantitative susceptibility mapping (QSM)
inferred← citeshttps://doi.org/10.1016/j.neuroimage.2021.118376openalex-snapshot
MoDL-QSM: Model-based deep learning for quantitative susceptibility mapping
inferred← citeshttps://doi.org/10.1016/j.neuroimage.2021.118404openalex-snapshot
Accelerating quantitative susceptibility and R2* mapping using incoherent undersampling and deep neural network reconstruction
inferred← citeshttps://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
inferred← citeshttps://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
inferred← citeshttps://doi.org/10.1016/j.neuroimage.2022.119788openalex-snapshot
Comparison of quantitative susceptibility mapping methods for iron-sensitive susceptibility imaging at 7T: An evaluation in healthy subjects and patients with Huntington's disease
inferred← citeshttps://doi.org/10.1016/j.neuroimage.2022.119842openalex-snapshot
Affine transformation edited and refined deep neural network for quantitative susceptibility mapping
inferred← citeshttps://doi.org/10.1016/j.neuroimage.2023.120181openalex-snapshot
msQSM: Morphology-based self-supervised deep learning for quantitative susceptibility mapping
inferred← citeshttps://doi.org/10.1016/j.neuroimage.2024.120547openalex-snapshot
Neuroimaging of Parkinson's disease by quantitative susceptibility mapping
inferred← citeshttps://doi.org/10.1016/j.neuroimage.2024.120583openalex-snapshot
Quantitative susceptibility mapping through model-based deep image prior (MoDIP)
inferred← citeshttps://doi.org/10.1016/j.neuroimage.2025.121499openalex-snapshot
Investigating the effect of masking and background field removal algorithms on the quality of QSM reconstructions using a realistic numerical head phantom
inferred← citeshttps://doi.org/10.1016/j.neuroimage.2025.121598openalex-snapshot
Resolution generalization of deep learning-based dipole inversion networks for QSM
inferred← citeshttps://doi.org/10.1016/j.zemedi.2021.06.004openalex-snapshot
Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning
inferred← citeshttps://doi.org/10.1016/j.zemedi.2022.08.001openalex-snapshot
BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources
inferred← citeshttps://doi.org/10.1088/1361-6560/ad237fopenalex-snapshot
A spatially adaptive regularization based three-dimensional reconstruction network for quantitative susceptibility mapping
inferred← citeshttps://doi.org/10.1101/2020.11.25.397695openalex-snapshot
QSM Reconstruction Challenge 2.0: Design and Report of Results
inferred← citeshttps://doi.org/10.1109/access.2024.3368518openalex-snapshot
NoQSM-net: Combining Convolutional Neural Network With Numerical Optimization Algorithm for Quantitative Susceptibility Mapping Reconstruction
inferred← citeshttps://doi.org/10.1109/access.2025.3620427openalex-snapshot
A Fair Comparison in Deep Learning-Based QSM Reconstruction
inferred← citeshttps://doi.org/10.1109/isbi60581.2025.10980951openalex-snapshot
UATTA-QSM: Uncertainty-Aware Test Time Adaptation for Improved Quantitative Susceptibility Mapping
inferred← citeshttps://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
inferred← citeshttps://doi.org/10.1109/jbhi.2023.3238714openalex-snapshot
Reconstruction of Quantitative Susceptibility Mapping from Total Field Maps with Local Field Maps Guided UU-Net
inferred← citeshttps://doi.org/10.1109/tbme.2025.3605587openalex-snapshot
Diffusion-QSM: Diffusion Model With Time-Travel and Resampling Refinement for Quantitative Susceptibility Mapping
inferred← citeshttps://doi.org/10.1109/tci.2021.3133692openalex-snapshot
Meta-Learning Based Interactively Connected Clique U-Net for Quantitative Susceptibility Mapping
inferred← citeshttps://doi.org/10.1109/tetci.2024.3425645openalex-snapshot
Deep Learning Quantitative Susceptibility Mapping With Reduced Training Time Based on Local Field Transformation
inferred← citeshttps://doi.org/10.12122/j.issn.1673-4254.2022.12.07openalex-snapshot
[A multi-channel input convolutional neural network for artifact reduction in quantitative susceptibility mapping].
inferred← citeshttps://doi.org/10.3389/fnins.2022.837721openalex-snapshot
Learn Less, Infer More: Learning in the Fourier Domain for Quantitative Susceptibility Mapping
inferred← citeshttps://doi.org/10.3389/fnins.2023.1165446openalex-snapshot
Quantitative susceptibility mapping using multi-channel convolutional neural networks with dipole-adaptive multi-frequency inputs
inferred← citeshttps://doi.org/10.3389/fnins.2023.1308829openalex-snapshot
A latent code based multi-variable modulation network for susceptibility mapping
inferred← citeshttps://doi.org/10.3389/fnins.2024.1366165openalex-snapshot
Incorporating a-priori information in deep learning models for quantitative susceptibility mapping via adaptive convolution
inferred← citeshttps://doi.org/10.3390/info16070592openalex-snapshot
DIP-UP: Deep Image Prior for Unwrapping Phase
inferred← citeshttps://doi.org/10.48550/arxiv.2012.03842openalex-snapshot
CycleQSM: Unsupervised QSM Deep Learning using Physics-Informed CycleGAN
inferred← citeshttps://doi.org/10.58530/2022/3923openalex-snapshot
Improved Padding in CNNs for Quantitative Susceptibility Mapping
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