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QSM reconstruction challenge 2.0: Design and report of results
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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.24365openalex-snapshot
Fast image reconstruction with L2‐regularization
inferredcites →https://doi.org/10.1002/jmri.24768openalex-snapshot
Susceptibility-weighted imaging and quantitative susceptibility mapping in the brain
inferredcites →https://doi.org/10.1002/mrm.1910340618openalex-snapshot
The rician distribution of noisy mri data
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.24272openalex-snapshot
Nonlinear formulation of the magnetic field to source relationship for robust quantitative susceptibility mapping
inferredcites →https://doi.org/10.1002/mrm.24384openalex-snapshot
Improving susceptibility mapping using a threshold‐based K‐space/image domain iterative reconstruction approach
inferredcites →https://doi.org/10.1002/mrm.24405openalex-snapshot
Toward online reconstruction of quantitative susceptibility maps: Superfast dipole inversion
inferredcites →https://doi.org/10.1002/mrm.24918openalex-snapshot
Quantitative oxygenation venography from MRI phase
inferredcites →https://doi.org/10.1002/mrm.26054openalex-snapshot
Gibbs‐ringing artifact removal based on local subvoxel‐shifts
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/mrm.26830openalex-snapshot
Quantitative susceptibility mapping: Report from the 2016 reconstruction challenge
inferredcites →https://doi.org/10.1002/mrm.27073openalex-snapshot
Fast nonlinear susceptibility inversion with variational regularization
inferredcites →https://doi.org/10.1002/mrm.27483openalex-snapshot
Weak‐harmonic regularization for quantitative susceptibility mapping
inferredcites →https://doi.org/10.1002/mrm.27976openalex-snapshot
Quantitative susceptibility map reconstruction using annihilating filter‐based low‐rank Hankel matrix approach
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.28377openalex-snapshot
An optimized and highly repeatable MRI acquisition and processing pipeline for quantitative susceptibility mapping in the head‐and‐neck region
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/nbm.3383openalex-snapshot
Streaking artifact reduction for quantitative susceptibility mapping of sources with large dynamic range
inferredcites →https://doi.org/10.1002/nbm.3491openalex-snapshot
Reproducibility of R2* and quantitative susceptibility mapping (QSM) reconstruction methods in the basal ganglia of healthy subjects
inferredcites →https://doi.org/10.1002/nbm.3601openalex-snapshot
An illustrated comparison of processing methods for MR phase imaging and QSM: combining array coil signals and phase unwrapping
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/10.1002/nbm.4461openalex-snapshot
xQSM: quantitative susceptibility mapping with octave convolutional and noise‐regularized neural networks
inferredcites →https://doi.org/10.1016/j.mri.2014.09.004openalex-snapshot
Quantitative susceptibility mapping: current status and future directions
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.2012.05.067openalex-snapshot
Quantitative susceptibility mapping for investigating subtle susceptibility variations in the human brain
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.2014.11.038openalex-snapshot
A modulated closed form solution for quantitative susceptibility mapping — A thorough evaluation and comparison to iterative methods based on edge prior knowledge
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.2016.05.013openalex-snapshot
Probing neuronal activation by functional quantitative susceptibility mapping under a visual paradigm: A group level comparison with BOLD fMRI and PET
inferredcites →https://doi.org/10.1016/j.neuroimage.2017.11.018openalex-snapshot
Rapid two-step dipole inversion for susceptibility mapping with sparsity priors
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.2018.07.065openalex-snapshot
A robust multi-scale approach to quantitative susceptibility mapping
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.2019.116389openalex-snapshot
QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with increased receptive field
inferredcites →https://doi.org/10.1016/j.neuroimage.2020.116579openalex-snapshot
Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction
inferredcites →https://doi.org/10.1109/tbme.2017.2749298openalex-snapshot
Quantitative Susceptibility Mapping (QSM) Algorithms: Mathematical Rationale and Computational Implementations
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.13122640openalex-snapshot
Intracranial Calcifications and Hemorrhages: Characterization with Quantitative Susceptibility Mapping
inferredcites →https://doi.org/10.1371/journal.pone.0057924openalex-snapshot
Quantitative Susceptibility Mapping Differentiates between Blood Depositions and Calcifications in Patients with Glioblastoma
inferredcites →https://doi.org/10.1523/jneurosci.1907-15.2016openalex-snapshot
In Vivo MRI Mapping of Brain Iron Deposition across the Adult Lifespan
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.1904.05493openalex-snapshot
Non-locally Encoder-Decoder Convolutional Network for Whole Brain QSM Inversion
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/hbm.26432openalex-snapshot
Quantitative susceptibility mapping identifies hippocampal and other subcortical grey matter tissue composition changes in temporal lobe epilepsy
inferred← citeshttps://doi.org/10.1002/hbm.70187openalex-snapshot
Sensitivity of Quantitative Susceptibility Mapping for Clinical Research in Deep Gray Matter
inferred← citeshttps://doi.org/10.1002/hbm.70213openalex-snapshot
Investigating the Association of Concussion and Contact Sport Exposure History With Brain Microstructure Using Quantitative Susceptibility Mapping
inferred← citeshttps://doi.org/10.1002/jmri.28828openalex-snapshot
Renal MRI : From Nephron to NMR Signal
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.28716openalex-snapshot
QSM reconstruction challenge 2.0: A realistic in silico head phantom for MRI data simulation and evaluation of susceptibility mapping procedures
inferred← citeshttps://doi.org/10.1002/mrm.28957openalex-snapshot
Streaking artifact suppression of quantitative susceptibility mapping reconstructions via L1‐norm data fidelity optimization (L1‐QSM)
inferred← citeshttps://doi.org/10.1002/mrm.29048openalex-snapshot
QSMxT: Robust masking and artifact reduction for quantitative susceptibility mapping
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.29084openalex-snapshot
Development, validation, qualification, and dissemination of quantitative MR methods: Overview and recommendations by the ISMRM quantitative MR study group
inferred← citeshttps://doi.org/10.1002/mrm.29218openalex-snapshot
Hybrid data fidelity term approach for quantitative susceptibility mapping
inferred← citeshttps://doi.org/10.1002/mrm.29365openalex-snapshot
Multi‐echo quantitative susceptibility mapping: how to combine echoes for accuracy and precision at 3 Tesla
inferred← citeshttps://doi.org/10.1002/mrm.29366openalex-snapshot
On the open‐source landscape of Magnetic Resonance in Medicine
inferred← citeshttps://doi.org/10.1002/mrm.29550openalex-snapshot
Investigating the effect of oblique image acquisition on the accuracy of QSM and a robust tilt correction method
inferred← citeshttps://doi.org/10.1002/mrm.29588openalex-snapshot
Multi‐echo dipole inversion for magnetic susceptibility mapping
inferred← citeshttps://doi.org/10.1002/mrm.29627openalex-snapshot
To mask or not to mask? Investigating the impact of accounting for spatial frequency distributions and susceptibility sources on QSM quality
inferred← citeshttps://doi.org/10.1002/mrm.29722openalex-snapshot
Robust quantitative susceptibility mapping via approximate message passing with parameter estimation
inferred← citeshttps://doi.org/10.1002/mrm.30006openalex-snapshot
Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: A consensus of the ISMRM electro‐magnetic tissue properties study group
inferred← citeshttps://doi.org/10.1002/mrm.30267openalex-snapshot
Multiband accelerated 2D EPI for multi‐echo brain QSM at 3 T
inferred← citeshttps://doi.org/10.1002/mrm.30271openalex-snapshot
XSIM : A structural similarity index measure optimized for MRI QSM
inferred← citeshttps://doi.org/10.1002/mrm.30537openalex-snapshot
Quantitative susceptibility mapping in magnetically inhomogeneous tissues
inferred← citeshttps://doi.org/10.1002/mrm.70193openalex-snapshot
Quantitative Susceptibility Mapping in Skull Base Chordoma: In Silico Analysis and In Vivo Application Towards Indirect Hypoxia Assessment
inferred← citeshttps://doi.org/10.1002/mrm.70468openalex-snapshot
A Realistic In Silico Brain Phantom for Quantifying Susceptibility Anisotropy‐Induced Error in Susceptibility Separation
inferred← citeshttps://doi.org/10.1002/nbm.4788openalex-snapshot
Multisite reproducibility of quantitative susceptibility mapping and effective transverse relaxation rate in deep gray matter at 3 T using locally optimized sequences in 24 traveling heads
inferred← citeshttps://doi.org/10.1002/nbm.5055openalex-snapshot
Model resolution‐based deconvolution for improved quantitative susceptibility mapping
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/10.1002/nbm.70225openalex-snapshot
Repeatability of Susceptibility Source Separation Methods in Human Brain: A Single‐Site Study at 3 T
inferred← citeshttps://doi.org/10.1007/978-3-031-40169-5_16openalex-snapshot
Quantitative Susceptibility Mapping of the Kidney
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/10.1007/s10334-025-01226-6openalex-snapshot
The statistical impact of ROI referencing on quantitative susceptibility mapping
inferred← citeshttps://doi.org/10.1007/s10334-025-01229-3openalex-snapshot
Comparing repeatability metrics for quantitative susceptibility mapping in the head and neck
inferred← citeshttps://doi.org/10.1016/b978-0-12-822726-8.00027-0openalex-snapshot
Quantitative Susceptibility-Mapping Reconstruction
inferred← citeshttps://doi.org/10.1016/b978-0-323-95739-7.00027-7openalex-snapshot
Quantitative susceptibility mapping in multiple sclerosis
inferred← citeshttps://doi.org/10.1016/b978-0-323-99898-7.00025-0openalex-snapshot
Phase imaging: Susceptibility-Weighted Imaging and Quantitative Susceptibility Mapping
inferred← citeshttps://doi.org/10.1016/j.ejmp.2022.09.012openalex-snapshot
Multi-centre and multi-vendor reproducibility of a standardized protocol for quantitative susceptibility Mapping of the human brain at 3T
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.media.2024.103230openalex-snapshot
The STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data
inferred← citeshttps://doi.org/10.1016/j.mri.2026.110708openalex-snapshot
Magnetic susceptibility source separation (χ-separation) in quantitative susceptibility mapping
inferred← citeshttps://doi.org/10.1016/j.neuroimage.2021.118399openalex-snapshot
Investigating the effect of flow compensation and quantitative susceptibility mapping method on the accuracy of venous susceptibility measurement
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.2023.120189openalex-snapshot
Cerebral oxygen metabolism from MRI susceptibility
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)
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SIPAS: A comprehensive susceptibility imaging process and analysis studio
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Biophysical contrast sources for magnetic susceptibility and R2* mapping: A combined 7 Tesla, mass spectrometry and electron paramagnetic resonance study
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.nicl.2025.103771openalex-snapshot
Thalamic iron in multiple sclerosis: Waning support for the early-rise late-decline hypothesis
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Investigating the relationship between thalamic iron concentration and disease severity in secondary progressive multiple sclerosis using quantitative susceptibility mapping: Cross-sectional analysis from the MS-STAT2 randomised controlled trial
inferred← citeshttps://doi.org/10.1088/1361-6560/ad237fopenalex-snapshot
A spatially adaptive regularization based three-dimensional reconstruction network for quantitative susceptibility mapping
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A Fair Comparison in Deep Learning-Based QSM Reconstruction
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Noise Propagation and MP-PCA Image Denoising for High-Resolution Quantitative $R_2^{\rm{*}}$, $T_2^{\rm{*}}$, and Magnetic Susceptibility Mapping (QSM)
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Deep Learning Quantitative Susceptibility Mapping With Reduced Training Time Based on Local Field Transformation
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Comparison of Phase Estimation Methods for Quantitative Susceptibility Mapping Using a Rotating-Tube Phantom
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Quantitative susceptibility mapping for iron quantification of deep gray matter in patients with ischemic cerebrovascular events: A systematic review
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Chronic active lesions in multiple sclerosis: classification, terminology, and clinical significance
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Impact of processing and analysis methodology on thalamic susceptibility assessment in multiple sclerosis
inferred← citeshttps://doi.org/10.3389/fnins.2023.1130524openalex-snapshot
Incomplete spectrum QSM using support information
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A latent code based multi-variable modulation network for susceptibility mapping
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Incorporating a-priori information in deep learning models for quantitative susceptibility mapping via adaptive convolution
inferred← citeshttps://doi.org/10.3390/brainsci15090944openalex-snapshot
Imaging of Cerebral Iron as an Emerging Marker for Brain Aging, Neurodegeneration, and Cerebrovascular Diseases
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Central Vein Sign and Paramagnetic Rim Lesions: Susceptibility Changes in Brain Tissues and Their Implications for the Study of Multiple Sclerosis Pathology
inferred← citeshttps://doi.org/10.5281/zenodo.156580openalex-snapshot
FIGURES 16–21 in Description of Ptychocroca, a new genus from Chile and Argentina, with comments on the Bonagota Razowski group of genera (Lepidoptera: Tortricidae: Euliini)
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Hybrid data fidelity term approach for quantitative susceptibility mapping
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