ArticleMagnetic resonance in medicine2021
QSM reconstruction challenge 2.0: Design and report of results.
Article in Magnetic resonance in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed, 63 citations in OpenAlex.
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- Repeatability of Susceptibility Source Separation Methods in Human Brain: A Single-Site Study at 3 T.NMR in biomedicine · 2026Article
- Quantitative susceptibility mapping in magnetically inhomogeneous tissues.Magnetic resonance in medicine · 2025Article
- Imaging of Cerebral Iron as an Emerging Marker for Brain Aging, Neurodegeneration, and Cerebrovascular Diseases.Brain sciences · 2025Review
- Comparing repeatability metrics for quantitative susceptibility mapping in the head and neck.Magma (New York, N.Y.) · 2025Article
- The statistical impact of ROI referencing on quantitative susceptibility mapping.Magma (New York, N.Y.) · 2025Article
- Sensitivity of Quantitative Susceptibility Mapping for Clinical Research in Deep Gray Matter.Human brain mapping · 2025Article
- Investigating the Association of Concussion and Contact Sport Exposure History With Brain Microstructure Using Quantitative Susceptibility Mapping.Human brain mapping · 2025Article
- Thalamic iron in multiple sclerosis: Waning support for the early-rise late-decline hypothesis.NeuroImage. Clinical · 2025Article
- Impact of processing and analysis methodology on thalamic susceptibility assessment in multiple sclerosis.PloS one · 2025Article
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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.Diagnostics (Basel, Switzerland) · 2024Review
- Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: A consensus of the ISMRM electro-magnetic tissue properties study group.Magnetic resonance in medicine · 2024Article
- IncorporatingFrontiers in neuroscience · 2024Article
- Chronic active lesions in multiple sclerosis: classification, terminology, and clinical significance.Therapeutic advances in neurological disorders · 2024Review
- Renal MRI: From Nephron to NMR Signal.Journal of magnetic resonance imaging : JMRI · 2023Review
- Quantitative susceptibility mapping identifies hippocampal and other subcortical grey matter tissue composition changes in temporal lobe epilepsy.Human brain mapping · 2023Article
- Robust quantitative susceptibility mapping via approximate message passing with parameter estimation.Magnetic resonance in medicine · 2023Article
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Authors and funding
7 authors at 6 institutions in 6 countries.
Funding
Abstract
purposeThe aim of the second quantitative susceptibility mapping (QSM) reconstruction challenge (Oct 2019, Seoul, Korea) was to test the accuracy of QSM dipole inversion algorithms in simulated brain data.
methodsA two-stage design was chosen for this challenge. The participants were provided with datasets of multi-echo gradient echo images synthesized from two realistic in silico head phantoms using an MR simulator. At the first stage, participants optimized QSM reconstructions without ground truth data available to mimic the clinical setting. At the second stage, ground truth data were provided for parameter optimization. Submissions were evaluated using eight numerical metrics and visual ratings.
resultsA total of 98 reconstructions were submitted for stage 1 and 47 submissions for stage 2. Iterative methods had the best quantitative metric scores, followed by deep learning and direct inversion methods. Priors derived from magnitude data improved the metric scores. Algorithms based on iterative approaches and total variation (and its derivatives) produced the best overall results. The reported results and analysis pipelines have been made public to allow researchers to compare new methods to the current state of the art.
conclusionThe synthetic data provide a consistent framework to test the accuracy and robustness of QSM algorithms in the presence of noise, calcifications and minor voxel dephasing effects. Total Variation-based algorithms produced the best results among all metrics. Future QSM challenges should assess whether this good performance with synthetic datasets translates to more realistic scenarios, where background fields and dipole-incompatible phase contributions are included.
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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.