ArticleMagnetic resonance in medicine2022
Multi-echo quantitative susceptibility mapping: how to combine echoes for accuracy and precision at 3 Tesla.
Article in Magnetic resonance in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 11 citations in OpenAlex.
- An accelerated PETALUTE MRI sequence for in vivo quantification of sodium content in human articular cartilage at 3T.Skeletal radiology · 2025Trial
- Association Between Deep Gray Matter Iron Deposition Dysregulation and Cognitive Performance in Type 2 Diabetes Mellitus Patients: A Quantitative Susceptibility Mapping Study.Journal of magnetic resonance imaging : JMRI · 2026Article
- Quantitative susceptibility mapping reveals differences between subtypes of Lewy body dementia.Brain : a journal of neurology · 2026Article
- The role of quantitative susceptibility mapping in dementia with Lewy Bodies.NeuroImage. Clinical · 2026Article
- Quantitative susceptibility mapping in myotonic dystrophy: clinical relevance of subcortical iron accumulation.Brain communications · 2026Article
- Evaluating hemorrhagic risks using intratumoral susceptibility signals in stereotactic brain biopsies.Neurosurgical review · 2025Article
- Multiparametric MRI dataset for susceptibility-based radiomic feature extraction and analysis.Scientific data · 2024Article
- Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging.Nature methods · 2024Article
- Assessing robustness of quantitative susceptibility-based MRI radiomic features in patients with multiple sclerosis.Scientific reports · 2023Article
- Cerebral oxygen metabolism from MRI susceptibility.NeuroImage · 2023Review
- Multi-echo quantitative susceptibility mapping: how to combine echoes for accuracy and precision at 3 Tesla.Magnetic resonance in medicine · 2022Article
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Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo compare different multi-echo combination methods for MRI QSM. Given the current lack of consensus, we aimed to elucidate how to optimally combine multi-echo gradient-recalled echo signal phase information, either before or after applying Laplacian-base methods (LBMs) for phase unwrapping or background field removal.
methodsMulti-echo gradient-recalled echo data were simulated in a numerical head phantom, and multi-echo gradient-recalled echo images were acquired at 3 Tesla in 10 healthy volunteers. To enable image-based estimation of gradient-recalled echo signal noise, 5 volunteers were scanned twice in the same session without repositioning. Five QSM processing pipelines were designed: 1 applied nonlinear phase fitting over TEs before LBMs; 2 applied LBMs to the TE-dependent phase and then combined multiple TEs via either TE-weighted or SNR-weighted averaging; and 2 calculated TE-dependent susceptibility maps via either multi-step or single-step QSM and then combined multiple TEs via magnitude-weighted averaging. Results from different pipelines were compared using visual inspection; summary statistics of susceptibility in deep gray matter, white matter, and venous regions; phase noise maps (error propagation theory); and, in the healthy volunteers, regional fixed bias analysis (Bland-Altman) and regional differences between the means (nonparametric tests).
resultsNonlinearly fitting the multi-echo phase over TEs before applying LBMs provided the highest regional accuracy of
conclusionFor multi-echo QSM, we recommend combining the signal phase by nonlinear fitting before applying LBMs.
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