ArticleMagnetic resonance in medicine2021
Asymmetric susceptibility tensor imaging.
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 4 papers.
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4 citing papers in PubMed, 6 citations in OpenAlex.
- Simulation of realistic brain phantoms for susceptibility tensor imaging.Magma (New York, N.Y.) · 2026Article
- Nonlinear inversion model-driven deep learning method for magnetic resonance imaging (MRI) quantitative susceptibility mapping imaging.Quantitative imaging in medicine and surgery · 2026Article
- Observations of anisotropic paramagnetic and diamagnetic susceptibility in the primate brain.bioRxiv : the preprint server for biology · 2025Article
- DeepSTI: Towards tensor reconstruction using fewer orientations in susceptibility tensor imaging.Medical image analysis · 2023Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
purposeTo investigate the symmetry constraint in susceptibility tensor imaging. THEORY: The linear relationship between the MRI frequency shift and the magnetic susceptibility tensor is derived without constraining the tensor to be symmetric. In the asymmetric case, the system matrix is shown to be maximally rank 6. Nonetheless, relaxing the symmetry constraint may still improve tensor estimation because noise and image artifacts do not necessarily follow the constraint.
methodsGradient echo phase data are obtained from postmortem mouse brain and kidney samples. Both symmetric and asymmetric tensor reconstructions are applied to the data. The reconstructions are then used for susceptibility tensor imaging fiber tracking. Simulations with ground truth and at various noise levels are also performed. The reconstruction methods are compared qualitatively and quantitatively.
resultsCompared to regularized and unregularized symmetric reconstructions, the asymmetric reconstruction shows reduced noise and streaking artifacts, better contrast, and more complete fiber tracking. In simulation, the asymmetric reconstruction achieves better mean squared error and better angular difference in the presence of noise. Decomposing the asymmetric tensor into its symmetric and antisymmetric components confirms that the underlying susceptibility tensor is symmetric and that the main sources of asymmetry are noise and streaking artifacts.
conclusionWhereas the susceptibility tensor is symmetric, asymmetric reconstruction is more effective in suppressing noise and artifacts, resulting in more accurate estimation of the susceptibility tensor.
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