ArticleNMR in biomedicine2020
Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM).
Article in NMR in biomedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Advancing Quantitative Susceptibility Mapping With 2.5D Diffusion Models for Rapid Intracranial Hemorrhage Quantification.Magnetic resonance in medicine · 2026Article
- Nonlinear inversion model-driven deep learning method for magnetic resonance imaging (MRI) quantitative susceptibility mapping imaging.Quantitative imaging in medicine and surgery · 2026Article
- Comparison study of quantitative susceptibility mapping with GRAPPA and wave-CAIPI: reproducibility, consistency, and microbleeds detection.Japanese journal of radiology · 2025Article
- Spinet-QSM: model-based deep learning with schatten p-norm regularization for improved quantitative susceptibility mapping.Magma (New York, N.Y.) · 2024Article
- 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
- Stuttering associated with a pathogenic variant in the chaperone protein cyclophilin 40.Brain : a journal of neurology · 2023Article
- BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources.Zeitschrift fur medizinische Physik · 2023Article
- DeepSTI: Towards tensor reconstruction using fewer orientations in susceptibility tensor imaging.Medical image analysis · 2023Article
- Toward a realistic in silico abdominal phantom for QSM.Magnetic resonance in medicine · 2023Article
- A latent code based multi-variable modulation network for susceptibility mapping.Frontiers in neuroscience · 2023Article
- Incomplete spectrum QSM using support information.Frontiers in neuroscience · 2023Article
- Quantitative susceptibility mapping using multi-channel convolutional neural networks with dipole-adaptive multi-frequency inputs.Frontiers in neuroscience · 2023Article
- [A multi-channel input convolutional neural network for artifact reduction in quantitative susceptibility mapping].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2022Article
- Clinical Application of MPRAGE Wave Controlled Aliasing in Parallel Imaging (Wave-CAIPI): A Comparative Study with MPRAGE GRAPPA.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2022Article
- Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning.Zeitschrift fur medizinische Physik · 2022Article
- Sub-acute Changes on MRI Measures of Cerebral Blood Flow and Venous Oxygen Saturation in Concussed Australian Rules Footballers.Sports medicine - open · 2022Article
- Quantitative susceptibility mapping as an imaging biomarker for Alzheimer's disease: The expectations and limitations.Frontiers in neuroscience · 2022Review
- QSM reconstruction challenge 2.0: Design and report of results.Magnetic resonance in medicine · 2021Article
- Single-step calculation of susceptibility through multiple orientation sampling.NMR in biomedicine · 2021Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
High-quality Quantitative Susceptibility Mapping (QSM) with Nonlinear Dipole Inversion (NDI) is developed with pre-determined regularization while matching the image quality of state-of-the-art reconstruction techniques and avoiding over-smoothing that these techniques often suffer from. NDI is flexible enough to allow for reconstruction from an arbitrary number of head orientations and outperforms COSMOS even when using as few as 1-direction data. This is made possible by a nonlinear forward-model that uses the magnitude as an effective prior, for which we derived a simple gradient descent update rule. We synergistically combine this physics-model with a Variational Network (VN) to leverage the power of deep learning in the VaNDI algorithm. This technique adopts the simple gradient descent rule from NDI and learns the network parameters during training, hence requires no additional parameter tuning. Further, we evaluate NDI at 7 T using highly accelerated Wave-CAIPI acquisitions at 0.5 mm isotropic resolution and demonstrate high-quality QSM from as few as 2-direction data.
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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.