ArticleNeuroImage2020
QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with increased receptive field.
Article in NeuroImage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Revisiting brain iron deficiency in restless legs syndrome using magnetic resonance imaging.NeuroImage. Clinical · 2022Pooled it
- 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
- Clinical translation of fluid, imaging, and digital biomarkers for Alzheimer's disease.Alzheimer's research & therapy · 2026Review
- IncorporatingFrontiers in neuroscience · 2024Article
- DeepSWI: Using Deep Learning to Enhance Susceptibility Contrast on T2*-Weighted MRI.Journal of magnetic resonance imaging : JMRI · 2023Article
- DeepSTI: Towards tensor reconstruction using fewer orientations in susceptibility tensor imaging.Medical image analysis · 2023Article
- U-Net-Based Models towards Optimal MR Brain Image Segmentation.Diagnostics (Basel, Switzerland) · 2023Article
- WaveSep: A Flexible Wavelet-Based Approach for Source Separation in Susceptibility Imaging.Machine learning in clinical neuroimaging : 6th international workshop, MLCN 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, proceedings. MLCN (Workshop) (6th : 2023 : Vancouver, B.C.) · 2023Article
- Quantitative susceptibility mapping using multi-channel convolutional neural networks with dipole-adaptive multi-frequency inputs.Frontiers in neuroscience · 2023Article
- Article
- [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
- HFP-QSMGAN: QSM from homodyne-filtered phase images.Magnetic resonance in medicine · 2022Article
- The role of generative adversarial networks in brain MRI: a scoping review.Insights into imaging · 2022Article
- Comparison of quantitative susceptibility mapping methods on evaluating radiation-induced cerebral microbleeds and basal ganglia at 3T and 7T.NMR in biomedicine · 2022Article
- Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning.Zeitschrift fur medizinische Physik · 2022Article
- Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends.NMR in biomedicine · 2022Review
- Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Autoencoders.BME frontiers · 2022Article
- Learn Less, Infer More: Learning in the Fourier Domain for Quantitative Susceptibility Mapping.Frontiers in neuroscience · 2022Article
- QSM reconstruction challenge 2.0: Design and report of results.Magnetic resonance in medicine · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Quantitative susceptibility mapping (QSM) is a powerful MRI technique that has shown great potential in quantifying tissue susceptibility in numerous neurological disorders. However, the intrinsic ill-posed dipole inversion problem greatly affects the accuracy of the susceptibility map. We propose QSMGAN: a 3D deep convolutional neural network approach based on a 3D U-Net architecture with increased receptive field of the input phase compared to the output and further refined the network using the WGAN with gradient penalty training strategy. Our method generates accurate QSM maps from single orientation phase maps efficiently and performs significantly better than traditional non-learning-based dipole inversion algorithms. The generalization capability was verified by applying the algorithm to an unseen pathology--brain tumor patients with radiation-induced cerebral microbleeds.
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Registered trials
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.