ArticleNMR in biomedicine2022
Combining deep learning and 3D contrast source inversion in MR-based electrical properties tomography.
Article in NMR in biomedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Roadmap towards personalized approaches and safety considerations in non-ionizing radiation: from dosimetry to therapeutic and diagnostic applications.Physics in medicine and biology · 2026Review
- MR electrical properties mapping using vision transformers and canny edge detectors.Magnetic resonance in medicine · 2025Article
- An optimized sliding rail-assisted micrometer system for sensing volume measurement of open-ended coaxial probes in breast cancer dielectric property analysis.Frontiers in bioengineering and biotechnology · 2025Article
- PIFON-EPT: MR-Based Electrical Property Tomography Using Physics-Informed Fourier Networks.IEEE journal on multiscale and multiphysics computational techniques · 2024Article
- Unrolled Optimization via Physics-Assisted Convolutional Neural Network for MR-Based Electrical Properties Tomography: A Numerical Investigation.IEEE open journal of engineering in medicine and biology · 2024Article
- Data-driven electrical conductivity brain imaging using 3 T MRI.Human brain mapping · 2023Article
- Use of machine learning to improve the estimation of conductivity and permittivity based on longitudinal relaxation time T1 in magnetic resonance at 7 T.Scientific reports · 2023Article
- A Novel Discretization Procedure in the CSI-FEM Algorithm for Brain Stroke Microwave Imaging.Sensors (Basel, Switzerland) · 2022Article
- Combining deep learning and 3D contrast source inversion in MR-based electrical properties tomography.NMR in biomedicine · 2022Article
- Transceive phase corrected 2D contrast source inversion-electrical properties tomography.Magnetic resonance in medicine · 2021Article
- Electrical Properties Tomography: A Methodological Review.Diagnostics (Basel, Switzerland) · 2021Review
- Brain Tissue Conductivity Measurements with MR-Electrical Properties Tomography: An In Vivo Study.Brain topography · 2021Article
- Conductivity Tensor Imaging of the Human Brain Using Water Mapping Techniques.Frontiers in neuroscience · 2021Article
- High-frequency conductivity at Larmor-frequency in human brain using moving local window multilayer perceptron neural network.PloS one · 2021Article
- Modeling radio-frequency energy-induced heating due to the presence of transcranial electric stimulation setup at 3T.Magma (New York, N.Y.) · 2020Article
- Deep learning-based reconstruction of in vivo pelvis conductivity with a 3D patch-based convolutional neural network trained on simulated MR data.Magnetic resonance in medicine · 2020Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Magnetic resonance electrical properties tomography (MR-EPT) is a technique used to estimate the conductivity and permittivity of tissues from MR measurements of the transmit magnetic field. Different reconstruction methods are available; however, all these methods present several limitations, which hamper the clinical applicability. Standard Helmholtz-based MR-EPT methods are severely affected by noise. Iterative reconstruction methods such as contrast source inversion electrical properties tomography (CSI-EPT) are typically time-consuming and are dependent on their initialization. Deep learning (DL) based methods require a large amount of training data before sufficient generalization can be achieved. Here, we investigate the benefits achievable using a hybrid approach, that is, using MR-EPT or DL-EPT as initialization guesses for standard 3D CSI-EPT. Using realistic electromagnetic simulations at 3 and 7 T, the accuracy and precision of hybrid CSI reconstructions are compared with those of standard 3D CSI-EPT reconstructions. Our results indicate that a hybrid method consisting of an initial DL-EPT reconstruction followed by a 3D CSI-EPT reconstruction would be beneficial. DL-EPT combined with standard 3D CSI-EPT exploits the power of data-driven DL-based EPT reconstructions, while the subsequent CSI-EPT facilitates a better generalization by providing data consistency.
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