ArticleMagnetic resonance in medicine2020
Deep learning-based reconstruction of in vivo pelvis conductivity with a 3D patch-based convolutional neural network trained on simulated MR data.
Article in Magnetic resonance in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 35 citations in OpenAlex.
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- Validation of the implementation of phased-array heating systems in Plan2Heat.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Article
- Machine learning-based amide proton transfer imaging using partially synthetic training data.Magnetic resonance in medicine · 2024Article
- PIFON-EPT: MR-Based Electrical Property Tomography Using Physics-Informed Fourier Networks.IEEE journal on multiscale and multiphysics computational techniques · 2024Article
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- Data-driven electrical conductivity brain imaging using 3 T MRI.Human brain mapping · 2023Article
- Deep learning reconstruction for brain diffusion-weighted imaging: efficacy for image quality improvement, apparent diffusion coefficient assessment, and intravoxel incoherent motion evaluation inDiagnostic and interventional radiology (Ankara, Turkey) · 2023Article
- Repeatability of brain phase-based magnetic resonance electric properties tomography methods and effect of compressed SENSE and RF shimming.Physical and engineering sciences in medicine · 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
- Deep Learning and Its Application to Function Approximation for MR in Medicine: An Overview.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2022Review
- Native-resolution myocardial principal Eulerian strain mapping using convolutional neural networks and Tagged Magnetic Resonance Imaging.Computers in biology and medicine · 2022Article
- Electrical Properties Tomography: A Methodological Review.Diagnostics (Basel, Switzerland) · 2021Review
- 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
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo demonstrate that mapping pelvis conductivity at 3T with deep learning (DL) is feasible.
methods210 dielectric pelvic models were generated based on CT scans of 42 cervical cancer patients. For all dielectric models, electromagnetic and MR simulations with realistic accuracy and precision were performed to obtain
resultsConductivity maps obtained from both network configurations were comparable. Accuracy was assessed by mean error (ME) with respect to ground truth conductivity. On average, ME < 0.1 Sm
conclusionAnatomically detailed, noise-robust 3D conductivity maps with good sensitivity to tissue conductivity variations were reconstructed in the pelvis with DL.
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