Unrolled Optimization via Physics-Assisted Convolutional Neural Network for MR-Based Electrical Properties Tomography: A Numerical Investigation

Magnetic Resonance imaging based Electrical Properties Tomography (MR-EPT) is a non-invasive technique that measures the electrical properties (EPs) of biological tissues. In this work, we present and numerically investigate the performance of an unrolled, physics-assisted method for 2D MR-EPT recon...

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Bibliographic Details
Published in:IEEE open journal of engineering in medicine and biology 2024-01, Vol.5, p.505-513
Main Authors: Zumbo, Sabrina, Mandija, Stefano, Meliado, Ettore F., Stijnman, Peter, Meerbothe, Thierry G., van den Berg, Cornelis A.T., Isernia, Tommaso, Bevacqua, Martina T.
Format: Article
Language:English
Subjects:
Artificial neural networks
Biological properties
Brain slice preparation
Computational modeling
Computational neuroscience
Computing time
Convolutional neural network
Electrical properties
Head
inverse scattering problems
Iterative methods
learning methods
Magnetic properties
Magnetic resonance imaging
Neural networks
Neuroimaging
Numerical models
Physics
Tissues
Tomography
Training
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Summary:Magnetic Resonance imaging based Electrical Properties Tomography (MR-EPT) is a non-invasive technique that measures the electrical properties (EPs) of biological tissues. In this work, we present and numerically investigate the performance of an unrolled, physics-assisted method for 2D MR-EPT reconstructions, where a cascade of Convolutional Neural Networks is used to compute the contrast update. Each network takes in input the EPs and the gradient descent direction (encoding the physics underlying the adopted scattering model) and returns as output the updated contrast function. The network is trained and tested in silico using 2D slices of realistic brain models at 128 MHz. Results show the capability of the proposed procedure to reconstruct EPs maps with quality comparable to that of the popular Contrast Source Inversion-EPT, while significantly reducing the computational time.
ISSN:2644-1276
2644-1276
DOI:10.1109/OJEMB.2024.3402998