Abstract
In the radiofrequency (RF) range, the electrical properties of tissues (EPs: conductivity and permittivity) are modulated by the ionic and water content, which change for pathological conditions. Information on tissues EPs can be used e.g. in oncology as a biomarker. The inability of MR-Electrical Properties Tomography techniques (MR-EPT) to accurately reconstruct tissue EPs by relating MR measurements of the transmit RF field to the EPs limits their clinical applicability. Instead of employing electromagnetic models posing strict requirements on the measured MRI quantities, we propose a data driven approach where the electrical properties reconstruction problem can be casted as a supervised deep learning task (DL-EPT). DL-EPT reconstructions for simulations and MR measurements at 3 Tesla on phantoms and human brains using a conditional generative adversarial network demonstrate high quality EPs reconstructions and greatly improved precision compared to conventional MR-EPT. The supervised learning approach leverages the strength of electromagnetic simulations, allowing circumvention of inaccessible MR electromagnetic quantities. Since DL-EPT is more noise-robust than MR-EPT, the requirements for MR acquisitions can be relaxed. This could be a major step forward to turn electrical properties tomography into a reliable biomarker where pathological conditions can be revealed and characterized by abnormalities in tissue electrical properties.
Highlights
The possibility to non-invasively measure tissue electrical properties of tissues (EPs) at RF frequencies with clinical MRI systems was first suggested in the early 1990s9
Instead of relying on analytical or algebraic reconstruction techniques derived from electromagnetic theory, and given the potential of data driven approaches, in this work we investigate the feasibility of using a data driven, supervised deep learning (DL) approach for EPs reconstructions
A novel approach for EPs reconstructions is presented, namely deep learning electrical properties tomography (DL-EPT). This technique is based on a data driven learning task, where the training data are obtained from a large number of realistic electromagnetic simulations
Summary
The possibility to non-invasively measure tissue EPs at RF frequencies with clinical MRI systems was first suggested in the early 1990s9. To mitigate the impact of noise in the reconstructed EPs maps, large derivative filters in combination with image filters and large voxel sizes are commonly used[2] This comes at the cost of severe errors at tissue boundaries, making MR-EPT reconstructions challenging, especially for highly spatially convoluted tissue structures such as the human brain[14]. From a fundamental point of view, these analytical reconstruction techniques are attractive due to their direct forward mathematical formulation allowing fast reconstructions These methods are sensitive to noise in the input data and require relatively high SNR levels that are not always feasible at clinical MR field strengths
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