Combining deep learning and 3D contrast source inversion in MR-based electrical properties tomography

Reijer Leijsen, Cornelis van den Berg, Andrew Webb, Rob Remis, Stefano Mandija

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)
20 Downloads (Pure)

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.

Original languageEnglish
Article numbere4211
Pages (from-to)1-7
Number of pages7
JournalNMR in Biomedicine
DOIs
Publication statusE-pub ahead of print - 2019

Keywords

  • conductivity
  • contrast source inversion EPT
  • deep learning EPT
  • electrical properties tomography
  • MR-EPT
  • MRI
  • permittivity

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