Combining deep learning and 3D contrast source inversion in MR‐based electrical properties tomography. (16th December 2019)

• Record Type: Journal Article
• Authors: Leijsen, Reijer; van den Berg, Cornelis; Webb, Andrew; Remis, Rob; Mandija, Stefano
• Other Names: Zhang Hui guestEditor.; Alexander Daniel C. guestEditor.; Shen Dinggang guestEditor.; Yap Pew‐Thian guestEditor.
• Is Part Of: NMR in biomedicine. Volume 35:Number 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-12-16
• Subjects: conductivity -- contrast source inversion EPT -- deep learning EPT -- electrical properties tomography -- MR‐EPT -- MRI -- permittivity; Nuclear magnetic resonance -- Periodicals; Magnetic Resonance Spectroscopy -- Periodicals; 574
• DOI: 10.1002/nbm.4211
• Languages: English
• ISSNs: 0952-3480

Abstract

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. Abstract : A hybrid method for electrical properties tomography (EPT), which combines deep learning EPT (DL‐EPT) and contrast source inversion EPT (CSI‐EPT), is presented. Its accuracy and precision are investigated by means of realistic electromagnetic simulations at 3 and 7 T. The presented hybrid method shows potential improvements compared with standard EPT methods, since it exploits the power of data‐driven DL‐based EPT reconstructions, while the subsequent CSI‐EPT algorithm facilitates a better generalization by providing data consistency.