Transceive phase corrected 2D contrast source inversion‐electrical properties tomography

Stijnman, Peter R. S.; Mandija, Stefano; Fuchs, Patrick S.; Berg, C.A.T. Van den; Remis, Rob

doi:10.1002/mrm.28619

Cited by 8 publications

(9 citation statements)

References 36 publications

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“…To provide a quantitative understanding, the boxplots reporting the median of the mean absolute percentage error (MAPE) (see Section S4 of SM) over all the test data (with the 25 th -bottom edge and 75 th -top edge percentiles) and for the TPA denotes the transceive phase assumption [15]. DS test is the testing dataset which has 1080 2D slices (which includes 20 healthy head models and 20 head models with a tumor-like anomaly.…”

Section: Resultsmentioning

confidence: 99%

“…Then, future works should be devoted to the analytical or numerical inclusion of RF shields in the Green's function. In this respect, a possibility could be found in [15], in which the RF shield is numerically implemented to model the RF fields inside the MRI scanner, by using mirror currents. Furthermore, CNNs are not the only possible architecture that can be used to resolve ISPs.…”

Section: Discussionmentioning

confidence: 99%

“…Contrast source inversion based EPT (CSI-EPT) allows to tackle the underlying ISP in its full non-linearity, by acting contemporarily on both the data and state equations [2], [15]- [22], [19]- [21].…”

Section: S1 Csi-eptmentioning

confidence: 99%

See 2 more Smart Citations

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

Zumbo,

Mandija,

Meliadò

et al. 2024

IEEE Open J. Eng. Med. Biol.

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Zumbo,

Mandija,

Meliadò

et al. 2024

IEEE Open J. Eng. Med. Biol.

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“…Our TPA results show a fairly good agreement with the ground truth, even at the relatively high field strength of 7 T. The results are expected to improve with lower field strengths, which are more often used in practice. Additional correction strategies can be applied, such as iterative updates of the receive phase, which has been shown to work for twodimensional implementations of CSI-EPT [35]. Some CSI-EPT implementations circumvent the transceive phase aspect by reformulating CSI-EPT such that it uses amplitude data only [36], [37].…”

Section: Discussionmentioning

confidence: 99%

Effects of Simulated Error-Sources on Different 3-D CSI-EPT Strategies

Leijsen

Brink

Webb

et al. 2021

IEEE Trans. Comput. Imaging

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Three-dimensional contrast source inversionelectrical properties tomography (3-D CSI-EPT) is an iterative reconstruction method that estimates the electrical properties of tissue from transmit field magnetic resonance data. However, in order to bring 3-D CSI-EPT into practice for complex tissue structures and to understand the origin and effect of errors, insight in the sensitivities of reconstruction accuracy to the major error-sources is necessary. In this paper, different strategies for implementing 3-D CSI-EPT, including their iterative structure, are presented, of which the regularized implementation shows the most potential to be used in practice. Moreover, the influence of initialization, noise, stopping criteria, incident fields, B1maps, transceive phase and domain truncation are discussed. We show that of all these different error-sources, initialization, accurate coil models and domain truncation have the most dramatic effect on electrical properties reconstructions in practice.

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“…To this end, the tranceive-phase approximation is often applied, but the validity of this approximation is not fully understood, and may lead to reconstruction errors in conductivity and permittivity profiles [ 28 ]. Fortunately, it is shown in Reference [ 31 ] that improved

phase approximations can be obtained from the tranceive phase by incorporating an iterative-phase correction scheme in the CSI–EPT reconstruction algorithm. This correction scheme seems to reliably retrieve

phase maps from the measured tranceive phase, and leads to improved conductivity and permittivity reconstructions compared with reconstructions that are obtained when tranceive-phase approximation is applied.…”

Section: Discussionmentioning

confidence: 99%

Developments in Electrical-Property Tomography Based on the Contrast-Source Inversion Method

et al. 2019

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The main objective of electrical-property tomography (EPT) is to retrieve dielectric tissue parameters from B ^ 1 + data as measured by a magnetic-resonance (MR) scanner. This is a so-called hybrid inverse problem in which data are defined inside the reconstruction domain of interest. In this paper, we discuss recent and new developments in EPT based on the contrast-source inversion (CSI) method. After a short review of the basics of this method, two- and three-dimensional implementations of CSI–EPT are presented along with a very efficient variant of 2D CSI–EPT called first-order induced current EPT (foIC-EPT). Practical implementation issues that arise when applying the method to measured data are addressed as well, and the limitations of a two-dimensional approach are extensively discussed. Tissue-parameter reconstructions of an anatomically correct male head model illustrate the performance of two- and three-dimensional CSI–EPT. We show that 2D implementation only produces reliable reconstructions under very special circumstances, while accurate reconstructions can be obtained with 3D CSI–EPT.

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Transceive phase corrected 2D contrast source inversion‐electrical properties tomography

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 8 publications

References 36 publications

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

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

Effects of Simulated Error-Sources on Different 3-D CSI-EPT Strategies

Developments in Electrical-Property Tomography Based on the Contrast-Source Inversion Method

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