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An Explicit EPT Reconstruction Method Based on the Dbar Equation Incorporating Longitudinal Magnetic Field Variations
Motofumi Fushimi1 and Takaaki Nara1

1The University of Tokyo, Tokyo, Japan

Synopsis

This paper presents a novel explicit reconstruction method for magnetic resonance-based electrical properties tomography (EPT). We derive the dbar equation for the electric field that incorporates the variation of the magnetic field along the longitudinal axis. This new dbar equation allows an explicit reconstruction of the electrical properties even when the magnetic field varies along that axis. We also propose an iterative reconstruction procedure that solves the dbar equation by the Cauchy–Pompeiu formula and updates EP values in each step. The proposed method correctly reconstructed EPs even in the case that the magnetic field and EPs vary along the longitudinal direction.

Introduction

Electrical properties tomography (EPT)1 noninvasively retrieves conductivity and permittivity distributions of biological tissues from radio-frequency (RF) magnetic field data obtained by the B1 mapping. Recent studies suggest that the knowledge of these electrical properties (EPs) is useful in oncology2 and dosimetry3. Various EPT methods have been presented so far and they can be divided into two types: partial differential equation (PDE) based local methods4,5 and integral equation (IE) based global methods6,7. In PDE-based local methods, differential operations amplify the noise and make the reconstruction process unstable. On the other hand, IE-based global methods rely on the explicit representation of Green’s function that is valid only in the boundary-free domain and time-consuming finite element method (FEM) calculation is needed to model an RF shield8. In this paper, we extend our previous EPT reconstruction method based on the dbar equation and Cauchy–Pompeiu formula9,10. The proposed method precisely accounts for the effect of magnetic field variations in the longitudinal direction, that was corrected only in an iterative manner in our previous method9.

Methods

By taking the $$$(x+iy)$$$- and $$$z$$$-components of time-harmonic Maxwell’s equations, we have

$$\bar{\partial}E_{z} = \omega\mu_{0}H^{+}+\partial_{z}E^{+},\quad 4\partial H^{+} = -\omega\kappa E_{z}, \quad\partial_{z} H^{+}=\omega\kappa E^{+},$$

where $$$\partial\equiv(\partial_{ x}-i\partial_{y})/2,\,\bar{\partial}\equiv(\partial_{x}+i\partial_{y})/2,\,H^{+}\equiv(H_{x}+iH_{y})/2,\,E^{+}\equiv(E_{x}+iE_{y})/2$$$ and $$$\kappa\equiv\epsilon-i\sigma/\omega$$$. $$$\omega_{0}/2\pi$$$ is the Larmor frequency and $$$\sigma$$$ and $$$\epsilon$$$ are the conductivity and the permittivity, respectively. Eliminating $$$E^{+}$$$ from these equations yields

$$\bar{\partial}E_{z}+\frac{\partial_{z}^{2}H^{+}}{4\partial H^{+}}E_{z} = \omega\mu_{0}H^{+}-\frac{\partial_{z}^{2}H^{+}}{\omega}\partial_{z}\left(\frac{1}{\kappa}\right).$$

According to Vekua11, we introduce an integral operator, $$$T$$$, satisfying $$$\bar{\partial}T[f]=f$$$. It can be shown, then, that the above equation is equivalent to the following equation:

$$\bar{\partial}\left(E_{z}\exp\left(T\left[\frac{\partial_{z}^{2}H^{+}}{4\partial H^{+}}\right]\right)\right)=\left(\omega\mu_{0}H^{+}-\frac{\partial_{z}^{2}H^{+}}{\omega}\partial_{z}\left(\frac{1}{\kappa}\right)\right)\exp\left(T\left[\frac{\partial_{z}^{2}H^{+}}{4\partial H^{+}}\right]\right).$$

This is the dbar equation for $$$E_{z}\exp([A])$$$ where $$$A\equiv\partial_{z}^{2}H^{+}/(4\partial H^{+})$$$ and its solution is explicitly given by the Cauchy–Pompeiu formula9. This new dbar equation includes $$$\partial_{z}H^{+}$$$ and thus is valid even when 2D approximation does not hold. If it holds that $$$\partial_{z}H^{+}=0$$$, the equation is reduced to $$$\bar{\partial}E_{z}=\omega\mu_{0}H^{+}$$$ that we utilized in our previous method. Based on the above, we can construct the reconstruction procedure as follows: First, we assume that $$$\partial_{z}\kappa=0$$$ and reconstruct $$$E_{z}$$$ directly by the following formula:

$$E_{z}(\zeta)=\frac{1}{2\pi i} \int_{\partial D} \frac{-4\partial H^{+}/(\omega\kappa)\exp(T[A](\zeta^{\prime})-T[A](\zeta))}{\zeta^{\prime}-\zeta}d\zeta^{\prime}-\frac{1}{\pi}\iint_{D}\frac{\omega\mu_{0}H^{+}\exp(T[A](\zeta^{\prime})-T[A](\zeta))}{\zeta^{\prime}-\zeta}dx^{\prime}dy^{\prime},$$

where $$$\zeta\equiv x+iy$$$, and calculate $$$\kappa$$$ from $$$E_{z}$$$ by Faraday's law. Then, we correct the dbar equation by using previously obtained $$$\kappa$$$ and solve it again. We note that no iteration is needed if $$$\partial_{z}\kappa=0$$$ holds near the region of interest even in the case that the magnetic field varies along the longitudinal direction.

We evaluated the proposed method by numerical simulations using an FEM simulator, COMSOL Multiphysics (COMSOL Inc.) and by a phantom experiment using 3 T MR scanner (Siemens, Magnetom Prisma). In numerical simulations, we constructed two simulation models (model 1 and 2) that shared the same shape but had different lengths as shown in figure 1. We compare the proposed method with our previous method with 2D approximation and no iterative correction.

Results and Discussion

Figure 2 shows the reconstruction results of model 1 when 1% of Gaussian noise was added. The previous method with 2D approximation yields lower estimations whereas the proposed method reconstructed more precisely. This is because the proposed method correctly accounted for the variation of $$$H^{+}$$$ along the $$$z$$$-direction. Figure 3 shows the reconstructed maps in simulation 2 when the same noise was added. In this case, the proposed method with no correction still yields lower estimations due to the assumption that $$$\partial_{z}\kappa=0$$$. However, only a single iteration sufficiently corrected the results. Figure 4 shows the experimental phantom and the reconstructed conductivity maps. It can be shown that the proposed method successfully distinguished three cylindrical regions with higher conductivity.

The reconstructed maps have spot-like artifacts in low electric-field regions4 and regularization methods should be adopted to alleviate them. Validations using human anatomical models are also our future work.

Conclusion

In this paper, we presented a novel EPT reconstruction method based on the dbar equation of the electric field that accounts for the variation of the magnetic field along the longitudinal axis. Unlike our previous method that corrected the effect of this magnetic field variations in an iterative manner, the proposed method can explicitly reconstruct EPs that are constant in the longitudinal direction near the region of interest even when the magnetic field varies along that axis. We also proposed an iterative reconstruction procedure that solves the dbar equation directly and updates EP values in each step to retrieve three-dimensionally distributed EPs. Numerical simulations and phantom experiments showed that the proposed method correctly reconstructed EPs even in the case that the magnetic field and EPs vary along the longitudinal axis.

Acknowledgements

This work was supported by JSPS KAKENHI Grant Number JP26108003.

References

1. Katscher U, van den Berg CAT. Electric properties tomography: Biochemical, physical and technical background, evaluation and clinical applications. NMR Biomed. 2017;30(8):1–15.

2. Joines WT, Zhang Y, et al. The measured electrical properties of normal and malignant human tissues from 50 to 900 MHz. Med. Phys. 1994;21(4):547–550.

3. Collins CM, Liu S, and Smith MB. SAR and B1 Field Distributions in a Heterogeneous Human Head Model within a Birdcage Coil. Magn. Reson. Med. 1998;40(6):847–856.

4. Hafalir FS, Oran OF, et al. Convection-reaction equation based magnetic resonance electrical properties tomography(cr-MREPT). IEEE Trans. Med. Imag. 2014;33(3):777–793.

5. Ammari H, Kwon H, et al. Magnetic-resonance-based reconstruction method of conductivity and permittivity distribution at Larmor frequency. Inverse Problems. 2015;31(10):105001.

6. Hong R, Li S. 3-D MRI-Based Electrical Properties Tomography Using the Volume Integral Equation Method. IEEE Trans. Microw. Theory Tech. 2017;65(12):4802–4811.

7. Balidemaj E, van den Berg CAT, et al. CSI-EPT: A Contrast Source Inversion Approach for Improved MRI-Based Electric Properties Tomography. IEEE Trans. Med. Imag., 2015;34(9):1788–1796.

8. Arduino A, Zilberti L, et al. CSI-EPT in presence of RF-shield for MR-coils. IEEE Trans on Med Imag. 2017;36(7):1396–1404.

9. Nara T, Furuichi T, Fushimi M. An explicit reconstruction method for magnetic resonance electrical property tomography base on the generalized Cauchy formula. Inverse Problems. 2017;33(10):105005.

10. Fushimi M, Nara T. A Boundary-Value-Free Reconstruction Method for Magnetic Resonance Electrical Properties Tomography Based on the Neumann-type Integral Formula over a Circular Region. SICE JCMSI. 2016;10(6):571–578.

11. Vekua I. Generalized Analytic Functions. Pergamon Press, 1962.

12. Insko EK, Bolinger L. Mapping of the radiofrequency field. J. Magn. Reson. 1993;103(1):82–85.

Figures

Figure 1: Two models used in numerical simulations. Both models have the same EP distributions in the transversal directions but model 2 is shorter than model 1 in the longitudinal direction.

Figure 2: Reconstruction results for model 1. The previous method resulted in lower estimation due to the 2D approximation whereas the proposed method correctly retrieved both EPs.

Figure 3: Reconstruction results for model 2. The proposed method with no iteration resulted in lower estimation due to the assumption that EPs are two-dimensionally distributed. A single iteration in the proposed method properly corrected this error.

Figure 4: Reconstructed conductivity maps in the phantom experiment. The proposed method successfully reconstructed three cylindrical regions with higher conductivity.

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)
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