The experimental study of mouse liver in magneto-acousto-electrical tomography by scan mode

Iterative Reconstruction Algorithms in Magneto-Acousto-Electrical Computed Tomography (MAE-CT) for Image Quality Improvement

Magneto-acousto-electrical computed tomography (MAE-CT) is a recently developed rotational magneto-acousto-electrical tomography (MAET) method, which can map the conductivity parameter of tissues with high spatial resolution. Since the imaging mode of MAE-CT is similar to that of CT, the reconstruction algorithms for CT are possible to be adopted for MAE-CT. Previous studies have demonstrated that the filtered back-projection (FBP) algorithm, which is one of the most common CT reconstruction algorithms, can be used for MAE-CT reconstruction. However, FBP has some inherent shortcomings of being sensitive to noise and non-uniform distribution of views. In this study, we introduced iterative reconstruction (IR) method in MAE-CT reconstruction and compared its performance with that of the FBP. The numerical simulation, the phantom, and in vitro experiments were performed, and several IR algorithms (ART, SART, SIRT) were used for reconstruction. The results show that the images reconstructed by the FBP and IR are similar when the data is noise-free in the simulation. As the noise level increases, the images reconstructed by SART and SIRT are more robust to the noise than FBP. In the phantom experiment, noise and some stripe artifacts caused by the FBP are removed by SART and SIRT algorithms. In conclusion, the IR method used in CT is applicable in MAE-CT, and it performs better than FBP, which indicates that the state-of-the-art achievements in the CT algorithm can also be adopted for the MAE-CT reconstruction in the future.

ARU-DGAN: A dual generative adversarial network based on attention residual U-Net for magneto-acousto-electrical image denoising

Magneto-Acousto-Electrical Tomography (MAET) is a multi-physics coupling imaging modality that integrates the high resolution of ultrasound imaging with the high contrast of electrical impedance imaging. However, the quality of images obtained through this imaging technique can be easily compromised by environmental or experimental noise, thereby affecting the overall quality of the imaging results. Existing methods for magneto-acousto-electrical image denoising lack the capability to model local and global features of magneto-acousto-electrical images and are unable to extract the most relevant multi-scale contextual information to model the joint distribution of clean images and noise images. To address this issue, we propose a Dual Generative Adversarial Network based on Attention Residual U-Net (ARU-DGAN) for magneto-acousto-electrical image denoising. Specifically, our model approximates the joint distribution of magneto-acousto-electrical clean and noisy images from two perspectives: noise removal and noise generation. First, it transforms noisy images into clean ones through a denoiser; second, it converts clean images into noisy ones via a generator. Simultaneously, we design an Attention Residual U-Net (ARU) to serve as the backbone of the denoiser and generator in the Dual Generative Adversarial Network (DGAN). The ARU network adopts a residual mechanism and introduces a linear Self-Attention based on Cross-Normalization (CNorm-SA), which is proposed in this paper. This design allows the model to effectively extract the most relevant multi-scale contextual information while maintaining high resolution, thereby better modeling the local and global features of magneto-acousto-electrical images. Finally, extensive experiments on a real-world magneto-acousto-electrical image dataset constructed in this paper demonstrate significant improvements in preserving image details achieved by ARU-DGAN. Furthermore, compared to the state-of-the-art competitive methods, it exhibits a 0.3 dB increase in PSNR and an improvement of 0.47% in SSIM.

Rapid rotational magneto-acousto-electrical tomography with filtered back-projection algorithm based on plane waves

Magneto-acousto-electrical tomography (MAET) is designed to produce conductivity images with high spatial resolution for a conducting object. In a previous study, for an irregular conductor, transverse scanning and rotational methods with a focus transducer were combined to collect complete electrical information. This kind of method, however, is time-consuming because of the transverse scanning procedure. In this study, we proposed a novel imaging method based on plane ultrasound waves and a new aspect of projection in rotational MAET. In the proposed method, we achieved the projection in each rotation angle by using plane waves rather than mechanical scanning of the focus waves along the transverse direction. Thus, the imaging time was significantly saved. To verify the proposed method, we derived a measurement formula containing a lateral integration, which built the relationship between the measurement formula and the projection under each rotation angle. Next, we constructed two different numerical models to compute magneto-acousto-electrical signals by using a finite element method and reconstructed the corresponding conductivity parameter images based on a filtered back-projection algorithm. Then, simulated signals under different signal-to-ratios (6, 20, 40, and 60 dB) were generated to test the performance of the proposed algorithm. To improve the image quality, we further analysed the influence of the filters and the frequency scaling factors embedded in the filtered back-projection algorithm. Moreover, we computed the L₂norm of the error in case of different frequency scaling factors and measurement noises. Finally, we conducted a phantom experiment with a 64-element linear phased array transducer (center frequency of 2.7 MHz) and reconstructed the conductivity parameter images of the circular phantom with an elliptical hole. The experimental results demonstrated the feasibility and time-efficiency of the proposed rapid rotational MAET.