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.
Keyphrases
- image quality
- high resolution
- deep learning
- computed tomography
- machine learning
- dual energy
- electron microscopy
- convolutional neural network
- optical coherence tomography
- finite element
- minimally invasive
- healthcare
- neural network
- wastewater treatment
- human milk
- single cell
- loop mediated isothermal amplification
- contrast enhanced
- single molecule
- working memory
- fluorescence imaging
- photodynamic therapy
- preterm birth