A B-Scan Imaging Method of Conductivity Variation Detection for Magneto–Acousto– Electrical Tomography

Based on the Hall effect, a magneto-acousto-electrical tomography (MAET) has been indicated to have a good ability for distinguishing the conductivity variations along the acoustic propagation direction, and B-scan imaging of the MAET is expected to obtain pathological information of the tissue. For achieving a clear B-scan image, in this paper, we designed and implemented a MAET system with a planar transducer and conducted a series of experiments to explore the characteristics of the magneto-acoustic-electrical (MAE) signal and electromagnetic interference (EMI) signal. The influence of the EMI signal on the MAE voltage signal was demonstrated experimentally, and the generation mechanism of the EMI signal was explained. Concurrently, several effective methods were proposed for reducing the EMI signal and improving the imaging resolution of the B-scan image. Additionally, for obtaining a B-scan image with high resolution, the detection front end was redesigned and algorithms applying the characteristics of the MAE signal were proposed. The accuracy, feasibility, and effectiveness of the improved methods were verified. Finally, a B-scan image was reconstructed with the relative amplitude of the conductivity. The results showed that: 1) the MAE signal obtained by the redesigned platform could be well separated from the EMI signal and had a higher SNR than that obtained by the previous detection system; 2) the proposed imaging algorithms had a high detection accuracy and achieved an axis resolution of 1 mm on the z-axis; and 3) the interfaces of the conductivity changes of homogeneous phantoms with 0.5% salinity were clearly presented by measuring the MAE wave packets, and the measured thicknesses of the phantoms were highly consistent with the actual thicknesses. This paper provides a theoretical and experimental basis for detecting the interface positions of conductivity variation, and the presented MAET technology is expected to become an alternative medical imaging modality for the early diagnosis and detection of biological cancerous tissues.