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Purpose Magnetoacoustic tomography with magnetic induction (MAT‐MI) is a technique that utilizes the acoustic signals induced by magnetic stimulation to reconstruct the electrical impedance distribution in biological tissues. Most algorithms ignored the fact that acoustic properties in human tissues are heterogeneous, which lead to distortion and blurring of small reconstructed objects. In this study, a novel algorithm is proposed for exact reconstruction of the sound source distribution in acoustic heterogeneous tissues. Methods Based on the ring transducer array, we develop an algorithm which combines algebraic reconstruction technique (ART) and time reversal method. Different to existing reconstruction methods, the ultrasonic transmission tomography (UTT) and the MAT‐MI can be completed in same system, which decreases the system complexity. The sound velocity distribution is reconstructed with the ART so that the propagation time of the magnetoacoustic signals in the heterogeneous tissue is corrected. And then, the sound source image is reconstructed based on the time reversal method from new sound pressure data. Both numerical simulations and phantom experiments are established to validate the proposed method. Results Compared with the results without consideration of the variation on acoustic speed, sound sources reconstructed by our method are more consistent with the model in terms of size and shape. Conclusions The novel algorithm can be used to reconstruct the high‐accuracy image of MAT‐MI sound source in the sound velocity inhomogeneous media. In addition, this method is applicable to scenarios that the prior knowledge of the imaging targets is unknown. The signal acquisition time of MAT‐MI in acoustically heterogeneity media is greatly reduced due to the introduction of ring transducer array into the imaging system. Therefore, our method will promote the application of MAT‐MI in noninvasive early diagnosis of tumor for preclinical study.

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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. INDEX TERMS Conductivity distribution, Hilbert transform, electromagnetic interference, weak signal processing and detection, magneto-acousto-electrical voltage.

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