Abstract

As the conductivities of healthy tissue and cancerous tissue are different, methods for detecting conductivity variations of biological tissue are potential medical imaging modalities for early diagnosis of cancer in the future. Magneto-acousto-electrical tomography (MAET) is a noninvasive and hybrid conductivity imaging technology that integrates the advantage of high contrast. It has been demonstrated to have an excellent capability to distinguish conductivity variations along the direction of acoustic propagation and extremely promising as an alternative medical imaging technology for early detection of cancer. However, the existing MAET has a low resolution, and the factors affecting the imaging resolution of MAET are rarely studied systematically. Therefore, we firstly designed an MAET detection system and performed several verification experiments. A B-scan algorithm was then proposed for enhancing the system resolution. Subsequently, two uniform phantoms with different intermediate gaps in the middle and a B-mode imaging experiment on pork tissue were used for testing the performance of detection resolution. Finally, comparative analyses were performed to verify whether the frequency, the cycle number of the excitation signal, and the B-scan algorithm can influence the detection resolution. We obtained the following results: 1) The longitudinal resolution of the conductivity B-scan image increases as the number of cycle excitation decreases. 2) Vertical resolution using a 2.5 MHz probe excitation is significantly better than using a 500 kHz probe excitation. 3) The B-scan algorithm can improve conductivity resolution, and it reveals that our proposed detection system's longitudinal resolution can reach 1mm. 4) we obtained the conductivity profile of pork tissue. The results showed that our detection platform could accurately distinguish the target sample's interfaces of conductivity variations, increasing the excitation frequency, reducing the cycle number, and adopting the B-mode imaging algorithm could improve its conductivity detection resolution, which laid the foundation for the design of a high-resolution MAET detection system.

Highlights

  • Numerous studies have found that conductivity variation within a tissue provides physiological information, reflects the pathological state of biological tissue, and that conductivity variation is a conspicuous feature of most cancer tissues in their early stage [1]

  • The results showed that our detection platform could accurately distinguish the target sample's interfaces of conductivity variations, increasing the excitation frequency, reducing the cycle number, and adopting the B-mode imaging algorithm could improve its conductivity detection resolution, which laid the foundation for the design of a high-resolution magneto-acousto-electrical tomography (MAET) detection system

  • The obtained magneto–acousto– electrical (MAE) waveform after being processed by Wiener filtering was shown in Fig. 4 (c), from which we can see that Wiener filtering significantly reduces external noise interference, and the signal-to-noise ratio (SNR) and detection resolution of the MAE signal is much high

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Summary

Introduction

Numerous studies have found that conductivity variation within a tissue provides physiological information, reflects the pathological state of biological tissue, and that conductivity variation is a conspicuous feature of most cancer tissues in their early stage [1]. The conductivity of healthy breast tissues is much lower than that of breast cancer tissues, and the conductivity of muscle tissue is about ten times that of fat tissue [2]. The conductivity difference can be employed as a sensitive sign for pathological diagnosis. Imagining the electrical impedance of both cancer tissues and surrounding healthy tissues may help the doctors diagnose cancer early and significantly reduce cancer mortality [3].

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