Literature DB >> 31251184

A Microwave-Induced Thermoacoustic Imaging System With Non-Contact Ultrasound Detection.

Ajay Singhvi, Kevin C Boyle, Mojtaba Fallahpour, Butrus T Khuri-Yakub, Amin Arbabian.   

Abstract

Portable and easy-to-use imaging systems are in high demand for medical, security screening, nondestructive testing, and sensing applications. We present a new microwave-induced thermoacoustic imaging system with non-contact, airborne ultrasound (US) detection. In this system, a 2.7 GHz microwave excitation causes differential heating at interfaces with dielectric contrast, and the resulting US signal via the thermoacoustic effect travels out of the sample to the detector in air at a standoff. The 65 dB interface loss due to the impedance mismatch at the air-sample boundary is overcome with high-sensitivity capacitive micromachined ultrasonic transducers with minimum detectable pressures (MDPs) as low as 278 μ Pa rms and we explore two different designs-one operating at a center frequency of 71 kHz and another at a center frequency of 910 kHz. We further demonstrate that the air-sample interface presents a tradeoff with the advantage of improved resolution, as the change in wave velocity at the interface creates a strong focusing effect alongside the attenuation, resulting in axial resolutions more than 10× smaller than that predicted by the traditional speed/bandwidth limit. A piecewise synthetic aperture radar (SAR) algorithm modified for US imaging and enhanced with signal processing techniques is used for image reconstruction, resulting in mm-scale lateral and axial image resolution. Finally, measurements are conducted to verify simulations and demonstrate successful system performance.

Year:  2019        PMID: 31251184     DOI: 10.1109/TUFFC.2019.2925592

Source DB:  PubMed          Journal:  IEEE Trans Ultrason Ferroelectr Freq Control        ISSN: 0885-3010            Impact factor:   2.725


  1 in total

1.  The study on the inverse problem of applied current thermoacoustic imaging based on generative adversarial network.

Authors:  Liang Guo; Su Li; Xiangye Wang; Caihong Zeng; Chunyu Liu
Journal:  Sci Rep       Date:  2021-11-25       Impact factor: 4.379

  1 in total

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