Development and characterization of thermoacoustic imaging system using rotational single element transducer

Abstract

Microwave-induced thermoacoustic tomography has the advantage of a high spatial resolution and a deep imaging depth. This method has been extensively explored over the past decade to find an alternative of existing imaging techniques. In this study, we have developed a compact microwave-induced thermoacoustic tomography (MI-TAT) with a waveguide antenna and a rotating ultrasound transducer unit. We performed a characterization study of the system in terms of pulse width, selection of microwave frequency and resolution. Then the optimized parameters were used to image in-vitro complex structure phantoms. Later, we expanded our system capability for spectroscopic study by imaging different concentrations of methanol and water to mimic the tissue properties and analyze them based on the absorption characteristics of these materials. We hope, this spectroscopic capability broadens the capability of thermoacoustic system to separate the diseased tissue from the healthy one (e.g., malignant from benign) with a high sensitivity and specificity.

Development of a thermoacoustic imaging system to image blood in the brain: preliminary ex-vivo results

“…In this study, we have developed a linear-array TAI system (LATIS) using readily accessible components, showcasing its capability to identify lower-grade hemorrhages situated deep within the brain following our previous work [31][32][33][34][35][36][37].…”

Signal denoising for thermal acoustic imaging

“…Thermoacoustic imaging (TAI) combines microwave energy's penetration depth with ultrasound's spatial resolution for medical imaging [1,2]. Microwave energy penetrates deep into the tissue and provides a valuable information about the internal structure of the tissue.…”

“…These denoising techniques include signal processing tools, machine and deep learning tools and some other innovative algorithms. Deep learning has the potential to learn complex relationships and excel in extracting meaning information [1][2][3][4][5][6][7][8][9][10]. In paper [11], the author proposes the application of Convolutional Neural Network Denoising Auto-Encoders to intelligently filter noise in aircraft engine gas path health signals, enhancing signal quality for improved diagnostic accuracy.…”