Thermoacoustic imaging (TAI) combines microwave energy's penetration depth with ultrasound's spatial resolution for medical imaging. Denoising is crucial in TAI to refine low energy thermoacoustic signals, overcoming depth limitations and improving imaging precision. We utilized the MI-TAT system to capture signals from different phantoms and gather data for training and validation. Our architectural approach harnesses both time and spatial signal features, enabling the design of an advanced deep-learning model.
This paper introduces an innovative approach to enhance the circular scanning-based photoacoustic tomography (CSPAT) system for photoacoustic imaging. The proposed method involves using a circular detection geometry with three carefully placed ultrasound transducers (USTs). By strategically selecting the angles of the USTs, the field of view (FOV) is expanded, and tangential resolution is improved without requiring additional imaging time. The new CS-PAT system demonstrates practicality and convenience, providing higher signal-to-noise ratio and better structural similarity compared to the conventional system. This approach overcomes the limitations of the limited size of USTs and widens the application potential of CS-PAT in a straightforward and efficient manner.
Thermoacoustic imaging (TAI) is a promising new technology for biomedical diagnostics. It combines the high contrast of electromagnetic absorption with the high resolution of ultrasound imaging. Traditional TAI systems use circular scanning modes with single or arc detectors, which can be slow and inefficient for body scanning. A linear-array detector, which is commonly used in medical ultrasound imaging systems, can be used to scan biological tissues more efficiently. In this study, we developed a novel linear-array TAI system (LATIS) for the detection of hemorrhage in the brain through fontanelle in neonates. The LATIS uses a linear-array transducer with multiple elements, which enables rapid data acquisition and real-time imaging. A custom-designed trigger mechanism synchronizes the microwave signal generation and data acquisition process, ensuring accurate timing for optimal image quality. We evaluated the LATIS performance by conducting several ex-vivo sheep brain hemorrhage of different amounts of artificially induced blood. The system was able to successfully detect lower grades of intraventricular hemorrhages in ex-vivo experiments. These results demonstrate the potential of LATIS for clinical imaging of brain hemorrhage in neonates as they are vulnerable to intraventricular hemorrhage.
KEYWORDS: Image restoration, Deep learning, Brain, Signal attenuation, Photoacoustic spectroscopy, Education and training, Data acquisition, Signal to noise ratio, Network architectures, Gallium nitride
We developed a deep learning algorithm, called enhancement Unet (E-Unet), to improve the signal-to-noise ratio (SNR) of signals acquired in a photoacoustic computed microscopy (PAM) system. We tried various combination of custom loss functions which included peak-amplitude, peak-position and mean-squared signal value with Adam optimizer for training purposes. For the testing purposes, we acquired PAM data with complicated phantoms in biological tissue. The performance of the improved signals is evaluated in terms of SNR, structural similarity index (SSIM), root mean square error (RMSE) and Pearson correlation.
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.
Laser scanning photoacoustic microscopy (LS-PAM) is one of the simplest implementations of photoacoustic microscopy (PAM) systems. In this work, we investigate how the sensitivity distribution of the transducer relates to its FOV, which in turn affect the imaging area. Furthermore, the relation between transducer active area and sensitivity distribution will be investigated. Transducers with varying diameters and sensitivity will be compared and their FOV will be evaluated. The results will be quantitatively compared in terms maximum sensitivity distribution, signal to noise ratio (SNR) of the signals received and peak signal to noise ratio (PSNR) of the images produced.
Thermoacoustic imaging (TAI) utilizes the advantages of excellent penetration depth and contrast of microwave energy and the high spatial resolution of ultrasound imaging. We evaluate the use of TAI for the detection of hemorrhage in the neonatal brain through fontanelle. We use a 3D human neonatal brain model, an antenna, and a linear array transducer in simulation to characterize the thermoacoustic signal and corresponding reconstructed images. All the characterizations are conducted using Computer Simulation Technology (CST) Studio Suite based on finite integration in technique (FIT). The absorbed electric field by the target and the time varying heating function data are reconstructed with a spatial resolution of 100 μm. To evaluate the impact of the applied microwave beam on the generated acoustic pressure wave, different pulse widths ranging from 0.01μs - 5μs at different frequencies from 1-3 GHz are tested. We also explore the impact of the type of antenna, by evaluating a horn antenna, a waveguide and a helical antenna.
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