The eyes function through the cooperation of different ocular components, and complications with any components would lead to ocular diseases that deteriorate vision. Hence early and precise detection and monitoring of ocular diseases, along with the improved understanding of pathological mechanisms, becomes essential for successful treatment. Photoacoustic Imaging (PAI) is a non-invasive and non-destructive imaging modality based on the photoacoustic effect, which gives high spatial resolution, sensitivity, contrast and penetration depth. Since PAI can provide anatomic and functional ocular characterizations, it can be a potential tool for medical screening/diagnosis of ocular diseases, staging, treatment, and continuous post-treatment monitoring. Here, we perform deep-tissue imaging of the Goldfish (Carassius auratus) eye using a home-built Photoacoustic microscope to identify various ocular components like iris, crystalline lens, retina, optic nerve and blood vessels. The study can be extended to observe changes in these structures under different ophthalmic disease conditions.
In the past two decades, High-Intensity Focused Ultrasound (HIFU) has been actively investigated for inducing tissue ablation for tumours, healing arteries and even skin tightening for inducing facelifts. Being a non-contact and non-invasive procedure, HIFU is an attractive alternative over other procedures like radio-frequency ablations and microwave ablations as these are semi-invasive methods. Computer simulations in this domain can provide us with a fast-track way to test and deduce results virtually before conducting related experiments. In this study, we simulated the properties of bovine liver tissue virtually in a MATLAB-based k-Wave toolbox to track lesion variations with varying HIFU on-time and with a constant amount of heat deposition. Photoacoustic reconstructions were also carried out with a linear and circular array of transducers using the simple Delay-And-Sum (DAS) algorithm to access the real-time imaging capability of this technique for HIFU therapy. All the results obtained are discussed and validated. This study will, in general, aid in deciding the HIFU configuration and operational parameters with respect to the tissue type, lesion size, and lesion location within the tissue.
Delay-and-sum (DAS) is a widely utilized beamforming algorithm in PAI because of its simplicity and real-time capabilities. However, it leads to strong sidelobes and low resolution. A beamformer called delay-multiplyand-sum (DMAS) was proposed, which improved the image quality compared to DAS in PAI. We introduce a weighting factor for DMAS called LAG-DMAS-SCF based on the phase correlation of signals in photoacoustic imaging (PAI). In LAG-DMAS-SCF, the pair-wised signals from DMAS are reorganized in terms of lag and the sign coherence factor (SCF) is applied based on the new signal to the output of DMAS. We have tested the performance of this technique using simulated and experimental data. We have used an LED-based PAI system for the experimental setup. Results show that the proposed algorithm improves the image quality compared to DAS and DMAS by suppressing sidelobes and reducing noise. In phantom imaging, the proposed algorithm showed improvement of around 45 dB and 30dB in Signal-to-noise ratio (SNR), and improvement of around 30% and 20% in full-width-half-maxima (FWHM) compared to DAS and DMAS, respectively. Hence, this algorithm can be an appropriate choice for improving image quality in the LED-based PAI system.
Delay-and-sum (DAS) beamforming is the most common non-adaptive beamforming algorithm in photoacoustic (PA) and ultrasound (US) imaging due to its simple implementation. However, it suffers from various drawbacks such as low image resolution, low contrast, and higher sidelobes. Several beamforming algorithms and weighting method was developed in last few years to resolve this problems. Herein, we introduce a new weighting factor named p th root coherence factor (PRCF) to enhance the DAS image quality in terms of various aspects. The proposed method provides better resolution and signal-to-noise ratio (SNR) than the conventional DAS and DAS+CF. We quantitatively compared the image quality metrics of the proposed algorithm with the conventional algorithms. It is observed that our proposed algorithm shows better spatial resolution, SNR, and lower sidelobes for both simulated as well as experimental data. The quantitative results of the experiment (using wire targets) shows that PRCF leads about 55% and 17% improvement in FWHM in comparision with DAS and DAS+CF respectively.
We report the development of an opto-acousto-fluidic platform by combining an illumination source in the form of a pulsed laser, a microfluidic channel, and an ultrasound transducer to detect photoacoustic signals generated from the fluid sample inside the channel. We study the effect of the channel dimensions on the emitted acoustic signals using methylene blue solution, a dye of immense interest in processing industry, as a target fluid and select an appropriate channel for further studies. We vary the concentration of the methylene blue dye and collect the corresponding photoacoustic signals. We find that the measured acoustic signal strength varies linearly with the increasing dye concentration, thus making this measurement scheme a potential dye concentration detector. This is a significant finding as it paves the way for developing a miniaturized photoacoustic detector for onsite sensing of dye concentration and perhaps even an online monitoring system which will be radical departure for current analysis methods using bench top bulky and expensive analytical tools.
Temperature dependence of photoacoustic (PA) signal is a crucial factor in PA temperature sensing, imaging, and monitoring of thermal therapies. In this study, we report theoretical and simulation studies to describe the temperature dependence of PA-signal and hence the Gruneisen parameter. A light-absorbing organic dye (Methylene blue) is taken as the sample, and the variation of PA-signal against change in temperature is plotted from the simulated data. Experiments were conducted, employing a home-built photoacoustic microscopy imaging system to measure PA signal, for the validation study. A CW-laser beam (of wavelength, 642nm) is used to vary the temperature of the organic dye during the experiment, and the average PA-signal is measured from the acquired PA image. The change in temperature of the sample due to the laser irradiation is noted separately. PA-signal strength and its amplification against temperature change is plotted from the experimental data. It is observed that the experimental results are well-matched with the theory and simulated data.
Delay and Sum (DAS) is one of the most prevalent and common beamforming algorithms used in ultrasound and photoacoustic image (PAI) reconstruction. This beamforming algorithm is mostly used due to its simple implementation and real time capability. However, it has several drawbacks such as high level side lobes, low image resolution and poor contrast. To address these issues, a new adaptive weighting factor has been proposed in this paper, named delay-multiply-sum-to-standard-deviation-factor (DMSSF). It improves all kinds of drawback like, lateral resolution, signal to noise ratio (SNR), and reduces side lobes of the reconstructed image compare to the other conventional methods like DAS, DAS+CF. The proposed weighting technique leads to get narrower main lobe and make the target much more detectable with respect to background. This novel method contributes significantly to the improvement of linear array based clinical photoacoustic imaging and expands the horizon of DAS image reconstruction and its potentiality.
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