The delay-and-sum (DAS) algorithm is widely used for beamforming in linear array photoacoustic (PA) imaging systems and is characterized by fast execution. However, these algorithms suffer from various drawbacks, such as low resolution, low contrast, high sidelobe artifacts, and lack of visual coherence. More recently, adaptive weighting was introduced to improve the reconstruction image quality. Unfortunately, the existing state-of-the-art adaptive beamforming algorithms are computationally expensive and do not consider the specific noise characteristics of the acquired ultrasonic signal. In this article, we present a new adaptive weighting factor named the variational coherence factor (VCF), which takes into account the noise level variations of radio frequency data. The proposed technique provides superior results in terms of image resolution, sidelobe reduction, signal-to-noise ratio (SNR), and contrast level improvement. The quantitative results of the microsphere phantom imaging show that the proposed VCF-assisted DAS method leads to 55% and 25% improvements in full-width-at-half-maximum (FWHM) and 57% and 32% improvement in SNR, respectively, compared to the state-of-the-art DAS-based methods. The results demonstrate that the proposed method can effectively improve the reconstructed image quality and deliver satisfactory imaging performance even with a limited number of sensor elements. The proposed method can potentially reduce the instrumentation cost of the PA imaging system and contribute toward the clinical translation of the modality.

Photoacoustic imaging (PAI) using a traditional delay-and-sum (DAS) beamformer suffers from various limitations such as low contrast, low signal-to-noise ratio (SNR) and strong side-lobe influence. Consequently, reconstructed image quality dramatically degrades. The present work improves reconstructed image quality by the use of a novel adaptive weighting algorithm named simplified-delay-multiply-and-standard-deviation (SDMASD). SDMASD combined with established DAS and delay-multiply-and-sum (DMAS) methodologies are presented in this article. This present method can efficiently improve the achievable resolution, contrast and SNR and conquer all the persisting drawbacks present in the DAS and DMAS beamformers. Computational complexity in this method, is reduced from O(N²) to O(N) by using a unique mathematical tool. Therefore, it can be programmable for real-time PA image reconstruction. The performance of the algorithm has been accelerated using GPU programming. It executes 50 times faster than the standard CPU based DAS method. A validation study of the algorithm was carried out both numerically and experimentally. In experimental study, a low-cost (16-elements) linear array transducer in a home-built PAI system was employed to acquire the PA signals. All the results illustrate an excellent capability of the proposed algorithm in improving image quality metrics. The quantitative evaluation of the agar phantom shows that SDMASD leads to an improvement of ~ 45% and ~ 40% in SNR, ~ 20% and ~ 30% in full-width-half-maximum (FWHM) compared to DAS and DMAS respectively. Conclusively, the study demonstrates that the proposed algorithm is very much powerful and promising for a cost-effective PAI system using a low-cost linear array transducer.

A new adaptive weighting method [delay-and-sum-to-delay-standard-deviation factor (DASDSF)] combined with minimum variance (MV) beamforming is introduced in photoacoustic imaging (PAI). Existing MV-based beamformers improve photoacoustic image quality in terms of achieving narrow main lobes and, thus, improving spatial resolution. But, the beamformers give a strong side-lobe signal strength that greatly degrades the reconstructed image contrast. As a feedback weighting factor, DASDSF addresses the persisting side-lobe issue present in MV-beamformed images, i.e., our proposed method is robust against reduction in noises as well as side lobes, and it outperforms MV and MV combined with coherence factor beamformers. Validation studies—being carried out both in numerical simulation and experiments employing a low-cost (16 elements) linear transducer array in a home-built PAI system—demonstrate an excellent performance of the proposed weighting approach in improving SNR, while reducing main-lobe width (i.e., FWHM) and side-lobe signal strength. The study demonstrates that the proposed algorithm holds promise for development of a cost-effective PAI system using a low-cost linear transducer ( ∼ 16 elements against ∼ 128 generally used).

Use of portable and affordable pulse light sources (light emitting diodes (LED) and laser diodes) for tissue illumination offers an opportunity to accelerate the clinical translation of photoacoustic imaging (PAI) technology. However, imaging depth in this case is limited because of low output (optical) power of these light sources. In this work, we developed a noninvasive technique for enhancing strength (amplitude) of photoacoustic (PA) signal. This is a photothermal-based technique in which a continuous wave (CW) optical beam, in addition to short-pulse $\left(~ \mathrm{nsec}\right)$ laser beam, is employed to irradiate and, thus, raise the temperature of sample material selectively over a pre-specified region of interest (we call the process as pre-illumination). The increase in temperature, in turn enhances the PA-signal strength. Experiments were conducted in methylene blue, which is one of the commonly used contrast agents in laboratory research studies, to validate change in temperature and subsequent enhancement of PA-signal strength for the following cases: (1) concentration or optical absorption coefficient of sample, (2) optical power of CW-optical beam, and (3) time duration of pre-illumination. A theoretical hypothesis, being validated by numerical simulation, is presented. To validate the proposed technique for clinical and/or pre-clinical applications (diagnosis and treatments of cancer, pressure ulcers, and minimally invasive procedures including vascular access and fetal surgery), experiments were conducted in tissue-mimicking Agar phantom and ex-vivo animal tissue (chicken breast). Results demonstrate that pre-illumination significantly enhances PA-signal strength (up to $~70\%$ (methylene blue), $~48\%$ (Agar phantom), and $~40\%$ (chicken tissue)). The proposed technique addresses one of the primary challenges in the clinical translation of LED-based PAI systems (more specifically, to obtain a detectable PA-signal from deep-seated tissue targets).

Observation and characterization of any changes in anatomical structures of ocular components remain as a conventional technique for diagnosis, staging, therapeutic treatments, and post-treatment monitoring of any ophthalmic disorders. The existing technologies fail to provide imaging of all of the various components of the eye simultaneously at one scanning time, i.e., one can recover vital patho-physiological information (structure and bio-molecular content) of the different ocular tissue sections only one after another. This article addresses the longstanding technological challenge by use of an emerging imaging modality [photoacoustic imaging (PAI)] in which we integrated a synthetic aperture reconstruction technique (SAFT). Experimental results—with experiments being conducted in excised tissues (goat eye)—demonstrated that we can simultaneously image the entire structure of the eye (∼2.5 cm) depicting clearly the distinctive ocular structures (cornea, aqueous humor, iris, pupil, eye lens, vitreous humor, and retina). This study uniquely opens an avenue for promising ophthalmic (clinical) applications of high clinical impact.

For the first time, we present photoacoustic (PA) imaging using a single-element (single-cell) piezoelectric micromachined ultrasound transducer (PMUT) with a low resonant frequency of approximately 1 MHz and a compact effective aperture as tiny as 0.06 mm $^{2}$ . Adding to this innovation, we introduce pioneering image enhancement techniques applied directly to images obtained from this standalone single-cell PMUT, aiming to elevate and refine PA image quality. Our approach employs a trio of advanced methods: 1) frame averaging to ensure clarity by reducing image clutter; 2) cutting-edge mathematical algorithms to fine-tune image quality metrics; and 3) precise hardware positioning for accurate image capture. These techniques underwent rigorous validation using both agar phantom and ex-vivo tissue samples. As benchmarks for our progress, we utilized quantifiers including full width at half maximum (FWHM), signal-to-noise ratio (SNR), and contrast ratio (CR). Our results were striking: a 34% improvement in FWHM, a 23% boost in SNR, and a 25% enhancement in CR. These numbers clearly demonstrate the efficacy of our methods in optimizing PA images through a low-frequency PMUT-based detector. In conclusion, our research indicates that these enhancement methods could be instrumental in shaping a cost-effective and efficient future for PMUT-based photoacoustic imaging (PAI) systems.