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Photoacoustic imaging (PAI) provides high-resolution and high-optical-contrast imaging beyond optical diffusion limit. Further improvement in imaging depth has been achieved by using near-infrared window-I (NIR-I, 700 to 900 nm) for illumination, due to lower scattering and absorption by tissues in this wavelength range. Recently, near-infrared window-II (NIR-II, 900 to 1700 nm) has been explored for PAI. We studied the imaging depths in biological tissues for different illumination wavelengths in visible, NIR-I, and NIR-II regions using Monte Carlo (MC) simulations and validated with experimental results. MC simulations were done to compute fluence in tissue, absorbance in blood vessel, and in a spherical absorber (mimicking sentinel lymph node) embedded at different depths in breast tissue. Photoacoustic tomography and acoustic resolution photoacoustic microscopy experiments were conducted to validate the MC results. We demonstrate that maximum imaging depth is achieved by wavelengths in NIR-I window (∼800  nm) when the energy density deposited is same for all wavelengths. However, illumination using wavelengths around 1064 nm (NIR-II window) gives the maximum imaging depth when the energy density deposited is proportional to maximum permissible exposure (MPE) at corresponding wavelength. These results show that it is the higher MPE of NIR-II window that helps in increasing the PAI depth for chromophores embedded in breast tissue.

Biomedical photoacoustic tomography, which can provide high-resolution 3D soft tissue images based on optical absorption, has advanced to the stage at which translation from the laboratory to clinical settings is becoming possible. The need for rapid image formation and the practical restrictions on data acquisition that arise from the constraints of a clinical workflow are presenting new image reconstruction challenges. There are many classical approaches to image reconstruction, but ameliorating the effects of incomplete or imperfect data through the incorporation of accurate priors is challenging and leads to slow algorithms. Recently, the application of deep learning (DL), or deep neural networks, to this problem has received a great deal of attention. We review the literature on learned image reconstruction, summarizing the current trends and explain how these approaches fit within, and to some extent have arisen from, a framework that encompasses classical reconstruction methods. In particular, it shows how these techniques can be understood from a Bayesian perspective, providing useful insights. We also provide a concise tutorial demonstration of three prototypical approaches to learned image reconstruction. The code and data sets for these demonstrations are available to researchers. It is anticipated that it is in in vivo applications—where data may be sparse, fast imaging critical, and priors difficult to construct by hand—that DL will have the most impact. With this in mind, we conclude with some indications of possible future research directions.

Reversibly photo-switchable proteins are essential for many super-resolution fluorescence microscopic and optoacoustic imaging methods. However, they have yet to be used as sensors that measure the distribution of specific analytes at the nanoscale or in the tissues of live animals. Here we constructed the prototype of a photo-switchable Ca 2+ sensor based on GCaMP5G that can be switched with 405/488-nm light and describe its molecular mechanisms at the structural level, including the importance of the interaction of the core barrel structure of the fluorescent protein with the Ca 2+ receptor moiety. We demonstrate super-resolution imaging of Ca 2+ concentration in cultured cells and optoacoustic Ca 2+ imaging in implanted tumor cells in mice under controlled Ca 2+ conditions. Finally, we show the generalizability of the concept by constructing examples of photo-switching maltose and dopamine sensors based on periplasmatic binding protein and G-protein-coupled receptor-based sensors. Calcium and other analytes can be imaged at super-resolution and in vivo with photo-switchable sensors.

Optoacoustic tomography (photoacoustic tomography) is an emerging imaging technology displaying great potential for medical diagnosis and preclinical research. Rationally designing activatable optoacoustic probes capable of diagnosing diseases and locating their foci can bring into full play the role of optoacoustic tomography (OAT) as a promising noninvasive imaging modality. Here we report two xanthene-based optoacoustic probes (C 1 X-OR 1 and C 2 X-OR 2 ) for temporospatial imaging of hepatic alkaline phosphatase (or β-galactosidase) for evaluating and locating drug-induced liver injury (or metastatic tumor). The probes rapidly respond to the disease-specific biomarkers by displaying red-shifted NIR absorption bands and generate prominent optoacoustic signals. Using multispectral optoacoustic tomography (MSOT), we can precisely localize the focus of drug-induced liver injury in mice using C 1 X-OR 1 , and the metastatic tumors using C 2 X-OR 2 . This work suggests that the activatable optoacoustic chromophores may potentially be applied for diagnosing and localizing disease foci, especially smaller and deeper ones. Optoacoustic tomography has great potential for medical diagnosis and preclinical research. Here they develop near infrared activatable xanthene-based optoacoustic probes for diagnosing and localizing drug-induced liver injury and metastasis in mouse models.

The photoacoustic effect is an emerging technology that has sparked significant interest in the research field since an acoustic wave can be produced simply by the incidence of light on a material or tissue. This phenomenon has been extensively investigated, not only to perform photoacoustic imaging but also to develop highly miniaturized ultrasound probes that can provide biologically meaningful information. Therefore, this review aims to outline the materials and their fabrication process that can be employed as photoacoustic targets, both biological and non-biological, and report the main components’ features to achieve a certain performance. When designing a device, it is of utmost importance to model it at an early stage for a deeper understanding and to ease the optimization process. As such, throughout this article, the different methods already implemented to model the photoacoustic effect are introduced, as well as the advantages and drawbacks inherent in each approach. However, some remaining challenges are still faced when developing such a system regarding its fabrication, modeling, and characterization, which are also discussed.

Photoacoustic tomography (PAT), also known as optoacoustic tomography, is an attractive imaging modality that provides optical contrast with acoustic resolutions. Recent progress in the applications of PAT largely relies on the development and employment of ultrasound sensor arrays with many elements. Although on-chip optical ultrasound sensors have been demonstrated with high sensitivity, large bandwidth, and small size, PAT with on-chip optical ultrasound sensor arrays is rarely reported. In this work, we demonstrate PAT with a chalcogenide-based micro-ring sensor array containing 15 elements, while each element supports a bandwidth of 175 MHz (−6 dB) and a noise-equivalent pressure of 2.2 mPaHz −1/2 . Moreover, by synthesizing a digital optical frequency comb (DOFC), we further develop an effective means of parallel interrogation to this sensor array. As a proof of concept, parallel interrogation with only one light source and one photoreceiver is demonstrated for PAT with this sensor array, providing images of fast-moving objects, leaf veins, and live zebrafish. The superior performance of the chalcogenide-based micro-ring sensor array and the effectiveness of the DOFC-enabled parallel interrogation offer great prospects for advancing applications in PAT. The authors report a highly sensitive chalcogenide-based micro-ring sensor array for photoacoustic tomography and develops a compatible parallel interrogation means by synthesizing a digital optical frequency comb. Imaging is demonstrated on fast-moving objects, leaf veins, and live zebrafish.

Significance: Acoustically detecting the rich optical absorption contrast in biological tissues, photoacoustic tomography (PAT) seamlessly bridges the functional and molecular sensitivity of optical excitation with the deep penetration and high scalability of ultrasound detection. As a result of continuous technological innovations and commercial development, PAT has been playing an increasingly important role in life sciences and patient care, including functional brain imaging, smart drug delivery, early cancer diagnosis, and interventional therapy guidance. Aim: Built on our 2016 tutorial article that focused on the principles and implementations of PAT, this perspective aims to provide an update on the exciting technical advances in PAT. Approach: This perspective focuses on the recent PAT innovations in volumetric deep-tissue imaging, high-speed wide-field microscopic imaging, high-sensitivity optical ultrasound detection, and machine-learning enhanced image reconstruction and data processing. Representative applications are introduced to demonstrate these enabling technical breakthroughs in biomedical research. Conclusions: We conclude the perspective by discussing the future development of PAT technologies.

Photoacoustic Tomography (PAT) has experienced rapid development as a biomedical imaging modality in recent years. In PAT, the quality of photoacoustic images depends on the choice of reconstruction algorithm. Currently, back-projection based algorithms are widely used due to their simplicity and computational efficiency. However, traditional back-projection algorithms introduce unwanted artifacts in the form of negative values, especially at the edges of the photoacoustic sources that lack physical significance. To address this issue, we propose an innovative photoacoustic imaging algorithm based on the orthogonal Transform. Through theoretical and experimental validation, this algorithm effectively eliminates negative artifacts without compromising overall image quality, significantly enhancing the interpretability of the images. By applying the orthogonal Transform to the raw photoacoustic data, our proposed algorithm overcomes the common issue of negative values encountered in back-projection-based algorithms, ensuring more accurate and meaningful photoacoustic imaging results.

Photoacoustic imaging (PAI) is an emerging biomedical imaging modality with promise as a point-of-care diagnostic. This imaging modality relies on optical excitation of an absorber followed by production of ultrasound through the photoacoustic effect, resulting in high spatial resolution with imaging depths in the centimeter range. Herein, we disclose the discovery of the first benchmarking parameter for small molecule dye performance in PAI, which we term the acoustic loudness factor (ALF). ALF can predict dye performance in PAI without the need for access to photoacoustic instrumentation and can be used to guide the systematic evaluation of design strategies to enhance photoacoustic signal. Lastly, we demonstrate that enhancements in ALF can be translated to in vivo PAI. Akin to the use of fluorescence brightness in fluorophore design and evaluation for fluorescence imaging, we anticipate that ALF will guide the design and evaluation of improved probes for PAI. The authors report on acoustic loudness factor (ALF), which can be used to benchmark small-molecule photoacoustic probes, analogous to fluorescence brightness. The ALF enables the direct comparison of probes used for photoacoustic imaging.

Photoacoustic tomography (PAT) is intrinsically sensitive to blood oxygen saturation (sO2) in vivo. However, making accurate sO2 measurements without knowledge of tissue- and instrumentation-related correction factors is extremely challenging. We have developed a low-cost flow phantom to facilitate validation of PAT systems. The phantom is composed of a flow circuit of tubing partially embedded within a tissue-mimicking material, with independent sensors providing online monitoring of the optical absorption spectrum and partial pressure of oxygen in the tube. We first test the flow phantom using two small molecule dyes that are frequently used for photoacoustic imaging: methylene blue and indocyanine green. We then demonstrate the potential of the phantom for evaluating sO2 using chemical oxygenation and deoxygenation of blood in the circuit. Using this dynamic assessment of the photoacoustic sO2 measurement in phantoms in relation to a ground truth, we explore the influence of multispectral processing and spectral coloring on accurate assessment of sO2. Future studies could exploit this low-cost dynamic flow phantom to validate fluence correction algorithms and explore additional blood parameters such as pH and also absorptive and other properties of different fluids.