Coronaviruses, which have been enveloped nonsegmented positive‐sense RNA viruses, were first mentioned in the mid‐1960s and can attack people as well as a wide range of animals (including mammals and birds). Three zoonotic coronaviruses have been identified as the cause of large‐scale epidemics over the last two decades: Middle East respiratory syndrome (MERS), severe acute respiratory syndrome (SARS), and swine acute diarrhea syndrome (SADS). Epithelial cells in the respiratory and gastrointestinal tract are the principal targeted cells, and viral shedding occurs via these systems in diverse ways such as through fomites, air, or feces. Patients infected with the novel coronavirus (2019‐nCoV) reported having visited the Wuhan seafood wholesale market shortly before disease onset. The clinical data on established 2019‐nCoV cases reported so far indicate a milder disease course than that described for patients with SARS‐CoV or MERS‐CoV. This study aimed to review radiation inactivation of these viruses in the food industry in three sections: visible light, ionizing radiation (alpha ray, beta ray, X‐ray, gamma ray, neutron, plasma, and ozone), and nonionizing radiation (microwave, ultraviolet, infrared, laser light, and radiofrequency). Due to the obvious possibility of human‐to‐human and animal‐to‐human transmission, using at least one of these three methods in food processing and packaging during coronavirus outbreaks will be critical for preventing further outbreaks at the community level.

Transfontanelle ultrasound imaging (TFUI) is the conventional approach for diagnosing brain injury in neonates. Despite being the first stage imaging modality, TFUI lacks accuracy in determining the injury at an early stage due to degraded sensitivity and specificity. Therefore, a modality like photoacoustic imaging that combines the advantages of both acoustic and optical imaging can overcome the existing TFUI limitations. Even though a variety of transducers have been used in TFUI, it is essential to identify the transducer specification that is optimal for transfontanelle imaging using the photoacoustic technique. In this study, we evaluated the performance of 6 commercially available ultrasound transducer arrays to identify the optimal characteristics for transfontanelle photoacoustic imaging. We focused on commercially available linear and phased array transducer probes with center frequencies ranging from 2.5MHz to 8.5MHz which covers the entire spectrum of the transducer arrays used for brain imaging. The probes were tested on both in vitro and ex vivo brain tissue, and their performance in terms of transducer resolution, size, penetration depth, sensitivity, signal to noise ratio, signal amplification and reconstructed image quality were evaluated. The analysis of selected transducers in these areas allowed us to determine the optimal transducer for transfontanelle imaging, based on vasculature depth and blood density in tissue using ex vivo sheep brain. The outcome of this evaluation identified the two most suitable ultrasound transducer probes for transfontanelle photoacoustic imaging.

The ultimate goal of photoacoustic tomography is to accurately map the absorption coefficient throughout the imaged tissue. Most studies either assume that acoustic properties of biological tissues such as speed of sound (SOS) and acoustic attenuation are homogeneous or fluence is uniform throughout the entire tissue. These assumptions reduce the accuracy of estimations of derived absorption coefficients (DeACs). Our quantitative photoacoustic tomography (qPAT) method estimates DeACs using iteratively refined wavefield reconstruction inversion (IR-WRI) which incorporates the alternating direction method of multipliers to solve the cycle skipping challenge associated with full wave inversion algorithms. Our method compensates for SOS inhomogeneity, fluence decay, and acoustic attenuation. We evaluate the performance of our method on a neonatal head digital phantom.

A synthetic phantom model is typically utilized to evaluate the initial performance of a photoacoustic image reconstruction algorithm. The characteristics of the phantom model (structural, optical, and acoustic) are required to be very similar to those of the biological tissue. Typically, generic two-dimensional shapes are used as imaging targets to calibrate reconstruction algorithms. However, these structures are not representative of complex biological tissue, and therefore the artifacts that exist in reconstructed images of biological tissue vasculature are ignored. Real data from 3D MRI/CT volumes can be extrapolated to create high-quality phantom models; however, these sometimes involve complicated pre-processing and mostly are challenging, due to the inaccessibility of these datasets or the requirement for approval to utilize the data. Therefore, it is necessary to develop a 3D tissue-mimicking phantom model consisting of different compartments with characteristics that can be easily modified. In this tutorial, we present an optimized development process of a generic 3D complex digital vasculature phantom model in Blender. The proposed workflow is such that an accurate and easily editable digital phantom can be developed. Other workflows for creating the same phantom will take much longer to set up and require more time to edit. We have made a few examples of editable 3D phantom models, which are publicly available to test and modify.

Distortions caused by the presence of skull remain a barrier to the use of photoacoustic imaging for accurate functional mapping of the human brain. Many have tried to understand this problem by the use of computationally designed skull models, but these efforts have largely failed to predict acoustic distortions with any precision, likely because they are lacking key geometric and material features. Here, we develop simulations that enable us to observe the path of intracranially-generated acoustic signals utilizing a model of the human skull which has been generated via micro computed tomography (μCT) of actual human skull bone. The implementation of real skull geometry reflects experimental conditions, where attenuation, distortion, and signal reverberation occur during acoustic propagation caused by the presence of porosity and changes in geometric shape. In this paper, we begin by analyzing the acoustic signals received after propagating through the skull in 2D simulations. We then extended our investigation to 3D simulations, performing transcranial imaging using various skull models to further study the effects of realistic skull representations on reconstructed images. By accounting for the heterogeneity of the skull, we examine the influence of each model on the photoacoustic signal in greater detail. In addition, aberration correction proved significantly more challenging for the micro-CT–derived model, underscoring the limitations of previously used models.

Photoacoustic imaging (PAI) is a rapidly developing biomedical imaging technology. Linear array-based photoacoustic tomography (LA-PAT) is one of the most popular configurations of cross-sectional PAI due to its simplicity and clinical translatability. However, when using an optical fiber for LA-PAT, the optical beam shape is deformed due to rapid divergence and, therefore, a larger area on the tissue is illuminated (and the illumination across the linear array is non-uniform), leading to the acquisition of PA signals outside the desired cross-section, which generates artifacts and degrades image resolution. A Powell lens is an optical element that converts a circular beam profile to a nearly linear flat-top profile. In this paper, a Powell lens is used to generate a uniform line illumination scheme that is evaluated with Zemax OpticStudio 2023 R1.02. The system is then characterized experimentally, and the performance is compared with a conventional illumination scheme in LA-PAT.