Automatic threshold selection algorithm to distinguish a tissue chromophore from the background in photoacoustic imaging

Optical tuning of copolymer-in-oil tissue-mimicking materials for multispectral photoacoustic imaging

Objective. The availability of tissue-mimicking materials (TMMs) for manufacturing high-quality phantoms is crucial for standardization, evaluating novel quantitative approaches, and clinically translating new imaging modalities, such as photoacoustic imaging (PAI). Recently, a gel comprising the copolymer styrene-ethylene/butylene-styrene (SEBS) in mineral oil has shown significant potential as TMM due to its optical and acoustic properties akin to soft tissue. We propose using artists' oil-based inks dissolved and diluted in balsam turpentine to tune the optical properties.Approach. A TMM was fabricated by mixing a SEBS copolymer and mineral oil, supplemented with additives to tune its optical absorption and scattering properties independently. A systematic investigation of the tuning accuracies and relationships between concentrations of oil-based pigments and optical absorption properties of the TMM across visible and near-infrared wavelengths using collimated transmission spectroscopy was conducted. The photoacoustic spectrum of various oil-based inks was studied to analyze the effect of increasing concentration and depth.Main results. Artists' oil-based inks dissolved in turpentine proved effective as additives to tune the optical absorption properties of mineral oil SEBS-gel with high accuracy. The TMMs demonstrated long-term stability and suitability for producing phantoms with desired optical absorption properties for PAI studies.Significance. The findings, including tuning of optical absorption and spectral shape, suggest that this TMM facilitates the development of more sophisticated phantoms of arbitrary shapes. This approach holds promise for advancing the development of PAI, including investigation of the spectral coloring effect. In addition, it can potentially aid in the development and clinical translation of ultrasound optical tomography.

Niche preclinical and clinical applications of photoacoustic imaging with endogenous contrast

In the past decade, photoacoustic (PA) imaging has attracted a great deal of popularity as an emergent diagnostic technology owing to its successful demonstration in both preclinical and clinical arenas by various academic and industrial research groups. Such steady growth of PA imaging can mainly be attributed to its salient features, including being non-ionizing, cost-effective, easily deployable, and having sufficient axial, lateral, and temporal resolutions for resolving various tissue characteristics and assessing the therapeutic efficacy. In addition, PA imaging can easily be integrated with the ultrasound imaging systems, the combination of which confers the ability to co-register and cross-reference various features in the structural, functional, and molecular imaging regimes. PA imaging relies on either an endogenous source of contrast (e.g., hemoglobin) or those of an exogenous nature such as nano-sized tunable optical absorbers or dyes that may boost imaging contrast beyond that provided by the endogenous sources. In this review, we discuss the applications of PA imaging with endogenous contrast as they pertain to clinically relevant niches, including tissue characterization, cancer diagnostics/therapies (termed as theranostics), cardiovascular applications, and surgical applications. We believe that PA imaging's role as a facile indicator of several disease-relevant states will continue to expand and evolve as it is adopted by an increasing number of research laboratories and clinics worldwide.

Segmentation and Quantitative Analysis of Photoacoustic Imaging: A Review

Photoacoustic imaging is an emerging biomedical imaging technique that combines optical contrast and ultrasound resolution to create unprecedented light absorption contrast in deep tissue. Thanks to its fusional imaging advantages, photoacoustic imaging can provide multiple structural and functional insights into biological tissues such as blood vasculatures and tumors and monitor the kinetic movements of hemoglobin and lipids. To better visualize and analyze the regions of interest, segmentation and quantitative analyses were used to extract several biological factors, such as the intensity level changes, diameter, and tortuosity of the tissues. Over the past 10 years, classical segmentation methods and advances in deep learning approaches have been utilized in research investigations. In this review, we provide a comprehensive review of segmentation and quantitative methods that have been developed to process photoacoustic imaging in preclinical and clinical experiments. We focus on the parametric reliability of quantitative analysis for semantic and instance-level segmentation. We also introduce the similarities and alternatives of deep learning models in qualitative measurements using classical segmentation methods for photoacoustic imaging.

Full-view in vivo skin and blood vessels profile segmentation in photoacoustic imaging based on deep learning

Photoacoustic (PA) microscopy allows imaging of the soft biological tissue based on optical absorption contrast and spatial ultrasound resolution. One of the major applications of PA imaging is its characterization of microvasculature. However, the strong PA signal from skin layer overshadowed the subcutaneous blood vessels leading to indirectly reconstruct the PA images in human study. Addressing the present situation, we examined a deep learning (DL) automatic algorithm to achieve high-resolution and high-contrast segmentation for widening PA imaging applications. In this research, we propose a DL model based on modified U-Net for extracting the relationship features between amplitudes of the generated PA signal from skin and underlying vessels. This study illustrates the broader potential of hybrid complex network as an automatic segmentation tool for the in vivo PA imaging. With DL-infused solution, our result outperforms the previous studies with achieved real-time semantic segmentation on large-size high-resolution PA images.

Efficient algorithm for tracking the single target applied to optical-phased-array LiDAR

A centroid dynamic programming track-before-detect algorithm is proposed, which is applied for tracking the moving target with an unknown speed. Using the inertialess scanning based on an optical phased array, the experimental tracking system is established, and the obtained maximum signal-to-noise ratio is 9.97 dB. Targets of different motion states can be accurately tracked with this algorithm. In addition, we innovated the original track-before-detect algorithm by adding the variable step, so that the target with large accelerations can be tracked accurately. The accuracy of our proposed algorithm is verified numerically and experimentally, which shows that our algorithm can be used to track the target trajectory effectively, and the error in extracting the target velocity is below 2%.

Translational Photoacoustic Imaging for Disease Diagnosis, Monitoring, and Surgical Guidance: introduction to the feature issue

This feature issue of Biomedical Optics Express covered all aspects of translational photoacoustic research. Application areas include screening and diagnosis of diseases, imaging of disease progression and therapeutic response, and image-guided treatment, such as surgery, drug delivery, and photothermal/photodynamic therapy. The feature issue also covers relevant developments in photoacoustic instrumentation, contrast agents, image processing and reconstruction algorithms.