Introduction to the feature issue Photoacoustic Imaging and Sensing: Beyond Fundamentals to Translation

Abstract

This Feature Issue in Biomedical Optics Express, “Photoacoustic Imaging and Sensing: Beyond Fundamentals to Translation” is a collection of seasonal research activities from fundamental science to clinical and industrial translation in the field of photoacoustic imaging.

Photoacoustic imaging (PAI), also referred to as optoacoustic imaging, combines the advantages of optical imaging and ultrasound imaging, providing high contrast and high resolution [1–4]. This advanced technology is rapidly gaining attention in biomedical research and clinical applications. With numerous ongoing clinical trials and the emergence of start-up companies, PAI is poised to revolutionize healthcare. This feature issue explores the challenges and recent advancements that are driving the widespread adoption of this promising technology. It includes four review articles and fifteen original research articles.

Among the four review articles, L. Menozzi et al. review multimodal, label-free optical metabolic imaging techniques that enable high-resolution, non-invasive assessment of cellular function by utilizing intrinsic photon–tissue interactions (such as absorption, emission, and scattering), including PAI, fluorescence microscopy (FLM), OCT, and Raman-based methods [5]. Furthermore, J. Miao et al. review how contrast-enhanced PAI combines optical and acoustic techniques to achieve deep, high-resolution molecular imaging, highlighting the use of FDA-approved dyes and novel contrast agents while addressing regulatory and standardization challenges for clinical translation [6]. From a technical perspective, D. Sankepalle et al. present a customizable, Verasonics-based multi-wavelength photoacoustic and ultrasound imaging platform that enables real-time visualization of tumor hemodynamics and supports functional cancer imaging and therapy monitoring [7]. In addition, X. Peng et al. review the integration of PAI with robotics, demonstrating how it enhances imaging precision, automation, and reproducibility, while addressing limitations such as motion artifacts and operator dependence, and enabling advanced scanning, feedback control, and intervention capabilities [8].

Clinical PAI is important because its successful translation into routine practice would enable clinicians to noninvasively assess tissue vascularity and oxygenation at the bedside, providing actionable information for diagnosis and treatment planning. Achieving robust, reliable, and user-friendly clinical implementation is essential for the technology to move beyond research settings and have a real-world impact on patient care and outcomes. X. Li et al. demonstrate in a pilot study that combining PAI with confocal Raman spectroscopy enables noninvasive, high-resolution assessment of microvascular, functional, and biochemical skin changes in atopic dermatitis, revealing that diabetes and obesity exacerbate vascular abnormalities and skin barrier dysfunction [9]. Similarly, X. Long et al. demonstrate a 3D ultrasound/photoacoustic dual-modality system capable of noninvasively visualizing subcutaneous microvascular networks and quantifying functional parameters such as oxygen saturation in human extremities [10]. K. Koo et al. apply a robotic arm–based ultrasound and PAI system to image human finger joints, particularly for detecting inflammatory arthritis [11]. Toward clinical translation, Y. Huang et al. present a miniaturized, endoscope-compatible photoacoustic–ultrasound imaging catheter that enables real-time, in vivo assessment of gastrointestinal inflammation and fibrosis by quantifying hemoglobin and collagen in a pig model [12].

One of the key criteria for successful clinical and industrial translation of PAI technology is the use of relatively small and cost-effective light sources. Therefore, laser diodes and light-emitting devices (LEDs) have been extensively explored. A. Das and M. Pramanik use low-cost LEDs for photoacoustic computed tomographic (PACT) applications [13], while V. Vincely et al. use near-infrared window II pulsed laser diodes for deep tissue imaging [14]. As a more advanced approach, V. Periyasamy et al. simultaneously use LEDs and laser diodes with a time delay to improve imaging performance [15].

In addition to ultrasound imaging, other optical imaging modalities can be easily integrated with PAI for multimodal imaging applications. Q. Zhou et al. develop a dual-modality optical imaging framework combining photoacoustic microscopy (PAM) and near-infrared-II fluorescence imaging to analyze liver structure and function at the hepatic lobule level, revealing progressive vascular disorganization, increased permeability heterogeneity, and reduced metabolic function during the progression of metabolic dysfunction-associated fatty liver disease [16]. I. Druzhkova et al. compare three aggressive tumor models (U87MG, MKN-45, and MIA PaCa-2) using PAI, immunohistochemistry, and FLIM, showing significant differences in vascularization, vasculogenic mimicry, and metabolism among the tumors [17].

PAM is another advanced modality for small animal imaging and histopathology. L. Zhang et al. introduce a broadband ultraviolet PAM method that combines morphological imaging and spectral analysis to rapidly generate label-free, histology-like images and distinguish normal and cancerous oral tissues with high consistency compared to H&E staining [18]. C. Jia et al. demonstrate that tartrazine, a food-grade dye, can act as an effective optical clearing agent for optical-resolution PAM when used at controlled concentrations, significantly improving in vivo microvascular imaging by reducing light scattering [19]. A. Kurnikov et al. introduce a novel PVDF-TrFE piezopolymer ring-segment detector optimized for optical-resolution PAM, which significantly enhances sensitivity and extends the depth of field beyond 1 mm when combined with a GRIN lens system [20].

Both conventional and deep learning–based image reconstruction methods have been developed to improve photoacoustic image quality. H. Xie et al. propose a hybrid deep learning framework that directly reconstructs PACT images from raw sinogram data using a fully connected network combined with a Swin-UNETR, eliminating the need for conventional multi-step reconstruction methods [21]. Z. Wang et al. propose a variance delay-and-sum algorithm for circular-array PAI that uses signal variance as a weighting factor to enhance the detection of optical absorbers and suppress artifacts compared to traditional methods [22]. S. German et al. demonstrate that photoacoustic techniques can detect phase transitions and track the position and velocity of the crystallization front during freezing-induced loading and freeze-casting, even in highly scattering media [23].

Disclosures

Chulhong Kim has financial interests in OPTICHO, which did not support this work.