Photoacoustic Microscopy Imaging Research Articles

UPAMNet: A unified network with deep knowledge priors for photoacoustic microscopy

Photoacoustic microscopy (PAM) has gained increasing popularity in biomedical imaging, providing new opportunities for tissue monitoring and characterization. With the development of deep learning techniques, convolutional neural networks have been used for PAM image resolution enhancement and denoising. However, there exist several inherent challenges for this approach. This work presents a Unified PhotoAcoustic Microscopy image reconstruction Network (UPAMNet) for both PAM image super-resolution and denoising. The proposed method takes advantage of deep image priors by incorporating three effective attention-based modules and a mixed training constraint at both pixel and perception levels. The generalization ability of the model is evaluated in details and experimental results on different PAM datasets demonstrate the superior performance of the method. Experimental results show improvements of 0.59 dB and 1.37 dB, respectively, for 1/4 and 1/16 sparse image reconstruction, and 3.9 dB for image denoising in peak signal-to-noise ratio.

Efficient Assessment of Tumor Vascular Shutdown by Photodynamic Therapy on Orthotopic Pancreatic Cancer Using High-Speed Wide-Field Waterproof Galvanometer Scanner Photoacoustic Microscopy.

To identify the vascular alteration by photodynamic therapy (PDT), the utilization of high-resolution, high-speed, and wide-field photoacoustic microscopy (PAM) has gained enormous interest. The rapid changes in vasculature during PDT treatment and monitoring of tumor tissue activation in the orthotopic pancreatic cancer model have received limited attention in previous studies. Here, a fully two-axes waterproof galvanometer scanner-based photoacoustic microscopy (WGS-PAM) system was developed for in vivo monitoring of dynamic variations in micro blood vessels due to PDT in an orthotopic pancreatic cancer mouse model. The photosensitizer (PS), Chlorin e6 (Ce6), was utilized to activate antitumor reactions in response to the irradiation of a 660 nm light source. Microvasculatures of angiogenesis tissue were visualized on a 40 mm2 area using the WGS-PAM system at 30 min intervals for 3 h after the PDT treatment. The decline in vascular intensity was observed at 24.5% along with a 32.4% reduction of the vascular density at 3 h post-PDT by the analysis of PAM images. The anti-vascularization effect was also identified with fluorescent imaging. Moreover, Ce6-PDT increased apoptotic and necrotic markers while decreasing vascular endothelial growth factor (VEGF) expression in MIA PaCa-2 and BxPC-3 pancreatic cancer cell lines. The approach of the WGS-PAM system shows the potential to investigate PDT effects on the mechanism of angiographic dynamics with high-resolution wide-field imaging modalities.

Longitudinal Monitoring of Angiogenesis in a Murine Window Chamber Model In Vivo.

Angiogenesis induced by growth factor administration, which can augment the blood supply in regenerative applications, has drawn wide attention in medical research. Longitudinal monitoring of vascular structure and development in vivo is important for understanding and evaluating the dynamics of involved biological processes. In this work, a dual-modality imaging system consisting of photoacoustic microscopy (PAM) and optical coherence tomography (OCT) was applied for noninvasive in vivo imaging of angiogenesis in a murine model. Fibrin scaffolds, with and without basic fibroblast growth factor (bFGF), were implanted in a flexible imaging window and longitudinally observed over 9 days. Imaging was conducted at 3, 5, 7, and 9 days after implantation to monitor vascularization in and around the scaffold. Several morphometric parameters were derived from the PAM images, including vessel area density (VAD), total vessel length (TVL), and vessel mean diameter (VMD). On days 7 and 9, mice receiving bFGF-laden fibrin gels exhibited significantly larger VAD and TVL compared to mice with fibrin-only gels. In addition, VMD significantly decreased in +bFGF mice versus fibrin-only mice on days 7 and 9. Blood vessel density, evaluated using immunohistochemical staining of explanted gels and underlying tissue on day 9, corroborated the findings from the PAM images. Overall, the experimental results highlight the utility of a dual-modality imaging system in longitudinally monitoring of vasculature in vivo with high resolution and sensitivity, thereby providing an effective tool to study angiogenesis.

Advanced nanomaterials for imaging of eye diseases.

Vision impairment and blindness present significant global challenges, with common causes including age-related macular degeneration, diabetes, retinitis pigmentosa, and glaucoma. Advanced imaging tools, such as optical coherence tomography, fundus photography, photoacoustic microscopy, and fluorescence imaging, play a crucial role in improving therapeutic interventions and diagnostic methods. Contrast agents are often employed with these tools to enhance image clarity and signal detection. This review aims to explore the commonly used contrast agents in ocular disease imaging. The first section of the review delves into advanced ophthalmic imaging techniques, outlining their importance in addressing vision-related issues. The emphasis is on the efficacy of therapeutic interventions and diagnostic methods, establishing a foundation for the subsequent exploration of contrast agents. This review focuses on the role of contrast agents, with a specific emphasis on gold nanoparticles, particularly gold nanorods. The discussion highlights how these contrast agents optimize imaging in ocular disease diagnosis and monitoring, emphasizing their unique properties that enhance signal detection and imaging precision. The final section, we explores both organic and inorganic contrast agents and their applications in specific conditions such as choroidal neovascularization, retinal neovascularization, and stem cell tracking. The review concludes by addressing the limitations of current contrast agent usage and discussing potential future clinical applications. This comprehensive exploration contributes to advancing our understanding of contrast agents in ocular disease imaging and sets the stage for further research and development in the field.

Unsupervised Learning for Enhanced Computed Photoacoustic Microscopy

Photoacoustic microscopy (PAM) is a medical-imaging technique with the merits of high contrast and resolution. Nevertheless, conventional PAM scans specimens in a diameter-by-diameter fashion, resulting in a time-consuming process. Furthermore, deep-learning-based PAM image enhancement necessitates acquiring ground-truth data for training purposes. In this paper, we built an optical-resolution photoacoustic microscopy system and introduced an innovative unsupervised-learning algorithm. First, we enhanced the rotational-scanning method, transitioning from a diameter-by-diameter approach to a sector-by-sector one, significantly reducing imaging time (from 280 s to 109 s). Second, by establishing a metric for unsupervised learning, we eliminated the need for collecting reliable and high-quality ground truth, which is a challenging task in photoacoustic microscopy. A total of 324 pairs of datasets (mouse ears) were collected for unsupervised learning, with 274 for training and 50 for testing. Additionally, carbon-fiber data were sampled for lateral resolution and contrast evaluation, as well as the effective rate evaluation of the algorithm. The enhanced images demonstrated superior performance compared with that of maximum projection, both subjectively and objectively. A 76% improvement in the lateral resolution was observed. The effective rate of the algorithm was measured to be 100%, which was tested on 50 random samples. The technique presented in this paper holds substantial potential for image postprocessing and opens new avenues for unsupervised learning in photoacoustic microscopy.

Sounding out the dynamics: a concise review of high-speed photoacoustic microscopy.

Photoacoustic microscopy (PAM) offers advantages in high-resolution and high-contrast imaging of biomedical chromophores. The speed of imaging is critical for leveraging these benefits in both preclinical and clinical settings. Ongoing technological innovations have substantially boosted PAM's imaging speed, enabling real-time monitoring of dynamic biological processes. This concise review synthesizes historical context and current advancements in high-speed PAM, with an emphasis on developments enabled by ultrafast lasers, scanning mechanisms, and advanced imaging processing methods. We examine cutting-edge innovations across multiple facets of PAM, including light sources, scanning and detection systems, and computational techniques and explore their representative applications in biomedical research. This work delineates the challenges that persist in achieving optimal high-speed PAM performance and forecasts its prospective impact on biomedical imaging. Recognizing the current limitations, breaking through the drawbacks, and adopting the optimal combination of each technology will lead to the realization of ultimate high-speed PAM for both fundamental research and clinical translation.

Photoacoustic imaging of squirrel monkey cortical responses induced by peripheral mechanical stimulation.

Non-human primates (NHPs) are crucial models for studies of neuronal activity. Emerging photoacoustic imaging modalities offer excellent tools for studying NHP brains with high sensitivity and high spatial resolution. In this research, a photoacoustic microscopy (PAM) device was used to provide a label-free quantitative characterization of cerebral hemodynamic changes due to peripheral mechanical stimulation. A 5 × 5 mm area within the somatosensory cortex region of an adult squirrel monkey was imaged. A deep, fully connected neural network was characterized and applied to the PAM images of the cortex to enhance the vessel structures after mechanical stimulation on the forelimb digits. The quality of the PAM images was improved significantly with a neural network while preserving the hemodynamic responses. The functional responses to the mechanical stimulation were characterized based on the improved PAM images. This study demonstrates capability of PAM combined with machine learning for functional imaging of the NHP brain.

Photoacoustic Microscopy Imaging for Advanced Biomedical Applications (Invited)

Photoacoustic Microscopy Imaging for Advanced Biomedical Applications (Invited)

Multimodal photoacoustic microscopy, optical coherence tomography, and fluorescence imaging of USH2A knockout rabbits

Usher syndrome type 2A (USH2A) is a genetic disorder characterized by retinal degeneration and hearing loss. To better understand the pathogenesis and progression of this syndrome, animal models such as USH2A knockout (USH2AKO) rabbits have been developed. In this study, we employed multimodal imaging techniques, including photoacoustic microscopy (PAM), optical coherence tomography (OCT), fundus autofluorescence (FAF), fluorescein angiography (FA), and indocyanine green angiography (ICGA) imaging to evaluate the retinal changes in the USH2AKO rabbit model. Twelve New Zealand White rabbits including USH2AKO and wild type (WT) were used for the experiments. Multimodal imaging was implemented at different time points over a period of 12 months to visualize the progression of retinal changes in USH2AKO rabbits. The results demonstrate that ellipsoid zone (EZ) disruption and degeneration, key features of Usher syndrome, began at the age of 4 months old and persisted up to 12 months. The EZ degeneration areas were clearly observed on the FAF and OCT images. The FAF images revealed retinal pigment epithelium (RPE) degeneration, confirming the presence of the disease phenotype in the USH2AKO rabbits. In addition, PAM images provided high-resolution and high image contrast of the optic nerve and the retinal microvasculature, including retinal vessels, choroidal vessels, and capillaries in three-dimensions. The quantification of EZ fluorescent intensity using FAF and EZ thickness using OCT provided comprehensive quantitative data on the progression of degenerative changes over time. This multimodal imaging approach allowed for a comprehensive and non-invasive assessment of retinal structure, microvasculature, and degenerative changes in the USH2AKO rabbit model. The combination of PAM, OCT, and fluorescent imaging facilitated longitudinal monitoring of disease progression and provided valuable insights into the pathophysiology of USH2A syndrome. These findings contribute to the understanding of USH2A syndrome and may have implications for the development of diagnostic and therapeutic strategies for affected individuals. The multimodal imaging techniques employed in this study offer a promising platform for preclinical evaluation of potential treatments and may pave the way for future clinical applications in patients with Usher syndrome.

Quantitative classification of melasma with photoacoustic microscopy: a pilot study.

The classification of melasma is critical for correct clinical diagnosis, treatment selection, and postoperative measures. However, preoperative quantitative determination of melasma type remains challenging using conventional Wood's lamp and optical dermoscopy techniques. Using photoacoustic microscopy (PAM) to simultaneously obtain the two diagnostic indicators of melanin and blood vessels for melasma classification and perform quantitative analysis to finally achieve accurate classification, rather than relying solely on physicians' experience. First, the patients were classified by experienced dermatologists with Wood's lamp and optical dermoscopy. Next, the patients were examined in vivo using the PAM imaging system. Further, the horizontal section images ( plane) of epidermal melanin and dermal vascular involvement were extracted from the 3D photoacoustic imaging results, which are important basis for PAM to quantitatively classify melasma. PAM can quantitatively reveal epidermal thickness and dermal vascular morphology in each case and obtain the quantitative diagnostic indicators of melanin and blood vessels. The mean vascular diameter in lesional skin () of epidermal M+V-type was much larger than that in non-lesional skin (), and the mean vascular density in lesional skin was more than three times that in non-lesional skin. Importantly, vascular diameter and density are important parameters for distinguishing M type from M+V type. PAM can obtain the data of epidermal thickness, pigment depth, subcutaneous vascular diameter, and vascular density, and realize the dual standard quantitative melasma classification by combining the parameters of melanin and blood vessels. In addition, PAM can provide new diagnostic information for uncertain melasma types and further refine the typing.

Monitoring angiogenesis in skin autografts using photoacoustic microscopy.

Skin autografts have been broadly used to manage the skin and soft tissue defects. It is important for surgeons to assess the vitality of skin autografts via observing the angiogenesis. However, there is lack of reliable approach for giving the quantitative angiogenesis information on the skin autografts. Recently, photoacoustic microscopy imaging has attracted much attention based on its good performance in angiography. In this study, we aim to monitor angiogenesis in skin autografts via PAM, and further verify its clinical potential for the early prediction of skin autografts clinical outcome. The results indicate that PAM is a feasible, precise, high-resolution, noninvasive technique for the early prediction of necrosis of skin autografts via monitoring the angiogenesis, providing a promising tool for surgeons to use this surgical technology.

Multimodal imaging of laser-induced choroidal neovascularization in pigmented rabbits

This study aimed to demonstrate longitudinal multimodal imaging of laser photocoagulation-induced choroidal neovascularization (CNV) in pigmented rabbits. Six Dutch Belted pigmented rabbits were treated with 12 laser lesions in each eye at a power of 300 mW with an aerial diameter spot size of 500 μm and pulse duration of 100 ms. CNV progression was monitored over a period of 4 months using different imaging techniques including color fundus photography, fluorescein angiography (FA), photoacoustic microscopy (PAM), and optical coherence tomography (OCT). All treated eyes developed CNV with a success rate of 100%. The margin and morphology of CNV were detected and rendered in three dimensions using PAM and OCT. The CNV was further distinguished from the surrounding melanin and choroidal vessels using FDA-approved indocyanine green dye-enhanced PAM imaging. By obtaining PAM at 700 nm, the location and density of CNV were identified, and the induced PA signal increased up to 59 times. Immunohistochemistry with smooth muscle alpha-actin (αSMA) antibody confirmed the development of CNV. Laser photocoagulation demonstrates a great method to create CNV in pigmented rabbits. The CNV was stable for up to 4 months, and the CNV area was measured from FA images similar to PAM and OCT results. In addition, this study demonstrates that contrast agent-enhanced PAM imaging allows for precise visualization and evaluation of the formation of new blood vessels in a clinically-relevant animal model of CNV. This laser-induced CNV model can provide a unique technique for longitudinal studies of CNV pathogenesis that can be imaged with multimodal imaging.

De-Noising of Photoacoustic Microscopy Images by Attentive Generative Adversarial Network.

As a hybrid imaging technology, photoacoustic microscopy (PAM) imaging suffers from noise due to the maximum permissible exposure of laser intensity, attenuation of ultrasound in the tissue, and the inherent noise of the transducer. De-noising is an image processing method to reduce noise, and PAM image quality can be recovered. However, previous de-noising techniques usually heavily rely on manually selected parameters, resulting in unsatisfactory and slow de-noising performance for different noisy images, which greatly hinders practical and clinical applications. In this work, we propose a deep learning-based method to remove noise from PAM images without manual selection of settings for different noisy images. An attention enhanced generative adversarial network is used to extract image features and adaptively remove various levels of Gaussian, Poisson, and Rayleigh noise. The proposed method is demonstrated on both synthetic and real datasets, including phantom (leaf veins) and in vivo (mouse ear blood vessels and zebrafish pigment) experiments. In the in vivo experiments using synthetic datasets, our method achieves the improvement of 6.53 dB and 0.26 in peak signal-to-noise ratio and structural similarity metrics, respectively. The results show that compared with previous PAM de-noising methods, our method exhibits good performance in recovering images qualitatively and quantitatively. In addition, the de-noising processing speed of 0.016 s is achieved for an image with 256×256 pixels, which has the potential for real-time applications. Our approach is effective and practical for the de-noising of PAM images.

Non-uniform image reconstruction for fast photoacoustic microscopy of histology imaging.

Photoacoustic microscopic imaging utilizes the characteristic optical absorption properties of pigmented materials in tissues to enable label-free observation of fine morphological and structural features. Since DNA/RNA can strongly absorb ultraviolet light, ultraviolet photoacoustic microscopy can highlight the cell nucleus without complicated sample preparations such as staining, which is comparable to the standard pathological images. Further improvements in the imaging acquisition speed are critical to advancing the clinical translation of photoacoustic histology imaging technology. However, improving the imaging speed with additional hardware is hampered by considerable costs and complex design. In this work, considering heavy redundancy in the biological photoacoustic images that overconsume the computing power, we propose an image reconstruction framework called non-uniform image reconstruction (NFSR), which exploits an object detection network to reconstruct low-sampled photoacoustic histology images into high-resolution images. The sampling speed of photoacoustic histology imaging is significantly improved, saving 90% of the time cost. Furthermore, NFSR focuses on the reconstruction of the region of interest while maintaining high PSNR and SSIM evaluation indicators of more than 99% but reducing the overall computation by 60%.

Deep learning based on co-registered ultrasound and photoacoustic imaging improves the assessment of rectal cancer treatment response.

Identifying complete response (CR) after rectal cancer preoperative treatment is critical to deciding subsequent management. Imaging techniques, including endorectal ultrasound and MRI, have been investigated but have low negative predictive values. By imaging post-treatment vascular normalization using photoacoustic microscopy, we hypothesize that co-registered ultrasound and photoacoustic imaging will better identify complete responders. In this study, we used in vivo data from 21 patients to develop a robust deep learning model (US-PAM DenseNet) based on co-registered dual-modality ultrasound (US) and photoacoustic microscopy (PAM) images and individualized normal reference images. We tested the model's accuracy in differentiating malignant from non-cancer tissue. Compared to models based on US alone (classification accuracy 82.9 ± 1.3%, AUC 0.917(95%CI: 0.897-0.937)), the addition of PAM and normal reference images improved the model performance significantly (accuracy 92.4 ± 0.6%, AUC 0.968(95%CI: 0.960-0.976)) without increasing model complexity. Additionally, while US models could not reliably differentiate images of cancer from those of normalized tissue with complete treatment response, US-PAM DenseNet made accurate predictions from these images. For use in the clinical settings, US-PAM DenseNet was extended to classify entire US-PAM B-scans through sequential ROI classification. Finally, to help focus surgical evaluation in real time, we computed attention heat maps from the model predictions to highlight suspicious cancer regions. We conclude that US-PAM DenseNet could improve the clinical care of rectal cancer patients by identifying complete responders with higher accuracy than current imaging techniques.

Adaptive enhancement of acoustic resolution photoacoustic microscopy imaging via deep CNN prior

Acoustic resolution photoacoustic microscopy (AR-PAM) is a promising medical imaging modality that can be employed for deep bio-tissue imaging. However, its relatively low imaging resolution has greatly hindered its wide applications. Previous model-based or learning-based PAM enhancement algorithms either require design of complex handcrafted prior to achieve good performance or lack the interpretability and flexibility that can adapt to different degradation models. However, the degradation model of AR-PAM imaging is subject to both imaging depth and center frequency of ultrasound transducer, which varies in different imaging conditions and cannot be handled by a single neural network model. To address this limitation, an algorithm integrating both learning-based and model-based method is proposed here so that a single framework can deal with various distortion functions adaptively. The vasculature image statistics is implicitly learned by a deep convolutional neural network, which served as plug and play (PnP) prior. The trained network can be directly plugged into the model-based optimization framework for iterative AR-PAM image enhancement, which fitted for different degradation mechanisms. Based on physical model, the point spread function (PSF) kernels for various AR-PAM imaging situations are derived and used for the enhancement of simulation and in vivo AR-PAM images, which collectively proved the effectiveness of proposed method. Quantitatively, the PSNR and SSIM values have all achieve best performance with the proposed algorithm in all three simulation scenarios; The SNR and CNR values have also significantly raised from 6.34 and 5.79 to 35.37 and 29.66 respectively in an in vivo testing result with the proposed algorithm.

Photoacoustic Microscopic Imaging of Cerebral Vessels for Intensive Monitoring of Metabolic Acidosis.

Metabolic acidosis as one of the most common perioperative complications has been associated with increased risks for poor prognosis. Routine monitoring methods include blood gas analysis and electrocardiogram, which are limited by time delay effects. And the existing intravital imaging modalities are difficult to achieve in one step. Here, we present a dual-wavelength photoacoustic imaging approach to overcome this dilemma. The aim of this study was to develop a rapid approach for intensive monitoring of acid-base imbalance and cerebral oxygen metabolism. We characterized the cerebrovascular structure by label-free dual-wavelength (532 and 559nm) photoacoustic microscopy in healthy and diabetic mouse models with metabolic acidosis. Concurrently, we developed a single-vessel analysis method to accurately delineate the differential responses of small vessels and quantify the cerebral oxygenation following experimental alteration of pH. We demonstrated that there was an increasing trend in changes of vascular measurements (density, diameter, and relative hemoglobin concentration) and cerebral microvascular oxygen metabolism with the aggravation of acidosis. Furthermore, we established a clinical nomogram for the diagnosis of disease severity and yielded good discrimination ability with area under the curve of 0.920-0.967 and accuracy of 81.9-93.0%. The nomogram was also validated well in the diabetic mouse model with metabolic acidosis. Our photoacoustic imaging approach has great potential for rapid detection of metabolic acidosis and brain oxygen metabolism, which could potentially be applied as a bedside monitoring method for brain protection and timely treatment of acid-base abnormalities.

Organ‐PAM: Photoacoustic Microscopy of Whole‐organ Multiset Vessel Systems

AbstractOrgans maintain their unique functions through a variety of distinctive vessel systems. However, examination of multiset vessels over the entire organ still remains challenging due to the difficulties of assigning multiple vessel systems and labor‐intensive imaging reconstruction technologies. Here, an organ‐level photoacoustic microscopic imaging platform, named Organ‐PAM, with a micron‐scale spatial resolution and ultra‐large field of view (FOV), which can achieve a lateral and axial FOV of up to ≈81 cm2 and ≈30 mm, respectively, is presented. With the assistance of whole‐organ decellularization, optical clearing, and a specifically designed photoacoustic gradient concentration differential angiographic pipeline, the visualization of up to four sets of vessel systems inside diversified organs is successfully achieved with multiple scales. In addition, quantitative analyses of vessel systems in both healthy livers and kidneys as well as cancerous livers with exogenous transplanted tumors are conducted at different stages. Thus, the platform enables high‐efficiency multiset vessel imaging, recognition, and quantification of different organs, providing critical insights into distinct vessel systems under varying pathological conditions.

Photoacoustic microscopy based on transparent piezoelectric ultrasound transducers

Photoacoustic microscopy (PAM), due to its deep penetration depth and high contrast, is playing an increasingly important role in biomedical imaging. PAM imaging systems equipped with conventional ultrasound transducers have demonstrated excellent imaging performance. However, these opaque ultrasonic transducers bring some constraints to the further development and application of PAM, such as complex optical path, bulky size, and difficult to integrate with other modalities. To overcome these problems, ultrasonic transducers with high optical transparency have appeared. At present, transparent ultrasonic transducers are divided into optical-based and acoustic-based sensors. In this paper, we mainly describe the acoustic-based piezoelectric transparent transducers in detail, of which the research advances in PAM applications are reviewed. In addition, the potential challenges and developments of transparent transducers in PAM are also demonstrated.

Research and development of polarized photoacoustic imaging technology

Life is a highly ordered combination, and the basic biological processes of cells and tissues are essentially controlled by the structural order of biomolecular assembly, in which the conformational characteristics of biomolecule arrangement, orientation, helix, and folding are closely related to the physiological functions of biological tissues. In the skin, muscle, and nerve tissues of living animals, for instance, fibrous proteins, collagen, nerve fibers, and DNA frequently exhibit molecular spatial conformation properties such as particular alignment or helical structure, and such tissues have distinct optical polarization responses. The fundamental structural foundation for tissues to carry out certain activities is provided by molecular conformational characteristics. Early illness diagnosis will be aided by the accurate detection and efficient revelation of molecular conformational characteristics and their changes. The microscopic organization, structure, orientation, chirality, and other structural details of living things or materials can be obtained by using polarization imaging. The analysis of the imaging depth and polarization data is challenging, despite its widespread usage in the fields of material detection and biological imaging. Photoacoustic imaging preserves both the great contrast of optical imaging and the deep penetration of ultrasonic imaging by using light as an excitation source and ultrasound as the carrier for information transmission. While keeping the benefits of non-invasiveness, it is capable of high-resolution imaging, deep penetration, and functional imaging. A polarized photoacoustic imaging technology has recently been developed to complement polarization optical imaging and allow the collection of three-dimensional polarization data from deeper layers of the medium. This provides a straightforward and efficient method of measuring the polarimetry of tissues, suggesting substantial promise for both biological imaging and substance detection. The evolution of polarized photoacoustic imaging technology is outlined in this paper. First, the technical underpinnings of polarized photoacoustic imaging are described. Then, from the two applications of biological tissue imaging and nanomaterial detection, the related research progress of polarized photoacoustic microscopic imaging, polarized photoacoustic computational tomography, and polarized photoacoustic nanoparticles' molecular imaging is presented. We briefly explain the depolarization that results from particle size, density, and organization as polarized light travels through tissue. In an anisotropic medium, the change in the mid-incident polarization state of such a sample is caused by tissue birefringence and scattering because of the inherent birefringence effect of molecules, whereas in the isotropic medium, depolarization is largely determined by the density and size of the scatter. The potential applications of polarized photoacoustic imaging are then discussed.