Feasibility of Prostatitis Evaluation Based on Photoacoustic Microscopy

Mosaic imaging is a computer vision process that is used for merging multiple overlapping imaging patches into a wide-field-of-view image. To achieve a wide-field-of-view photoacoustic microscopy (PAM) image, the limitations of the scan range of PAM require a merging process, such as marking the location of patches or merging overlapping areas between adjacent images. By using the mosaic imaging process, PAM shows a larger field view of targets and preserves the quality of the spatial resolution. As an essential process in mosaic imaging, various feature generation methods have been used to estimate pairs of image locations. In this study, various feature generation algorithms were applied and analyzed using a high-resolution mouse ear PAM image dataset to achieve and optimize a mosaic imaging process for wide-field PAM imaging. We compared the performance of traditional and deep learning feature generation algorithms by estimating the processing time, the number of matches, good matching ratio, and matching efficiency. The analytic results indicate the successful implementation of wide-field PAM images, realized by applying suitable methods to the mosaic PAM imaging process.

Choroidal neovascularization (CNV) is a hallmark of advanced age-related macular degeneration and is a leading cause of irreversible vision loss and blindness in the world. There is a lack of imaging modalities that can detect CNV early. Photoacoustic microscopy (PAM) combines acoustic and optical imaging to create high-resolution images that can delineate microvasculature non-invasively and permit for visualization of CNV. However, to distinguish CNV from native microvasculature, molecular contrast agents can be applied to increase the sensitivity of PAM imaging. In this study, functionalized indocyanine green (ICG) with RGD ligands (ICG-RGD) was synthesized and applied to in vivo imaging of CNV in the rabbit retina using a custom-built high-resolution PAM imaging system. RGD ligands selective binds to integrins present in neovascularization. The CNV model was created in three New Zealand White rabbits via subretinal injection of human vascular endothelial grow factor (VEGF-165). Three-dimensional in vivo PAM images of CNV were acquired before and after intravenous injection of 400 µL ICG-RGD at a concentration of 2.5 mg/mL over a period of 14 days. In addition, color fundus photography and fluorescence imaging were also acquired. This enables visualization of the newly developed CNV as well as monitoring the changes in CNV over time. With the administration of ICG-RGD, PAM signal amplitudes enhanced up to 15.7-fold at day 1 post-injection and persisted up to 5 days. The PAM results were also confirmed by fluorescence imaging. Biosafety studies show no systemic toxicity of the ICG-RGD. These studies demonstrate the potential of molecular PAM imaging for the characterization of microvasculature pathologies.

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.

Retinal safety evaluation of photoacoustic microscopy

Research on photoacoustic microscopy imaging based on photoacoustic transmission matrix with a digital micromirror device

To explore the possible mechanism of electroacupuncture (EA) underlying improvement of chronic pelvic pain syndrome (CPPS). Fifty SD rats were randomly divided into control, model, sham operation, EA and sham EA groups (n=10 rats in each group). The CPPS model was established by injecting complete Freund's adjuvant (CFA, 50 μL) into the ventral lobes of the prostate. EA (2 Hz/100 Hz) was applied to "Guanyuan"(CV4), "Zhongji"(CV3), "Sanyinjiao" (SP6) and "Huiyang"(BL35) once daily for 40 min, 5 days a week for 4 weeks, while rats in the sham EA group were treated with the same acupoints but without electrical stimulation. Mechanical pain threshold (MPT) and heat pain threshold (HPT) were measured before and after intervention. The body weight and prostate weight were measured and prostate index was calculated. Histopathological changes of prostate tissue were observed by HE staining. The levels of cycloxygenase-2 (COX-2), prostaglandin E2 (PGE2) and β-endorphin (β-EP) in prostate tissue were detected by ELISA. Compared with the control and sham operation groups, the MPT and HPT were significantly lower (P<0.01), and the prostate weight, prostate index, the contents of PGE2 and COX-2 were significantly increased (P<0.01), while the content of β-EP was significantly decreased (P<0.01) in the model group. Compared with the model group, the MPT and HPT were significantly increased (P<0.01) after 3 and 4 courses of treatment, and the prostate weight, prostate index, the contents of PGE2 and COX-2 were significantly decreased (P<0.01), while the content of β-EP was significantly increased (P<0.01) in the EA group, rather than in the sham EA group (P>0.05). EA can effectively relieve pain in CPPS rats, which may be related to its functions in down-regulating COX-2 and PGE2, and up-regulating β-EP.

Visualizing ocular vasculature is important in clinical ophthalmology because ocular circulation abnormalities are early signs of ocular diseases. Photoacoustic microscopy (PAM) images the ocular vasculature without using exogenous contrast agents, avoiding associated side effects. Moreover, 3D PAM images can be useful in understanding vessel-related eye disease. However, the complex structure of the multi-layered vessels still present challenges in evaluating ocular vasculature. In this study, we demonstrate a new method to evaluate blood circulation in the eye by combining in vivo PAM imaging and an ocular surface estimation method based on a machine learning algorithm: a random sample consensus algorithm. By using the developed estimation method, we were able to visualize the PA ocular vascular image intuitively and demonstrate layer-by-layer analysis of injured ocular vasculature. We believe that our method can provide more accurate evaluations of the eye circulation in ophthalmic applications.

&lt;p&gt;Over the last two and a half decades, photoacoustic (PA) imaging has become an important area of research in biomedical optics. Combining the high contrast of optical imaging with the high spatial resolution of ultrasound (US) imaging, PA imaging can simultaneously visualize anatomical structures while interrogating their functionality through multiwavelength optical spectroscopy. Alongside technological developments and imaging applications in optical and acoustic resolution PA imaging, a family of PA signal analysis techniques can extract additional information about the sample being imaged. This Tutorial focuses on techniques that rely on the analysis of PA signals in a manner similar to that in the complimentary field of quantitative ultrasound (QUS) imaging of soft tissues. In QUS, signal analysis techniques have been developed to analyze the US signals resulting from the scattering of many unresolved scatterers within the resolution volume of the imaging device. The implementation of these US techniques in PA can enable new applications in biomedicine beyond traditional anatomical PA imaging, further increasing the utilization and impact of this promising modality.&lt;/p&gt;

Photoacoustic imaging is a new imaging technology in recent years, which combines the advantages of high resolution and rich contrast of optical imaging with the advantages of high penetration depth of acoustic imaging. Photoacoustic imaging has been widely used in biomedical fields, such as brain imaging, tumor detection and so on. The signal-tonoise ratio (SNR) of image signals in photoacoustic imaging is generally low due to the limitation of laser pulse energy, electromagnetic interference in the external environment and system noise. In order to solve the problem of low SNR of photoacoustic images, we use feedforward denoising convolutional neural network to further process the obtained images, so as to obtain higher SNR images and improve image quality. We use Python language to manage the referenced Python external library through Anaconda, and build a feedforward noise-reducing convolutional neural network on Pycharm platform. We first processed and segmated a training set containing 400 images, and then used it for network training. Finally, we tested it with a series of cerebrovascular photoacoustic microscopy images. The results show that the peak signal-to-noise ratio (PSNR) of the image increases significantly before and after denoising. The experimental results verify that the feed-forward noise reduction convolutional neural network can effectively improve the quality of photoacoustic microscopic images, which provides a good foundation for the subsequent biomedical research.

To explore the effect of icariin on early steroid-induced osteonecrosis of the femoral head in rabbits. Fifty mature New Zealand rabbits (weighing, 2.5-3.0 kg) were randomly divided into control group ( n=10), model group ( n=20), and experimental group ( n=20). The rabbits of model and experimental groups were injected with lipopolysaccharide and methylprednisolone to establish the animal model of early steroid-induced osteonecrosis of the femoral head. The rabbits of experimental group were feeded with icariin solution once a day for 6 weeks since the first injection of methylprednisolone, whereas the rabbits of control and model groups were given normal saline at the same time points. The left femoral heads were removed after 6 weeks and gross morphological features were evaluated. Micro-CT scan was performed to analyze the trabecular microstructure with the following parameters: trabecular bone volume to total volume (BV/TV), trabecular number (Tb.N), trabecular thickness (Tb.Tn), and trabecular separation (Tb.Sp). The Micro-CT scan was also converted to three-dimensional reconstruction images for observation. HE staining was applied to observe the trabecular structure and morphological changes of osteocytes and marrow adipocytes. It was also used to determine whether the samples of femoral heads occurred osteonecrosis based on the criteria for pathological diagnosis, and calculate the rate of empty lacunae. Seven rabbits died during the study, and 9, 16, and 18 rabbits in the control, model, and experimental groups, respectively, enrolled the final analysis. Compared with control group, the femoral head collapse and trabecular breaks were more obvious, and the trabeculae were sparse with irregular arrangement in the model group according to the results of gross observation, Micro-CT scan, and three-dimensional reconstruction images. But in the experimental group, the surface of femoral head was slight shrinking without obvious collapse, and the degeneration of trabecular structure was mild. According to bone microstructures analysis, the Tb.N, Tb.Tn, and BV/TV of femoral head in model and experimental groups were lower than those in control group, while the Tb.Sp in the model and experimental groups were significantly higher. The Tb.N, Tb.Tn, and BV/TV of femoral head in experimental group were higher than those in model group, while the Tb.Sp in the experimental group was significantly lower. The differences between groups were all significant ( P<0.05). In the model group, HE staining showed that the number of osteocytes reduced, the number of empty lacunae increased, and the marrow adipocytes piled up in the space between femoral trabeculae, some even mashed together like a cyst. In the experimental group, the trabecular structure was still relatively complete compared with model group, no obvious apoptosis of osteocytes was observed, the size and number of adipocytes were basically normal. None of the animals in control group occurred osteonecrosis of the femoral head based on the criteria for pathological diagnosis, and the incidence of osteonecrosis were 81.3% (13/16) in the model group and 66.7% (12/18) in the experimental group, and the difference was not significant ( P=0.448). The rate of empty lacunae of osteonecrotic femoral heads in the model group was 33.1%±1.4%, which was higher than that in experimental group (18.9%±0.8%) and in control group (12.7%±1.5%), and the differences between groups were significant ( P<0.05). The icariin has a protective effect on the early steroid-induced osteonecrosis of the femoral head in rabbits, which can decrease osteocytes apoptosis, improve the bone microstructure, and delay such disease processes.

As a functional imaging technique, photoacoustic imaging has the ability to produce high contrast and high resolution image. However, photoacoustic microscopy shows the disadvantage of limited depth of field, due to the strong focusing process. Only some part of the whole image is clear while some other information might be lost or misleading. To achieve a large volumetric and high resolution photoacoustic, we propose a depth-of-field expansion motivated multi-focus photoacoustic microscopy image fusion algorithm which fuses the information from imaging of different focal positions into single one to obtain all-in-focus image. To achieve this goal, the volumes obtained at different focus positions are sliced into 2D and the 2D image fusion based on cross bilateral filter is performed for correspond slices. Finally, 3D reconstruction can be performed on these fused slices. Experiment results show that our method can produce a 3D fusion result which maintains useful photoacoustic signal information for further analysis and visualization.

Photoacoustic microscopy is an in vivo imaging technology based on the photoacoustic effect. It is widely used in various biomedical studies because it can provide high-resolution images while being label-free, safe, and harmless to biological tissue. Polygon-scanning is an effective scanning method in photoacoustic microscopy that can realize fast imaging of biological tissue with a large field of view. However, in polygon-scanning, fluctuations of the rotating motor speed and the geometric error of the rotating mirror cause image distortions, which seriously affect the photoacoustic-microscopy imaging quality. To improve the image quality of photoacoustic microscopy using polygon-scanning, an image correction method is proposed based on accurate ultrasound positioning. In this method, the photoacoustic and ultrasound imaging data of the sample are simultaneously obtained, and the angle information of each mirror used in the polygon-scanning is extracted from the ultrasonic data to correct the photoacoustic images. Experimental results show that the proposed method can significantly reduce image distortions in photoacoustic microscopy, with the image dislocation offset decreasing from 24.774 to 10.365 μm.

Photoacoustic imaging is a new biomedical imaging technology developed in recent years. It has the advantages of high resolution and sensitivity to the functional characteristics of biological tissues. However, whether for photoacoustic tomography or photoacoustic microscopy imaging, the imaging resolution depends on the frequency and bandwidth of the received ultrasonic signal. This leads to a large amount of data being collected under the Nyquist's law. So storage medium and DSP processor are under unprecedented pressure. The problem of large amount of data is usually solved by using compression. Therefore, the combination of compressed sensing theory and photoacoustic imaging can not only restore images with high quality, but also reduce the amount of data as much as possible. It saves storage space and the time of further data processing. This paper introduces signal sparsity and measurement matrix of compressed sensing theory briefly. The virtual photoacoustic imaging of blood vessels is carried out in the simulation environment constructed by the k-wave toolbox of MATLAB. The collected data are restored by using the gradient projection for sparse reconstruction. The results show that high quality photoacoustic imaging images can be reconstructed while a small amount of data is stored. The performance of the simulation platform is verified. And it is of great significance to solve the problem of high data volume of photoacoustic imaging.

Regenerative therapies such as stem cell therapies are an area of active investigation that will likely play an important role in the future to improve vision loss from retinal diseases including macular degeneration and retinitis pigmentosa. It is important to visualize these cells after administration to determine their fate and effect. In this study, an advanced non-invasive photoacoustic microscopy (PAM) and optical coherence tomography (OCT) imaging system was developed to monitor cells in vivo. To boost the sensitivity of PAM and OCT, novel ultrapure functionalized chain-like gold nanoparticle (CGNP) clusters were synthesized and cultured into a precursor human retinal pigment epithelial cell line with differentiated properties (ARPE-19) cells. The fabricated CGNP clusters have redshifted plasmonic peak absorption from 520nm to 650nm, resulting in reduced background signal from hemoglobin. The position of cells following subretinal injection into the rabbit retina having laser injury is selectively tracked longitudinally in vivo using integrated photoacoustic microscopy (PAM) and OCT over 3 months in 3 rabbits. PAM images obtained at two different optical wavelengths of 578 nm and 650 nm were overlaid on the same image plane and on the OCT image allowed to distinguish transplanted cells from the adjacent native choroidal vessels and track the migration of the cells over time. Quantification of PAM and OCT signals illustrated that the PAM signal increased by 30-fold, and OCT signal increased by 180 %. Histological analysis confirmed that ARPE-19 cells migrated to the injured sites and correlated with the location noted on the PAM/OCT imaging. This work provides a comprehensive imaging and nanoparticle system that could be used for labeling and tracking of cell-based regenerative therapies.

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.