UPAM‐KAN: A Method for Highly Undersampled Photoacoustic Microscopy Image Reconstruction

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.

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

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.

Retinal safety evaluation of photoacoustic microscopy

OBJECTIVERoughly 6.5 million diabetic patients in the United States suffer from chronic wounds that exhibit impaired healing response1. These wounds often do not respond to standard treatment due to insufficient oxygenation in the wound bed2. Given the heterogeneity of oxygen levels in wounds and the importance of oxygen in tissue repair, it is compelling to develop a reliable way to non‐invasively monitor oxygen levels within chronic wounds over time. Previously, we demonstrated the ability of topically‐delivered BF2nbm(I)PLA boron dye nanoparticles (BNPs) that phosphoresce differentially at varying oxygen levels, to provide a spatial map of oxygen levels throughout the wound bed of a murine full‐thickness cutaneous wound3. The objective of this work is to validate the ability of BNP imaging to detect impaired tissue oxygenation during wound healing in diabetic individuals compared to healthy individuals.METHODSWe have developed a novel model of wound healing in the murine ear which allows measurements of oxygen levels in both tissue and blood vessels using BNP imaging and photoacoustic microscopy (PAM), respectively. PAM non‐invasively measures structural and hemodynamic changes, including vessel diameter, hemoglobin concentration, blood flow speed, and hemoglobin oxygen saturation levels in individual blood vessels down to the capillary level during microvascular remodeling within wound beds4. In this ear wound model, the epidermis and a portion of the dermis, including the center feeder arteriole and its main branches, are excised from the center of the ear lobe. The wound is covered with Tegaderm bandage to prevent drying, and the mouse is fitted with an Elizabethan collar to prevent it from removing the bandage. At regular intervals until the wound heals, BNPs are topically applied to the wound and excited by UV light for two minutes during which the fluorescence (F) and phosphorescence (P) emission signals are recorded using a camera. A custom Matlab program calculates the ratio of F:P emission intensities at each pixel in the wound bed image, which provides a relative mapping of oxygen levels throughout the wound. Following BNP imaging, PAM imaging is performed in order to calculate the percent sO2 in each blood vessel in/around the wound bed. BNP and PAM measurements are compared to one another for wounds in both healthy and diabetic mice.RESULTSDiabetic db/db mice exhibit muted wound oxygenation increases over time compared to non‐diabetic control mice. BNP fluorescence:phosphorescence emission ratios from wound beds emulate sO2 trends generated via PAM, confirming the ability of BNPs to assess tissue oxygenation over time in both diabetic and healthy wounds.CONCLUSIONSBNPs can be used to monitor tissue oxygenation over time in diabetic and healthy wounds. Given their low cost and potential for clinical monitoring of these wounds in humans, BNPs warrant further study for non‐invasively monitoring wound healing over time.Support or Funding InformationUVA School of Medicine Pinn Scholars ProgramThis abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

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.

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.

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.

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.

This study aims to investigate the feasibility of using photoacoustic microscopy for the diagnosis of prostatitis. We induced inflammation in rats to establish a model of prostatitis using Freund’s Complete Adjuvant (FCA). Prostate tissues from both the model and control groups were extracted and processed into histological sections. We explored the photoacoustic microscopy imaging results of unstained sections, consistent with the histological detection results using HE staining. Inflammation was found to enhance the photoacoustic signals. Subsequently, we conducted photoacoustic microscopy imaging on all samples, and the detection results were nearly consistent with the diagnoses made by medical professionals. Finally, we quantified the collected photoacoustic signals to classify the severity of prostatitis.

In this study, the nondestructive testing (NDT) of tilted subsurface defects with a concave cross section was performed using a photoacoustic microscope (PAM). The tilted subsurface defects were formed in a metal plane specimen by mechanical processing. The obtained signal distribution was affected by the tilt angle of the subsurface defects, and the relationship between the gradient ratio and the tilt angle value exhibited a good correlation.

Freehand scanning photoacoustic microscopy with simultaneous localization and mapping

The advances in biomedical imaging equipment have produced a massive amount of medical images that are generated by the different modalities. Consequently, a huge volume of data has been produced and caused a complex and time-consuming retrieving process of the relevant cases. To resolve this issue, the Content-Based Biomedical Image Retrieval (CBMIR) system is applied to retrieve the related images from the earlier patients’ databases. However, the previous handcrafted features methods that applied the CBMIR model have shown poor performance in many multimodal databases. In this paper, we focus on designing CBMIR technique using Deep Learning (DL) models. We present a new Multimodal Biomedical Image Retrieval and Classification (M-BMIRC) technique for retrieving and classifying the biomedical images from huge databases. The proposed M-BMIRC model involves three dissimilar processes as following: feature extraction, similarity measurement, and classification. It uses an ensemble of handcrafted features from Zernike Moments (ZM) and deep features from Deep Convolutional Neural Networks (DCNN) for feature extraction process. Additionally, the Hausdorff Distance based similarity measure is employed to identify the resemblance between the queried image and the images that exist in the database. Moreover, the classification process gets executed on the retrieval images using the Probabilistic Neural Network (PNN) model, which allocates the class labels of the tested images. Finally, the experimental studies are conducted using two benchmark medical datasets and the results ensure the superior performance of the proposed model in terms of different measures include Average Precision Rate (APR), Average Recall Rate (ARR), F-score, accuracy, and Computation Time (CT).

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.

Acoustic-resolution photoacoustic micro-scopy (AR-PAM) is an emerging biomedical imaging modality that combines superior optical sensitivity and fine ultrasonic resolution in an optical quasi-diffusive regime (~1-3 mm in tissues). AR-PAM has been explored for anatomical, functional, and molecular information in biological tissues. Heretofore, AR-PAM systems have suffered from a limited field-of-view (FOV) and/or slow imaging speed, which have precluded them from routine preclinical and clinical applications. Here, we demonstrate an advanced AR-PAM system that overcomes both limitations of previous AR-PAM systems. The new AR-PAM system demonstrates a super wide-field scanning that utilized a 1-axis water-proofing microelectromechanical systems (MEMS) scanner integrated with two linear stepper motor stages. We achieved an extended FOV of 36 ×80 mm2 by mosaicking multiple volumetric images of 36 ×2.5 mm2 with a total acquisition time of 224 seconds. For one volumetric data (i.e., 36 ×2.5 mm2), the B-scan imaging speed over the short axis (i.e., 2.5 mm) was 83 Hz in humans. The 3D volumetric image was also provided by using MEMS mirror scanning along the X-axis and stepper-motor scanning along the Y-axis. The super-wide FOV mosaic image was realized by registering and merging all individual volumetric images. Finally, we obtained multi-plane whole-body in-vivo PA images of small animals, illustrating distinct multi-layered structures including microvascular networks and internal organs. Importantly, we also visualized microvascular networks in human fingers, palm, and forearm successfully. This advanced MEMS-AR-PAM system could potentially enable hitherto not possible wide preclinical and clinical applications.