Motion Correction in Optical Resolution Photoacoustic Microscopy

Quantitative volumetric photoacoustic assessment of vasoconstriction by topical corticosteroid application in mice skin

Topical corticosteroids manage inflammatory skin conditions via their action on the immune system. An effect of application of corticosteroids to the skin is skin blanching caused by peripheral vasoconstriction. This has been used to characterize, in some cases relative potency and also as a way to compare skin penetration. Chromameters have been used to assess skin blanching-the outcome of vasoconstriction caused by topical corticosteroids-but do not directly measure vasoconstriction. Here, we demonstrate quantitative volumetric photoacoustic microscopy (PAM) as a tool for directly assessing the vasoconstriction followed by topical corticosteroid application, noninvasively visualizing skin vasculature without any exogeneous contrast agent. We photoacoustically differentiated the vasoconstrictive ability of four topical corticosteroids in small animals through multiparametric analyses, offering detailed 3D insights into vasoconstrictive mechanisms across different skin depths. Our findings highlight the potential of PAM as a noninvasive tool for measurement of comparative vasoconstriction with potential for clinical, pharmaceutical, and bioequivalence applications.

Review on Photoacoustic Monitoring after Drug Delivery: From Label-Free Biomarkers to Pharmacokinetics Agents

Photoacoustic imaging (PAI) is an emerging noninvasive and label-free method for capturing the vasculature, hemodynamics, and physiological responses following drug delivery. PAI combines the advantages of optical and acoustic imaging to provide high-resolution images with multiparametric information. In recent decades, PAI's abilities have been used to determine reactivity after the administration of various drugs. This study investigates photoacoustic imaging as a label-free method of monitoring drug delivery responses by observing changes in the vascular system and oxygen saturation levels across various biological tissues. In addition, we discuss photoacoustic studies that monitor the biodistribution and pharmacokinetics of exogenous contrast agents, offering contrast-enhanced imaging of diseased regions. Finally, we demonstrate the crucial role of photoacoustic imaging in understanding drug delivery mechanisms and treatment processes.

Towards in vivo photoacoustic human imaging: Shining a new light on clinical diagnostics

Multiscale visualization of human anatomical structures is revolutionizing clinical diagnosis and treatment. As one of the most promising clinical diagnostic techniques, photoacoustic imaging (PAI), or optoacoustic imaging, bridges the spatial-resolution gap between pure optical and ultrasonic imaging techniques, by the modes of optical illumination and acoustic detection. PAI can non-invasively capture multiple optical contrasts from the endogenous agents such as oxygenated/deoxygenated hemoglobin, lipid and melanin or a variety of exogenous specific biomarkers to reveal anatomy, function, and molecular for biological tissues in vivo, showing significant potential in clinical diagnostics. In 2001, the worldwide first clinical prototype of the photoacoustic system was used to screen breast cancer in vivo, which opened the prelude to photoacoustic clinical diagnostics. Over the past two decades, PAI has achieved monumental discoveries and applications in human imaging. Progress towards preclinical/clinical applications includes breast, skin, lymphatics, bowel, thyroid, ovarian, prostate, and brain imaging, etc., and there is no doubt that PAI is opening new avenues to realize early diagnosis and precise treatment of human diseases. In this review, the breakthrough researches and key applications of photoacoustic human imaging in vivo are emphatically summarized, which demonstrates the technical superiorities and emerging applications of photoacoustic human imaging in clinical diagnostics, providing clinical translational orientations for the photoacoustic community and clinicians. The perspectives on potential improvements of photoacoustic human imaging are finally highlighted.

Unsupervised deep learning enables real-time image registration of fast-scanning optical-resolution photoacoustic microscopy

A fast scanner of optical-resolution photoacoustic microscopy is inherently vulnerable to perturbation, leading to severe image distortion and significant misalignment among multiple 2D or 3D images. Restoration and registration of these images is critical for accurately quantifying dynamic information in long-term imaging. However, traditional registration algorithms face a great challenge in computational throughput. Here, we develop an unsupervised deep learning based registration network to achieve real-time image restoration and registration. This method can correct artifacts from B-scan distortion and remove misalignment among adjacent and repetitive images in real time. Compared with conventional intensity based registration algorithms, the throughput of the developed algorithm is improved by 50 times. After training, the new deep learning method performs better than conventional feature based image registration algorithms. The results show that the proposed method can accurately restore and register the images of fast-scanning photoacoustic microscopy in real time, offering a powerful tool to extract dynamic vascular structural and functional information.

Correction of high-rate motion for photoacoustic microscopy by orthogonal cross-correlation

Photoacoustic imaging is a promising technology for in vivo imaging. However, its imaging performance can be hampered by motion artifacts, especially when dealing with high-rate motion. In this paper, we propose an orthogonal motion correction method that utilizes cross-correlation along orthogonal scan directions to extract accurate motion displacements from the photoacoustic data. The extracted displacements are then applied to remove artifacts and compensate for motion-induced distortions. Phantom experiments demonstrate that the proposed method can extract the motion information and the structural similarity index measurement after correction is increased by 26.5% and 11.2% compared to no correction and the previous correction method. Then the effectiveness of our method is evaluated in vivo imaging of a mouse brain. Our method shows a stable and effective performance under high-rate motion. The high accuracy of the motion correction method makes it valuable in improving the accuracy of photoacoustic imaging.

Quality control in clinical raster-scan optoacoustic mesoscopy

Optoacoustic (photoacoustic) mesoscopy bridges the gap between optoacoustic microscopy and macroscopy and enables high-resolution visualization deeper than optical microscopy. Nevertheless, as images may be affected by motion and noise, it is critical to develop methodologies that offer standardization and quality control to ensure that high-quality datasets are reproducibly obtained from patient scans. Such development is particularly important for ensuring reliability in applying machine learning methods or for reliably measuring disease biomarkers. We propose herein a quality control scheme to assess the quality of data collected. A reference scan of a suture phantom is performed to characterize the system noise level before each raster-scan optoacoustic mesoscopy (RSOM) measurement. Using the recorded RSOM data, we develop a method that estimates the amount of motion in the raw data. These motion metrics are employed to classify the quality of raw data collected and derive a quality assessment index (QASIN) for each raw measurement. Using simulations, we propose a selection criterion of images with sufficient QASIN, leading to the compilation of RSOM datasets with consistent quality. Using 160 RSOM measurements from healthy volunteers, we show that RSOM images that were selected using QASIN were of higher quality and fidelity compared to non-selected images. We discuss how this quality control scheme can enable the standardization of RSOM images for clinical and biomedical applications.

Advances in photoacoustic imaging aided by nano contrast agents: special focus on role of lymphatic system imaging for cancer theranostics

Photoacoustic imaging (PAI) is a successful clinical imaging platform for management of cancer and other health conditions that has seen significant progress in the past decade. However, clinical translation of PAI based methods are still under scrutiny as the imaging quality and clinical information derived from PA images are not on par with other imaging methods. Hence, to improve PAI, exogenous contrast agents, in the form of nanomaterials, are being used to achieve better image with less side effects, lower accumulation, and improved target specificity. Nanomedicine has become inevitable in cancer management, as it contributes at every stage from diagnosis to therapy, surgery, and even in the postoperative care and surveillance for recurrence. Nanocontrast agents for PAI have been developed and are being explored for early and improved cancer diagnosis. The systemic stability and target specificity of the nanomaterials to render its theranostic property depends on various influencing factors such as the administration route and physico-chemical responsiveness. The recent focus in PAI is on targeting the lymphatic system and nodes for cancer diagnosis, as they play a vital role in cancer progression and metastasis. This review aims to discuss the clinical advancements of PAI using nanoparticles as exogenous contrast agents for cancer theranostics with emphasis on PAI of lymphatic system for diagnosis, cancer progression, metastasis, PAI guided tumor resection, and finally PAI guided drug delivery.

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.

Miniature Probe for Optomechanical Focus-Adjustable Optical-Resolution Photoacoustic Endoscopy

Photoacoustic microscopy (PAM) is a promising imaging modality because it is able to reveal optical absorption contrast in high resolution on the order of a micrometer. It can be applied in an endoscopic approach by implementing PAM into a miniature probe, termed photoacoustic endoscopy (PAE). Here we develop a miniature focus-adjustable PAE (FA-PAE) probe characterized by both high resolution (in micrometers) and large depth of focus (DOF) via a novel optomechanical design for focus adjustment. To realize high resolution and large DOF in a miniature probe, a 2-mm plano-convex lens is specially adopted, and the mechanical translation of a single-mode fiber is meticulously designed to allow the use of multi-focus image fusion (MIF) for extended DOF. Compared with existing PAE probes, our FA-PAE probe achieves high resolution of [Formula: see text] within unprecedentedly large DOF of 3.2 mm, more than 27 times the DOF of the probe without performing focus adjustment for MIF. The superior performance is first demonstrated by imaging both phantoms and animals including mice and zebrafish in vivo by linear scanning. Further, in vivo endoscopic imaging of a rat's rectum by rotary scanning of the probe is conducted to showcase the capability of adjustable focus. Our work opens new perspectives for PAE biomedical applications.

Automated Laser-Fiber Coupling Module for Optical-Resolution Photoacoustic Microscopy

Photoacoustic imaging has emerged as a promising biomedical imaging technique that enables visualization of the optical absorption characteristics of biological tissues in vivo. Among the different photoacoustic imaging system configurations, optical-resolution photoacoustic microscopy stands out by providing high spatial resolution using a tightly focused laser beam, which is typically transmitted through optical fibers. Achieving high-quality images depends significantly on optical fluence, which is directly proportional to the signal-to-noise ratio. Hence, optimizing the laser-fiber coupling is critical. Conventional coupling systems require manual adjustment of the optical path to direct the laser beam into the fiber, which is a repetitive and time-consuming process. In this study, we propose an automated laser-fiber coupling module that optimizes laser delivery and minimizes the need for manual intervention. By incorporating a motor-mounted mirror holder and proportional derivative control, we successfully achieved efficient and robust laser delivery. The performance of the proposed system was evaluated using a leaf-skeleton phantom in vitro and a human finger in vivo, resulting in high-quality photoacoustic images. This innovation has the potential to significantly enhance the quality and efficiency of optical-resolution photoacoustic microscopy.

In vivo characterization and analysis of glioblastoma at different stages using multiscale photoacoustic molecular imaging

Simultaneous spatio-temporal description of tumor microvasculature, blood-brain barrier, and immune activity is pivotal to understanding the evolution mechanisms of highly aggressive glioblastoma, one of the most common primary brain tumors in adults. However, the existing intravital imaging modalities are still difficult to achieve it in one step. Here, we present a dual-scale multi-wavelength photoacoustic imaging approach cooperative with/without unique optical dyes to overcome this dilemma. Label-free photoacoustic imaging depicted the multiple heterogeneous features of neovascularization in tumor progression. In combination with classic Evans blue assay, the microelectromechanical system based photoacoustic microscopy enabled dynamic quantification of BBB dysfunction. Concurrently, using self-fabricated targeted protein probe (αCD11b-HSA@A1094) for tumor-associated myeloid cells, unparalleled imaging contrast of cells infiltration associated with tumor progression was visualized by differential photoacoustic imaging in the second near-infrared window at dual scale. Our photoacoustic imaging approach has great potential for tumor-immune microenvironment visualization to systematically reveal the tumor infiltration, heterogeneity, and metastasis in intracranial tumors.

Photoacoustic imaging for microcirculation

Microcirculation facilitates the blood-tissue exchange of nutrients and regulates blood perfusion. It is, therefore, essential in maintaining tissue health. Aberrations in microcirculation are potentially indicative of underlying cardiovascular and metabolic pathologies. Thus, quantitative information about it is of great clinical relevance. Photoacoustic imaging (PAI) is a capable technique that relies on the generation of imaging contrast via the absorption of light and can image at micron-scale resolution. PAI is especially desirable to map microvasculature as hemoglobin strongly absorbs light and can generate a photoacoustic signal. This paper reviews the current state of the art for imaging microvascular networks using photoacoustic imaging. We further describe how quantitative information about blood dynamics such as the total hemoglobin concentration, oxygen saturation, and blood flow rate is obtained using PAI. We also discuss its importance in understanding key pathophysiological processes in neurovascular, cardiovascular, ophthalmic, and cancer research fields. We then discuss the current challenges and limitations of PAI and the approaches that can help overcome these limitations. Finally, we provide the reader with an overview of future trends in the field of PAI for imaging microcirculation.

Visualizing tumor angiogenesis and boundary with polygon-scanning multiscale photoacoustic microscopy

Recently, we developed an integrated optical-resolution (OR) and acoustic-resolution (AR) PAM, which has multiscale imaging capability using different resolutions. However, limited by the scanning method, a tradeoff exists between the imaging speed and field of view, which impedes its wider applications. Here, we present an improved multiscale PAM which achieves high-speed wide-field imaging based on a homemade polygon scanner. Encoder trigger mode was proposed to avoid jittering of the polygon scanner during imaging. Distortions caused by polygon scanning were analyzed theoretically and compared with traditional types of distortions in optical-scanning PAM. Then a depth correction method was proposed and verified to compensate for the distortions. System characterization of OR-PAM and AR-PAM was performed prior to in vivo imaging. Blood reperfusion of an in vivo mouse ear was imaged continuously to demonstrate the feasibility of the multiscale PAM for high-speed imaging. Results showed that the maximum B-scan rate could be 14.65 Hz in a fixed range of 10 mm. Compared with our previous multiscale system, the imaging speed of the improved system was increased by a factor of 12.35. In vivo imaging of a subcutaneously inoculated B-16 melanoma of a mouse was performed. Results showed that the blood vasculature around the melanoma could be resolved and the melanoma could be visualized at a depth up to 1.6 mm using the multiscale PAM.

Self-Fluence-Compensated Functional Photoacoustic Microscopy

Optical-resolution photoacoustic microscopy (OR-PAM) can image blood oxygen saturation (sO₂) in vivo with high resolution and excellent sensitivity and offers a great tool for neurovascular study and early cancer diagnosis. OR-PAM ignores the wavelength-dependent optical attenuation in superficial tissue, which cause errors in sO₂ imaging. Monte Carlo simulation shows that variations in imaging depth, vessel diameter, and focal position can cause up to  ∼ 60 % decrease in sO₂ imaging. Here, we develop a self-fluence-compensated OR-PAM to compensate for the wavelength-dependent fluence attenuation. We propose a linearized model to estimate the fluence attenuations and use three optical wavelengths to compensate for them in sO₂ calculation. We validate the model in both numerical and physical phantoms and show that the compensation method can effectively reduce the sO₂ errors. In functional brain imaging, we demonstrate that the compensation method can effectively improve sO₂ accuracy, especially in small vessels. Compared with uncompensated ones, the sO₂ values are improved by 10~30% in the brain. We monitor ischemic-stroke-induced brain injury which demonstrates great potential for the pre-clinical study of vascular diseases.

Suppression of motion artifacts in intravascular photoacoustic image sequences

Intravascular photoacoustic (IVPA) imaging is an image-based imaging modality for the assessment of atherosclerotic plaques. Successful application of IVPA for in vivo coronary arterial imaging requires one overcomes the challenge of motion artifacts associated with the cardiac cycle. We propose a method for correcting artifacts owing to cardiac motion, which are observed in sequential IVPA images acquired by the continuous pullback of the imaging catheter. This method groups raw photoacoustic signals into subsets corresponding to similar phases in the cardiac cycles. Thereafter, the sequential images are reconstructed, by representing the initial pressure distribution on the vascular cross-sections based on the clustered frames of signals by time reversal. Results of simulation data demonstrate the efficacy of this method in suppressing motion artifacts. Qualitative and quantitative evaluations of the method indicate an enhancement of the image quality. Comparison results reveal that this method is computationally efficient in motion correction compared with the image-based gating.

High-resolution functional photoacoustic monitoring of vascular dynamics in human fingers

Functional imaging of microvascular dynamics in extremities delivers intuitive information for early detection, diagnosis, and prognosis of vascular diseases. High-resolution and high-speed photoacoustic microscopy (PAM) visualizes and measures multiparametric information of microvessel networks in vivo such as morphology, flow, oxygen saturation, and metabolic rate. Here, we demonstrate high-resolution photoacoustic monitoring of vascular dynamics in human fingers. We photoacoustically monitored the position displacement of blood vessels associated with arterial pulsation in human fingers. Then, during and after arterial occlusion, we photoacoustically quantified oxygen consumption and blood perfusion in the fingertips. The results demonstrate that high-resolution functional PAM could be a vital tool in peripheral vascular examination for measuring heart rate, oxygen consumption, and/or blood perfusion.

Photoacoustic imaging of in vivo hemodynamic responses to sodium nitroprusside

The in vivo hemodynamic impact of sodium nitroprusside (SNP), a widely used antihypertensive agent, has not been well studied. Here, we applied functional optical-resolution photoacoustic microscopy (OR-PAM) to study the hemodynamic responses to SNP in mice in vivo. As expected, after the application of SNP, the systemic blood pressure (BP) was reduced by 53%. The OR-PAM results show that SNP induced an arterial vasodilation of 24% and 23% in the brain and skin, respectively. A weaker venous vasodilation of 9% and 5% was also observed in the brain and skin, respectively. The results show two different types of blood oxygenation response. In mice with decreased blood oxygenation, the arterial and venous oxygenation was respectively reduced by 6% and 13% in the brain, as well as by 7% and 18% in the skin. In mice with increased blood oxygenation, arterial and venous oxygenation was raised by 4% and 22% in the brain, as well as by 1% and 9% in the skin. We observed venous change clearly lagged the arterial change in the skin, but not in the brain. Our results collectively show a correlation among SNP induced changes in systemic BP, vessel size and blood oxygenation.

Optoacoustic imaging in endocrinology and metabolism

Imaging is an essential tool in research, diagnostics and the management of endocrine disorders. Ultrasonography, nuclear medicine techniques, MRI, CT and optical methods are already used for applications in endocrinology. Optoacoustic imaging, also termed photoacoustic imaging, is emerging as a method for visualizing endocrine physiology and disease at different scales of detail: microscopic, mesoscopic and macroscopic. Optoacoustic contrast arises from endogenous light absorbers, such as oxygenated and deoxygenated haemoglobin, lipids and water, or exogenous contrast agents, and reveals tissue vasculature, perfusion, oxygenation, metabolic activity and inflammation. The development of high-performance optoacoustic scanners for use in humans has given rise to a variety of clinical investigations, which complement the use of the technology in preclinical research. Here, we review key progress with optoacoustic imaging technology as it relates to applications in endocrinology; for example, to visualize thyroid morphology and function, and the microvasculature in diabetes mellitus or adipose tissue metabolism, with particular focus on multispectral optoacoustic tomography and raster-scan optoacoustic mesoscopy. We explain the merits of optoacoustic microscopy and focus on mid-infrared optoacoustic microscopy, which enables label-free imaging of metabolites in cells and tissues. We showcase current optoacoustic applications within endocrinology and discuss the potential of these technologies to advance research and clinical practice.

Regional motion correction for in vivo photoacoustic imaging in humans using interleaved ultrasound images

In translation from preclinical to clinical studies using photoacoustic imaging, motion artifacts represent a major issue. In this study the feasibility of an in-house algorithm, referred to as intensity phase tracking (IPT), for regional motion correction of in vivo human photoacoustic (PA) images was demonstrated. The algorithm converts intensity to phase-information and performs 2D phase-tracking on interleaved ultrasound images. The radial artery in eight healthy volunteers was imaged using an ultra-high frequency photoacoustic system. PA images were motion corrected and evaluated based on PA image similarities. Both controlled measurements using a computerized stepping motor and free-hand measurements were evaluated. The results of the controlled measurements show that the tracking corresponded to 97 ± 6% of the actual movement. Overall, the mean square error between PA images decreased by 52 ± 15% and by 43 ± 19% when correcting for controlled- and free-hand induced motions, respectively. The results show that the proposed algorithm could be used for motion correction in photoacoustic imaging in humans.

Deep learning in photoacoustic imaging: a review

SIGNIFICANCE

Photoacoustic (PA) imaging can provide structural, functional, and molecular information for preclinical and clinical studies. For PA imaging (PAI), non-ideal signal detection deteriorates image quality, and quantitative PAI (QPAI) remains challenging due to the unknown light fluence spectra in deep tissue. In recent years, deep learning (DL) has shown outstanding performance when implemented in PAI, with applications in image reconstruction, quantification, and understanding.

AIM

We provide (i) a comprehensive overview of the DL techniques that have been applied in PAI, (ii) references for designing DL models for various PAI tasks, and (iii) a summary of the future challenges and opportunities.

APPROACH

Papers published before November 2020 in the area of applying DL in PAI were reviewed. We categorized them into three types: image understanding, reconstruction of the initial pressure distribution, and QPAI.

RESULTS

When applied in PAI, DL can effectively process images, improve reconstruction quality, fuse information, and assist quantitative analysis.

CONCLUSION

DL has become a powerful tool in PAI. With the development of DL theory and technology, it will continue to boost the performance and facilitate the clinical translation of PAI.

Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

Data extraction and visualization of 3D medical images of ocular blood vessels are performed by geometric transformation algorithm, which first performs random resonance response in a global sense to achieve detection of high-contrast coarse blood vessels and then redefines the input signal as a local image shielding the global detection result to achieve enhanced detection of low-contrast microfine vessels and complete multilevel random resonance segmentation detection. Finally, a random resonance detection method for fundus vessels based on scale decomposition is proposed, in which the images are scale decomposed, the high-frequency signals containing detailed information are randomly resonantly enhanced to achieve microfine vessel segmentation detection, and the final vessel segmentation detection results are obtained after fusing the low-frequency image signals. The optimal stochastic resonance response of the nonlinear model of neurons in the global sense is obtained to detect the high-grade intensity signal; then, the input signal is defined as a local image with high-contrast blood vessels removed, and the parameters are optimized before the detection of the low-grade intensity signal. Finally, the multilevel random resonance response is fused to obtain the segmentation results of the fundus retinal vessels. The sensitivity of the multilevel segmentation method proposed in this paper is significantly improved compared with the global random resonance results, indicating that the method proposed in this paper has obvious advantages in the segmentation of vessels with low-intensity levels. The image library was tested, and the experimental results showed that the new method has a better segmentation effect on low-contrast microscopic blood vessels. The new method not only makes full use of the noise for weak signal detection and segmentation but also provides a new idea of how to achieve multilevel segmentation and recognition of medical images.

Deep Learning Enables Superior Photoacoustic Imaging at Ultralow Laser Dosages

Optical-resolution photoacoustic microscopy (OR-PAM) is an excellent modality for in vivo biomedical imaging as it noninvasively provides high-resolution morphologic and functional information without the need for exogenous contrast agents. However, the high excitation laser dosage, limited imaging speed, and imperfect image quality still hinder the use of OR-PAM in clinical applications. The laser dosage, imaging speed, and image quality are mutually restrained by each other, and thus far, no methods have been proposed to resolve this challenge. Here, a deep learning method called the multitask residual dense network is proposed to overcome this challenge. This method utilizes an innovative strategy of integrating multisupervised learning, dual-channel sample collection, and a reasonable weight distribution. The proposed deep learning method is combined with an application-targeted modified OR-PAM system. Superior images under ultralow laser dosage (32-fold reduced dosage) are obtained for the first time in this study. Using this new technique, a high-quality, high-speed OR-PAM system that meets clinical requirements is now conceivable.

Hybrid deep learning network for vascular segmentation in photoacoustic imaging

Photoacoustic (PA) technology has been used extensively on vessel imaging due to its capability of identifying molecular specificities and achieving high optical-diffraction-limited lateral resolution down to the cellular level. Vessel images carry essential medical information that provides guidelines for a professional diagnosis. Modern image processing techniques provide a decent contribution to vessel segmentation. However, these methods suffer from under or over-segmentation. Thus, we demonstrate both the results of adopting a fully convolutional network and U-net, and propose a hybrid network consisting of both applied on PA vessel images. Comparison results indicate that the hybrid network can significantly increase the segmentation accuracy and robustness.

High-speed wide-field multi-parametric photoacoustic microscopy

Capable of imaging blood perfusion, oxygenation, and flow simultaneously at the microscopic level, multi-parametric photoacoustic microscopy (PAM) has quickly emerged as a powerful tool for studying hemodynamic and metabolic changes due to physiological stimulations or pathological processes. However, the low scanning speed poised by the correlation-based blood flow measurement impedes its application in studying rapid microvascular responses. To address this challenge, we have developed a new, to the best of our knowledge, multi-parametric PAM system. By extending the optical scanning range with a cylindrically focused ultrasonic transducer (focal zone, 76µm×4.5mm) for simultaneous acquisition of 500 B-scans, the new system is 112 times faster than our previous multi-parametric system that uses a spherically focused transducer (focal diameter, 40 µm) and enables high-resolution imaging of blood perfusion, oxygenation, and flow over an area of 4.5×1mm2 at a frame rate of 1 Hz. We have demonstrated the feasibility of this system in the living mouse ear. Further development of this system into reflection mode will enable real-time cortex-wide imaging of hemodynamics and metabolism in the mouse brain.

Rapid functional optoacoustic micro-angiography in a burst mode

Optoacoustic microscopy (OAM) can image intrinsic optical absorption contrast at depths of several millimeters where state-of-the-art optical microscopy techniques fail due to intense light scattering in living tissues. Yet, wide adoption of OAM in biology and medicine is hindered by slow image acquisition speed, small field of view (FOV), and/or lack of spectral differentiation capacity of common system implementations. We report on a rapid acquisition functional optoacoustic micro-angiography approach that employs a burst-mode laser triggering scheme to simultaneously acquire multi-wavelength 3D images over an extended FOV covering ${50}\;{\rm mm} \times {50}\;{\rm mm}$50mm×50mm in a single mechanical overfly scan, attaining 28 µm and 14 µm resolution in lateral and axial dimensions, respectively. Owing to an ultrawideband low-noise design featuring a spherically focused polyvinylidene difluoride transducer, we demonstrate imaging of human skin and underlying vasculature at up to 3.8 mm depth when using per-pulse laser energies of only 25 µJ without employing signal averaging. Overall, the developed system greatly enhances performance and usability of OAM for dermatologic and micro-angiographic studies.

Versatile Single-Element Ultrasound Imaging Platform using a Water-Proofed MEMS Scanner for Animals and Humans

Single-element transducer based ultrasound (US) imaging offers a compact and affordable solution for high-frequency preclinical and clinical imaging because of its low cost, low complexity, and high spatial resolution compared to array-based US imaging. To achieve B-mode imaging, conventional approaches adapt mechanical linear or sector scanning methods. However, due to its low scanning speed, mechanical linear scanning cannot achieve acceptable temporal resolution for real-time imaging, and the sector scanning method requires specialized low-load transducers that are small and lightweight. Here, we present a novel single-element US imaging system based on an acoustic mirror scanning method. Instead of physically moving the US transducer, the acoustic path is quickly steered by a water-proofed microelectromechanical (MEMS) scanner, achieving real-time imaging. Taking advantage of the low-cost and compact MEMS scanner, we implemented both a tabletop system for in vivo small animal imaging and a handheld system for in vivo human imaging. Notably, in combination with mechanical raster scanning, we could acquire the volumetric US images in live animals. This versatile US imaging system can be potentially used for various preclinical and clinical applications, including echocardiography, ophthalmic imaging, and ultrasound-guided catheterization.

A new deep learning method for image deblurring in optical microscopic systems

Deconvolution is the most commonly used image processing method in optical imaging systems to remove the blur caused by the point-spread function (PSF). While this method has been successful in deblurring, it suffers from several disadvantages, such as slow processing time due to multiple iterations required to deblur and suboptimal in cases where the experimental operator chosen to represent PSF is not optimal. In this paper, we present a deep-learning-based deblurring method that is fast and applicable to optical microscopic imaging systems. We tested the robustness of proposed deblurring method on the publicly available data, simulated data and experimental data (including 2D optical microscopic data and 3D photoacoustic microscopic data), which all showed much improved deblurred results compared to deconvolution. We compared our results against several existing deconvolution methods. Our results are better than conventional techniques and do not require multiple iterations or pre-determined experimental operator. Our method has several advantages including simple operation, short time to compute, good deblur results and wide application in all types of optical microscopic imaging systems. The deep learning approach opens up a new path for deblurring and can be applied in various biomedical imaging fields.

Deep-learning-based motion-correction algorithm in optical resolution photoacoustic microscopy

In this study, we propose a deep-learning-based method to correct motion artifacts in optical resolution photoacoustic microscopy (OR-PAM). The method is a convolutional neural network that establishes an end-to-end map from input raw data with motion artifacts to output corrected images. First, we performed simulation studies to evaluate the feasibility and effectiveness of the proposed method. Second, we employed this method to process images of rat brain vessels with multiple motion artifacts to evaluate its performance for in vivo applications. The results demonstrate that this method works well for both large blood vessels and capillary networks. In comparison with traditional methods, the proposed method in this study can be easily modified to satisfy different scenarios of motion corrections in OR-PAM by revising the training sets.