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Although accurate detection of breast cancer still poses significant challenges, deep learning (DL) can support more accurate image interpretation. In this study, we develop a highly robust DL model based on combined B-mode ultrasound (B-mode) and strain elastography ultrasound (SE) images for classifying benign and malignant breast tumors. This study retrospectively included 85 patients, including 42 with benign lesions and 43 with malignancies, all confirmed by biopsy. Two deep neural network models, AlexNet and ResNet, were separately trained on combined 205 B-mode and 205 SE images (80% for training and 20% for validation) from 67 patients with benign and malignant lesions. These two models were then configured to work as an ensemble using both image-wise and layer-wise and tested on a dataset of 56 images from the remaining 18 patients. The ensemble model captures the diverse features present in the B-mode and SE images and also combines semantic features from AlexNet and ResNet models to classify the benign from the malignant tumors. The experimental results demonstrate that the accuracy of the proposed ensemble model is 90%, which is better than the individual models and the model trained using B-mode or SE images alone. Moreover, some patients that were misclassified by the traditional methods were correctly classified by the proposed ensemble method. The proposed ensemble DL model will enable radiologists to achieve superior detection efficiency owing to enhance classification accuracy for breast cancers in ultrasound (US) images.

Photoacoustic imaging (PAI) has attracted great attention as a medical imaging method. Typically, photoacoustic (PA) images are reconstructed via beamforming, but many factors still hinder the beamforming techniques in reconstructing optimal images in terms of image resolution, imaging depth, or processing speed. Here, we demonstrate a novel deep learning PAI that uses multiple speed of sound (SoS) inputs. With this novel method, we achieved SoS aberration mitigation, streak artifact removal, and temporal resolution improvement all at once in structural and functional in vivo PA images of healthy human limbs and melanoma patients. The presented method produces high-contrast PA images in vivo with reduced distortion, even in adverse conditions where the medium is heterogeneous and/or the data sampling is sparse. Thus, we believe that this new method can achieve high image quality with fast data acquisition and can contribute to the advance of clinical PAI.

Significance Multimodal imaging based on optics and ultrasound can provide guide images and complementary structural and functional information, thus improving the accuracy of medical diagnosis and treatment monitoring. However, because conventional ultrasound transducers are opaque, in multimodal imaging with optics, the optical devices must be placed off-axis from the ultrasound transducer. This off-axis arrangement is prone to misalignment, adds complexity and bulk to the system, and can result in a low signal-to-noise-ratio. Here, we present a transparent ultrasound transducer at the heart of a quadruple fusion imaging system that seamlessly integrates ultrasound imaging, photoacoustic imaging, optical coherence tomography, and fluorescence imaging, and we demonstrate the system’s use in imaging responses to both ophthalmologic injuries and oncologic diseases., Ultrasound and optical imagers are used widely in a variety of biological and medical applications. In particular, multimodal implementations combining light and sound have been actively investigated to improve imaging quality. However, the integration of optical sensors with opaque ultrasound transducers suffers from low signal-to-noise ratios, high complexity, and bulky form factors, significantly limiting its applications. Here, we demonstrate a quadruple fusion imaging system using a spherically focused transparent ultrasound transducer that enables seamless integration of ultrasound imaging with photoacoustic imaging, optical coherence tomography, and fluorescence imaging. As a first application, we comprehensively monitored multiparametric responses to chemical and suture injuries in rats’ eyes in vivo, such as corneal neovascularization, structural changes, cataracts, and inflammation. As a second application, we successfully performed multimodal imaging of tumors in vivo, visualizing melanomas without using labels and visualizing 4T1 mammary carcinomas using PEGylated gold nanorods. We strongly believe that the seamlessly integrated multimodal system can be used not only in ophthalmology and oncology but also in other healthcare applications with broad impact and interest.

We demonstrate an agent-free multi-structural peripheral angiography technique based on the volumetric photoacoustic (PA) and ultrasound (US) images. The volumetric images are obtained by stacking B-mode PA/US images along the elevational direction using a motorized scanner. Three structural features such as skin, bone, and vasculature are extracted from the volumetric US image and combined with the PA microvasculature image to provide a multi-structural vascular image of the foot. For quantitative PA imaging, we have tested the reliability of the PA images. The method can be used to provide a comprehensive anatomic and functional analysis of the extremity.

Among clinical photoacoustic and ultrasound beamforming (BF) techniques, a nonlinear p-th root delay and sum (NL-p-DAS) exhibits better contrast and resolution than conventional delay-and-sum. However, despite its advantages, signal loss and grainy speckles often degrade image quality by signal deformation. Thus, we proposed a new nonlinear BF algorithm called nonlinear p-th root spectral magnitude scaling (NL-p-SMS), which scales the signal in the spectral domain instead of the temporal domain. As a result, we achieved the enhanced contrast, contrast noise ratio, spatial resolution, and less grainy texture without significant signal or feature loss in both in vivo photoacoustic and ultrasound images.

A recently introduced nonlinear p th root delay-and-sum ( NL- p -DAS ) beamforming (BF) technique for ultrasound (US) and photoacoustic (PA) imaging, achieving better spatial and contrast resolution compared to a conventional delay and sum (DAS) technique. While the method is advantageous for better resolution, it suffers from grainy speckles and dark areas in the image mainly due to the interference of non-sinusoidal functions. In this Letter, we introduce a modified NL- p -DAS technique called nonlinear p th root spectral magnitude scaling ( NL- p -SMS ), which performs the p th root on the spectral magnitude instead of the temporal amplitude. We evaluated the US and PA images of NL- p -SMS against those of NL- p -DAS by comparing the axial and lateral line profiles, contrasts, and contrast-to-noise ratios (CNRs) in both phantom and in vivo imaging studies with various p values. As a result, we found that the NL- p -SMS has better axial resolution and CNR than the NL- p -DAS , and reduces the grainy speckles and dark area artifacts. We believe that, with this enhanced performance, our proposed approach could be an advancement compared to the existing nonlinear BF algorithms.

Acoustic-resolution photoacoustic microscopy (AR-PAM) is a promising technology for vascular or tumor-targeted molecular imaging. Unique advantages of AR-PM are its non-invasive, non-ionizing real-time, and deeper imaging depth. AR-PAM typically uses an ultrasound transducer with a high acoustic numerical aperture (NA) to enable deeper imaging depth. While high NA achieves good lateral resolution in the focal plane but significantly degrades the lateral resolution in the out-of-focus region. Synthetic aperture focusing technique (SAFT) has been introduced to overcome this out-of-focus degradation by synthesizing the correlated signals. Several 2-D SAFTs have been also reported to improve degraded resolution in all directions. However, the resolution enhancement of the previously reported 2-D SAFTs are suboptimal and are not equivalent to the 1-D SAFT performance under an ideal condition with the sample orientation perpendicular to the synthetic aperture direction. In this paper, we present a new 2-D SAFT called 2-D directional SAFT that improves the lateral resolution significantly and we compare our results against 1-D SAFT under ideal condition. We applied this algorithm to phantom and in vivo images to show the improvement in image quality. We also implement this algorithm in a graphical processing unit to achieve high performance to show the practicality of implementing this new algorithm in a system.

Beamforming algorithms are widely used for photoacoustic (PA) imaging to reconstruct the initial pressure map. In the reconstruction process, they typically assumed that the imaged biological tissue was a homogeneous medium. However, as biological tissue is generally heterogeneous, the misassumption causes suboptimal image reconstruction. Because it is difficult to predict the heterogeneity of a medium, it was still common to reconstruct images assuming a uniform medium. To solve this problem, we introduce a deep learning-based algorithm that can correct the speed of sound (SoS) aberration in the PA image. We trained a neural network with the multiple simulation datasets and successfully corrected SoS aberrations in a PA in vivo image of the human forearm. We observed that the proposed algorithm effectively suppressed side lobes and noise in the PA image and greatly improves image quality.

Photoacoustic imaging (PAI) is a new promising medical imaging technology available for diagnosing and assessing various pathologies. PAI complements existing imaging modalities by providing information not currently available for diagnosing, e.g., oxygenation level of the underlying tissue. Currently, researchers are translating PAI from benchside to bedside to make unique clinical advantages of PAI available for patient care. The requirements for a successful clinical PAI system are; deeper imaging depth, wider field of view, and faster scan time than the laboratory-level PAI systems. Currently, many research groups and companies are developing novel technologies for data acquisition/signal processing systems, detector geometry, and an acoustic sensor. In this review, we summarize state-of-the-art clinical PAI systems with three types of the imaging transducers: linear array transducer, curved linear array transducer, and volumetric array transducer. We will also discuss the limitations of the current PAI systems and describe latest techniques being developed to address these for further enhancing the image quality of PAI for successful clinical translation.

In the clinical photoacoustic (PA) imaging, ultrasound (US) array transducers are typically used to provide B-mode images in real-time. To form a B-mode image, delay-and-sum (DAS) beamforming algorithm is the most commonly used algorithm because of its ease of implementation. However, this algorithm suffers from low image resolution and low contrast drawbacks. To address this issue, delay-multiply-and-sum (DMAS) beamforming algorithm has been developed to provide enhanced image quality with higher contrast, and narrower main lobe compared but has limitations on the imaging speed for clinical applications. In this paper, we present an enhanced real-time DMAS algorithm with modified coherence factor (CF) for clinical PA imaging of humans in vivo. Our algorithm improves the lateral resolution and signal-to-noise ratio (SNR) of original DMAS beamformer by suppressing the background noise and side lobes using the coherence of received signals. We optimized the computations of the proposed DMAS with CF (DMAS-CF) to achieve real-time frame rate imaging on a graphics processing unit (GPU). To evaluate the proposed algorithm, we implemented DAS and DMAS with/without CF on a clinical US/PA imaging system and quantitatively assessed their processing speed and image quality. The processing time to reconstruct one B-mode image using DAS, DAS with CF (DAS-CF), DMAS, and DMAS-CF algorithms was 7.5, 7.6, 11.1, and 11.3 ms, respectively, all achieving the real-time imaging frame rate. In terms of the image quality, the proposed DMAS-CF algorithm improved the lateral resolution and SNR by 55.4% and 93.6 dB, respectively, compared to the DAS algorithm in the phantom imaging experiments. We believe the proposed DMAS-CF algorithm and its real-time implementation contributes significantly to the improvement of imaging quality of clinical US/PA imaging system. Keywords: Optoacoustic, Image reconstruction, Imeage enhancement, High frame rate

Pressure ulcer (PU) formation is a common problem among patients confined to bed or restricted to wheelchairs. The ulcer forms when the affected skin and underlying tissues go through repeated cycles of ischemia and reperfusion, leading to inflammation. This theory is evident by intravital imaging studies performed in immune cell‐specific, fluorescent reporter mouse skin with induced ischemia‐reperfusion (I‐R) injuries. However, traditional confocal or multi‐photon microscopy cannot accurately monitor the progression of vascular reperfusion by contrast agents, which leaks into the interstitium under inflammatory conditions. Here, we develop a dual‐wavelength micro electro mechanical system (MEMS) scanning based optical resolution photoacoustic microscopy (OR‐PAM) system for continuous label‐free functional imaging of vascular reperfusion in an IR mouse model. This MEMS‐OR‐PAM system provides fast scanning speed for concurrent dual‐wavelength imaging, which enables continuous monitoring of the reperfusion process. During reperfusion, the revascularization of blood vessels and the oxygen saturation (sO_2) changes in both arteries and Illustration of the vascular reperfusion of the ear skin. veins are recorded, from which the local oxygen extraction ratios of the ischemic tissue and the unaffected tissue can be quantified. Our MEMS‐OR‐PAM system provides novel perspectives to understand the I‐R injuries. It solves the problem of dynamic label‐free functional monitoring of the vascular reperfusion at high spatial resolution.

We had developed a real-time clinical photoacoustic (PA) and ultrasound (US) imaging system by combining a programmable ultrasound machine and a wavelength-tunable laser. The system was able to acquire real-time images of biological tissue, but the user had to restart the image acquisition software to modify parameters for optimizing the images. We have recently updated the system to adjust imaging parameters in real-time by implementing a real-time parameter control software, which is compatible with the programmable platform of the US machine. To adjust the parameters of both PA and US images, we also implemented custom functional blocks including beamforming, frequency demodulation, log compression, decimation, scan conversion, and image display. To acquire real-time images, we performed all the calculations by using parallel processing with a graphic processing unit in US machine. The updated system has the great potential to be widely applied to a variety of clinical and preclinical applications because it allows real-time optimization of imaging parameters as well as visualizing the images in real-time.

Acoustic-resolution photoacoustic microscopy (AR-PAM) has great advantage over deep imaging depth when compared to optical-resolution PAM (OR-PAM). This is because that the point spread function (PSF) of AR-PAM is determined by the acoustic focus, which is relatively less scattered in biological tissues. In general, to maintain a high signal-to-noise ratio (SNR) and lateral resolution at a deep depth, AR-PAM uses an acoustic lens with a high numerical aperture (NA). The high NA lens provides high resolution and SNR in focal region, but significantly degrades the SNR and resolution in out-of-focus region. To overcome this problem, many researchers have introduced the synthetic aperture focusing technique (SAFT), which sums up the corresponding signals in the solid angle of the acoustic NA. However, the image enhancement of the conventional SAFTs has been limited because those techniques accumulate the signals without considering the actual photoacoustic (PA) wavefronts. In this study, we propose a novel SAFT that can overcome the existing limitation by exploiting each enhanced frequency components of the 1D SAFT images performed in multiple directions. As a result, we confirmed that the output AR-PAM image of our novel SAFT is superior to the existing SAFT image quality.

The 2019 novel coronavirus (COVID-19) has spread rapidly all over the world. The standard test for screening COVID-19 patients is the polymerase chain reaction test. As this method is time consuming, as an alternative, chest X-rays may be considered for quick screening. However, specialization is required to read COVID-19 chest X-ray images as they vary in features. To address this, we present a multi-channel pre-trained ResNet architecture to facilitate the diagnosis of COVID-19 chest X-ray. Three ResNet-based models were retrained to classify X-rays in a one-against-all basis from (a) normal or diseased, (b) pneumonia or non-pneumonia, and (c) COVID-19 or non-COVID19 individuals. Finally, these three models were ensembled and fine-tuned using X-rays from 1579 normal, 4245 pneumonia, and 184 COVID-19 individuals to classify normal, pneumonia, and COVID-19 cases in a one-against-one framework. Our results show that the ensemble model is more accurate than the single model as it extracts more relevant semantic features for each class. The method provides a precision of 94% and a recall of 100%. It could potentially help clinicians in screening patients for COVID-19, thus facilitating immediate triaging and treatment for better outcomes.

We have developed a reflection-mode switchable subwavelength Bessel-beam (BB) and Gaussian-beam (GB) photoacoustic microscopy (PAM) system. To achieve both reflection-mode and high resolution, we tightly attached a very small ultrasound transducer to an optical objective lens with numerical aperture of 1.0 and working distance of 2.5 mm. We used axicon and an achromatic doublet in our system to obtain the extended depth of field (DOF) of the BB. To compare the DOF performance achieved with our BB-PAM system against GB-PAM system, we designed our system so that the GB can be easily generated by simply removing the lenses. Using a 532 nm pulse laser, we achieved the lateral resolutions of 300 and 270 nm for BB-PAM and GB-PAM, respectively. The measured DOF of BB-PAM was approximately 229 μm, which was about 7× better than that of GB-PAM. We imaged the vasculature of a mouse ear using BB-PAM and GB-PAM and confirmed that the DOF of BB-PAM is much better than the DOF of GB-PAM. Thus, we believe that the high resolution achieved at the extended DOF by our system is very practical for wide range of biomedical research including red blood cell (RBC) migration in blood vessels at various depths and observation of cell migration or cell culture.

Photoacoustic (PA) imaging is one of the fastest growing imaging technologies nowadays in both research and clinical applications, especially due to its unique capability to visualize blood vessels. The PA microscopy (PAM) is classified into two types: optical-resolution PAM (OR-PAM) and acoustic-resolution PAM (AR-PAM). OR-PAM image has a point spread function (PSF) much smaller than AR-PAM because it uses a tightly focused optical beam and the PSF is determined by the optical focus. In contrast, AR-PAM uses an unfocused optical illumination to excite a relatively large area and detects the PA signal from a small area determined by its acoustic focus. Because ultrasound is less scattered than light in biological tissue, AR-PAM can achieve deeper imaging depth than OR-PAM at the expense of image resolution. Due to the limited resolution and imaging depth scale of each PAM type, it is challenging to image vessels in various area of small animals. In this study, we demonstrated in vivo OR-/AR-PAM imaging of blood vessels in various areas such as eye, ear, and hind limb by using a single commercial PAM system. Additionally, we quantified micro-vessel density (MVD) of the mouse eye and ear images, and applied a synthetic aperture focusing technique (SAFT) to correct the distorted PA signal at the out-of-focus in AR-PAM image. As a result, we have demonstrated multiscale PAM imaging of small animal vasculature in various areas with vessel quantification and resolution enhancement, so we believe that this multiscale PAM imaging technique would be helpful in biology research such as ischemia and neovascularization.

Visualization methods are very important in biomedical imaging. As a technology that understands life, biomedical imaging has the unique advantage of providing the most intuitive information in the image. This advantage of biomedical imaging can be greatly improved by choosing a special visualization method. This is more complicated in volumetric data. Volume data has the advantage of containing 3D spatial information. Unfortunately, the data itself cannot directly represent the potential value. Because images are always displayed in 2D space, visualization is the key and creates the real value of volume data. However, image processing of 3D data requires complicated algorithms for visualization and high computational burden. Therefore, specialized algorithms and computing optimization are important issues in volume data. Photoacoustic-imaging is a unique imaging modality that can visualize the optical properties of deep tissue. Because the color of the organism is mainly determined by its light absorbing component, photoacoustic data can provide color information of tissue, which is closer to real tissue color. In this research, we developed realistic tissue visualization using acoustic-resolution photoacoustic volume data. To achieve realistic visualization, we designed specialized color transfer function, which depends on the depth of the tissue from the skin. We used direct ray casting method and processed color during computing shader parameter. In the rendering results, we succeeded in obtaining similar texture results from photoacoustic data. The surface reflected rays were visualized in white, and the reflected color from the deep tissue was visualized red like skin tissue. We also implemented the CUDA algorithm in an OpenGL environment for real-time interactive imaging.

Vulnerable plaque is the one of the leading causes of cardiovascular disease occurrence. However, conventional intravascular imaging techniques suffer from difficulty in finding vulnerable plaque due to limitation such as lack of physiological information, imaging depth, and depth sensitivity. Therefore, new techniques are needed to help determine the vulnerability of plaque, Thermal strain imaging (TSI) is an imaging technique based on ultrasound (US) wave propagation speed that varies with temperature of medium. During temperature increase, strain occurs in the medium and its variation tendency is depending on the type of tissue, which makes it possible to use for tissue differentiation. Here, we demonstrate laser-induced photo-thermal strain imaging (pTSI) to differentiate tissue using an intravascular ultrasound (IVUS) catheter and a 1210-nm continuous-wave laser for heating lipids intensively. During heating, consecutive US images were obtained from a custom-made phantom made of porcine fat and gelatin. A cross correlation-based speckle-tracking algorithm was then applied to calculate the strain of US images. In the strain images, the positive strain produced in lipids (porcine fat) was clearly differentiated from water-bearing tissue (gelatin). This result shows that laser-induced pTSI could be a new method to distinguish lipids in the plaque and can help to differentiate vulnerability of plaque.

Photoacoustic microscopy (PAM) is a hybrid imaging technology using optical illumination and acoustic detection. PAM is divided into two types: optical-resolution PAM (OR-PAM) and acoustic-resolution photoacoustic microscopy (AR-PAM). Among them, AR-PAM has a great advantage in the penetration depth compared to OR-PAM because ARPAM relies on the acoustic focus, which is much less scattered in biological tissue than optical focus. However, because the acoustic focus is not as tight as the optical focus with a same numerical aperture (NA), the AR-PAM requires acoustic NA higher than optical NA. The high NA of the acoustic focus produces good image quality in the focal zone, but significantly degrades spatial resolution and signal-to-noise ratio (SNR) in the out-of-focal zone. To overcome the problem, synthetic aperture focusing technique (SAFT) has been introduced. SAFT improves the degraded image quality in terms of both SNR and spatial resolution in the out-of-focus zone by calculating the time delay of the corresponding signals and combining them. To extend the dimension of correction effect, several 2D SAFTs have been introduced, but there was a problem that the conventional 2D SAFTs cannot improve the degraded SNR and resolution as 1D SAFT can do. In this study, we proposed a new 2D SAFT that can compensate the distorted signals in x and y directions while maintaining the correction performance as the 1D SAFT.

Photoacoustic microscopy (PAM) is a multiscale imaging technique. In optical-resolution photoacoustic microscopy (OR-PAM), a single mode (SM) fiber is normally used as the source of optical excitation to be focused into a diffraction-limited spot. Recent advances in OR-PAM have improved its imaging speed using microelectromechanical systems (MEMS). Here we report for the first time the use of a multimode (MM) fiber as the optical excitation source for high resolution OR-PAM in vivo imaging. A high-speed MEMS scanner based OR-PAM system combined with the mechanical movement to provide wide area imaging was used. The use of multimode fiber for achieving tight optical focus would make the optical alignment easier and high repetition rate light delivery possible for high-speed OR-PAM imaging. A lateral resolution of 3.5 µm and axial resolution of 27 µm with ~1.5 mm imaging depth was successfully demonstrated using the system. The efficacy of multimode fibers for achieving tight focus is beneficial for developing high-resolution photoacoustic endoscopy systems and can be combined with other optical endoscopic imaging modalities as well.

In this study, we developed an imaging device for preclinical and clinical application using photoacoustic effect. The photoacoustic effect is to make ultrasound images using light as its name illustrates. Light irradiated in a short time (nano seconds) is absorbed by the tissue, and the local pressure is generated by the thermo-elastic expansion. The photoacoustic effect is a safe method of imaging because it uses an endogenous chromophore in the human body without the use of an external contrast agent. In this study, the image uses an optical-resolution photoacoustic microscope (OR-PAM) which has a resolution of 12 ¥im. The conventional or-pam has the disadvantage of being slow and bulky using mechanical motors. To overcome this problem, our research team developed a photoacoustic imaging system that uses a MEMS scanner to equip a fast image capability into a compact handheld probe.

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.

Optical resolution photoacoustic microscopy (OR-PAM) is a non-invasive, label-free method of in vivo imaging with microscopic resolution and high optical contrast. Based on intrinsic contrasts, OR-PAM has expanded to include in vivo vessel imaging, flow cytometry, physiological parameter analysis, and single-cell characterization. However, since conventional OR-PAM systems have a fixed tabletop configuration, a large system size, and slow imaging speed, their use in preclinical and clinical studies remains limited. In this study, using microelectromechanical systems (MEMS) technology, we developed a handheld PAM probe with a high signal-to-noise ratio and image rate. To enable broader application of the OR-PAM system, we reduced its size and combined its fast scanning capabilities into a small handheld probe that uses a 2-axis waterproof MEMS scanner (2A-WP-MEMS scanner). All acoustical, optical, and mechanical components are integrated into a single probe with a diameter of 17 mm and a weight of 162 g. This study shows phantom and in vivo images of various samples acquired with the probe, including carbon fibers, electrospun microfibers, and the ear, iris, and brain of a living mouse. In particular, this study investigated the possibility of clinical applications for melanoma diagnosis by imaging the boundaries and morphology of a human mole.

Ocular chemical damage may induce limbal vessel ischemia and neovascularization, but the pathophysiology of the disease is not completely known. To observe changes in blood vessels after alkaline burn, we monitored the anterior segment and choroidal vasculature using a photoacoustic microscope (OR-PAM). We were able to observe not only the iris blood vessels but also the choroidal vessels under the sclera, which were difficult to be observed with conventional photographs. After alkali burning, we observed neovascularization and limbal ischemia and successfully tracked changes in vasculature during the 7-day healing process. We also used the RANdom SAmple Consensus (RANSAC) method to segment the abnormally generated blood vessels in the cornea by detecting the eyeball surface and successfully visualize the distance from each PA signal to the center of the eye. We believe that photoacoustic imaging has an important potential to reveal the pathophysiology of limb ischemia and neovascularization.

Photoacoustic microscopy (PAM) provides high resolution and large penetration depth by utilizing the high optical sensitivity and low scattering of ultrasound. Hybrid PAM systems can be classified into two categories: opticalresolution photoacoustic microscopy (OR-PAM) and acoustic-resolution photoacoustic microscopy (AR-PAM). ORPAM provides a very high lateral resolution with a strong optical focus, but the penetration depth is limited to one optical transport mean free path. AR-PAM provides a relatively greater penetration depth using diffused light in biological tissues. The resolution of AR-PAM is determined by its ultrasonic parameters. In this study, we performed an in vivo testing of a switchable OR-/AR-PAM system. In this system, two modes can be switched by changing its collimator lens and optical fiber. The lateral resolution of OR-PAM was measured using a resolution test target, and the full width at half maximum (FWHM) of the edge spread function was 2.5 μm. To calculate the lateral resolution of ARPAM, a 6-μm-diameter carbon fiber was used, and the FWHM of the line spread function was 80.2 μm. We successfully demonstrated the multiscale imaging capability of the switchable OR-/AR-PAM system by visualizing microvascular networks in mouse ears, brain, legs, skin, and eyes.

We propose an improved synthetic aperture focusing technique based on a delaymultiply- and-sum for acoustic-resolution photoacoustic microscopy. A lateral resolution in out-offocus regions is much improved, thereby extending the imaging focal region.

We propose an improved version of a synthetic aperture focusing technique (SAFT) based on a delay-multiply-and-sum algorithm for acoustic-resolution photoacoustic microscopy (AR-PAM). In this method, the photoacoustic (PA) signals from multiple scan-lines are combinatorially coupled, multiplied, and then summed. This process can be considered a correlation operation of the PA signals in each scan-line, so the spatial coherent information between the PA signals can be efficiently extracted. By applying this method in conventional AR-PAM, lateral resolution and signal-to-noise ratio in out-of-focus regions are much improved compared with those estimated from the previously developed SAFT, respectively, thereby achieving the extension of the imaging focal region. Our phantom and in vivo imaging experiments prove the validity of our proposed method.

In this study, high performance nitride-based flip-chip (FC) light-emitting diodes (LEDs) using optimized distributed bragg reflector (DBR) were fabricated and compared with conventional FC-LED using silver (Ag) reflector. Most of FCLEDs are using the silver (Ag) as reflector due to its superior reflectance at visual spectrum region. However, A silver has detrimental problems such as electro-chemical migration and agglomerations, which resulting in reliability issues such as degradation of power drop, unstable operating voltage and leakage issues. Our DBR structure was designed to have 99% at whole visible spectrum range (400~750nm), which is higher reflectance than silver reflector (90~95%). Optical power is higher than higher than the Ag-LED up to 30% @ 500mA. As the current increases up to 1A, the gap slightly decreased. Reliability test results show stable optical power, operating voltage, and leakage maintenance.

Vulnerable plaques are the major cause of cardiovascular disease, but they are difficult to detect with conventional intravascular imaging techniques. Techniques are needed to identify plaque vulnerability based on the presence of lipids in plaque. Thermal strain imaging (TSI) is an imaging technique based on ultrasound (US) wave propagation speed, which varies with the medium temperature. In TSI, the strain that occurs during tissue temperature change can be used for lipid detection because it has a different tendency depending on the type of tissue. Here, we demonstrate photothermal strain imaging (pTSI) using an intravascular ultrasound catheter. pTSI is performed by slightly and selectively heating lipid using a relatively inexpensive continuous laser source. We applied a speckle-tracking algorithm to US B-mode images for strain calculations. As a result, the strain produced in porcine fat was different from the strain produced in water-bearing gelatin phantom, which made it possible to distinguish the two. This suggests that pTSI could potentially be a way of differentiating lipids in coronary artery.

The detection of metal surface defects is important in achieving the goals of product quality enhancement and manufacturing cost reduction. Identifying the defects with visual inspection is difficult, inaccurate, and time-consuming. Thus, several inspection methods using line cameras, magnetic field, and ultrasound have been proposed. However, identifying small defects on metal surfaces remains a challenge. To deal with this problem, we propose the use of photoacoustic imaging (PAI) as a new non-destructive imaging tool to detect metal surface defects. We successfully visualized two types of cracks (i.e., unclassified and seam cracks) in metal plate samples using PAI. In addition, we successfully extracted cracked edges from height-encoded photoacoustic maximum amplitude projection images using the Laplacian of Gaussian filtering method, and then, quantified the detected edges for a statistical analysis. We concluded that PAI can be useful in detecting metal surface defects reducing the defect rate and manufacturing cost during metal production.