This PDF file contains the front matter associated with SPIE Proceedings Volume 12745 including the Title Page, Copyright information, Table of Contents, and Conference Committee Page.

Photoacoustic Imaging (PAI) is an emerging imaging modality with great potential for preclinical research and clinical practice. Uniquely combining optical excitation and acoustic detection, PAI provides a rich contrast mechanism from either endogenous or exogenous chromophores with high spatial resolutions in both optical ballistic and diffusive regimes. This manuscript focuses on the efforts to translate PAI from the bench to the bedside by increasing the imaging depth and speed, providing clinically useful information (i.e., imaging contrast), reducing the device size, and improving the system’s reliability. Assisted by advanced laser systems, ultrasonic arrays, and data acquisition circuits, modern PAI can reveal hematogenous anatomy and functions at high spatiotemporal resolution in small animals and human organs. As a rapidly evolving imaging technology, PAI promises multiple preclinical and clinical applications, attracting growing interest in oncology, dermatology, pediatrics, neuroscience, and orthopedics, among other areas of study.

Sparse reconstruction in photoacoustic tomography has always faced the problem of artifacts. To address this issue, a diffusion model-based method for sparse data reconstruction in photoacoustic tomography was proposed. During the training phase, the gradient of the probability density of the image was learned as the data prior by adding noise and denoising at each step. During the testing phase, ultrasonic signals are generated by illuminating with pulsed laser and acquired by ultrasonic transducers surrounding the object, which was implemented using the k-Wave toolbox. The reconstructed image was finally obtained by reserve-time Stochastic Differential Equation (SDE). Experimental results on vascular data show that the proposed algorithm can effectively remove artifacts and improve image quality compared with conventional reconstruction methods under 32 and 64 detectors, respectively.

Rapid 3D imaging of whole organs at cellular resolution is vital to pathological research, but this task remains challenging in light sheet microscopy due to the compromise between axial resolution and field of view. In the meanwhile, needed magnification varies when doing different analysis. Here, we report on a high-throughput multi-scale light sheet microscopy which combines adjustable beam shaping system providing continuous imaging magnification from 1.26X~12.6X, sample holding device for fast sample switching, modulation mask to generate thin optical sectioning and large illumination field, capable of imaging a lung lobe with cellular resolution (~2.8μm), centimeter-scale field of view (1.1cm×0.8cm) within 1 minute. We demonstrate the imaging of hundreds of lung lobes of mice and other large-scale tissue. Using our ultra-high throughput light sheet microscopy with multiple magnification, we are able to quickly get the 3D view of the whole tissue and the statistical data of the volume of tumors, the number tumor cells and so on for further biological analysis.

Blood analysis, through the complete blood count, remains the most fundamental medical test for diagnosing broad diseases. Even so, it is still limited to central laboratories with sophisticated facilities and skilled professionals. Here, we propose a simple, machine-vision-aided, label-free blood analysis technique via a regular microscope utilizing contrast-enhance defocus imaging, i.e., defocusing imaging under 415 nm, small aperture illumination. We have shown that this technique can simultaneously obtain leucocytes’ optical phase and erythrocytes’ spectrophotometric information, making it feasible to realize five-part leucocyte differential and hemoglobin quantification with machine vision. The reliability was verified by comparing the quantified results with clinical reference results, which indicates significant linear correlations (significance levels ⪅ 0.0001 and Pearson coefficients ⪆ 0.90). We also show that the virtual staining of the label-free blood cell images can be performed with a generative adversarial network to mimic conventional Wright-Giemsa images, facilitating this technique’s medical translation. This study reports a simple, easy-to-use, quick, reliable blood analysis technique that may lead to a reformation in the blood analysis field. We emphasize this technique’s great potential for early screening of various diseases, including anemia, leukemia, and neglected tropical diseases, especially in resource-limited settings.

Diffusion Optical Tomography (DOT) is an emerging optical imaging modality with the advantages of being low-cost, non-invasive and non-damage-free, it has demonstrated great potential in differentiating benign and malignant breast tumors. However, due to the strong scattering of biological tissues and the limited number of boundary measurements, the DOT image reconstruction is ill-posed and ill-conditioned. Conventional DOT image reconstruction algorithms such as Tikhonov regularization, are easy to implement. Nevertheless, images with poor quality were usually achieved. In this paper, to improve the image quality of DOT, we develop a post-processing reconstruction algorithm based on deep learning. It has a type of U-net architecture, but with the low-quality image obtained with Tikhonov regularization and the acquired multi-wavelength optical signals as the input. Meanwhile, the output of the network is images of chromophores, including HbO, Hb and water. To train the proposed algorithm, we construct a dataset with 15,000 images for training, 6,000 for validation, and 3,900 for testing. Our results demonstrate that it has achieved good reconstruction results for the phantoms with single or double inclusions in terms of PSNR and SSIM. Compared to the Tikhonov regularization, the average PSNR of HbO, Hb and water are improved by 55%, 74% and 71%, respectively, and the average SSIM are improved by 11%, 76%, and 70%, respectively.

Liver cancer is one of the most common digestive system malignancies with an average five-year survival rate of less than 20%, while traditional methods are often unautomated, labeling required, and limited for early liver cancer detection. Exosomes are a type of extracellular vesicles with a diameter of 40-150 nm, which play important role in disease diagnosis and treatment. It is of interest to develop a label-free optical system for the analysis of nanoscale exosomes. Here, we developed a label-free two-dimensional (2D) light scattering acquisition system for the measurements of microparticles and the exosomes derived from the normal liver cells. By adjusting the thickness of the light sheet for illumination in our system, nanoparticles down to 41 nm are detected. The visualization and accurate particle size analysis of liver cell exosomes are then performed by our 2D light scattering technology. Our method is expected to have important applications in the quantitative analysis field of cellular and extracellular structures that may find potential applications in clinics such as for early cancer diagnosis.

Early detection of retinopathy in the periphery of the macula is an important step in preventing severe vision loss. Some morphological parameters about the extensive retina can be obtained through ultra-wide-field OCTA images. Based on small-scale fundus OCTA vessel segmentation, accurate diagnosis can already be obtained by means of deep learning. However, no similar research of segmentation of peripheral blood vessels is reported. In this study, blood vessels of retina were segmented, and blood vessel centerlines were extracted in ultra-wide-field OCTA images. Quantification of the segmented images was performed to explore features of blood vessel. We used a U-shaped neural network that performs well on small samples to cope with the problem of limited data sets. Scale compression and slice segmentation were used to apply the trained network model to vessel segmentation and centerline extraction in ultra-wide-field OCTA images which is of size at 21mm×21mm. Based on the results of the segmentation of blood vessels, the diameter index of blood vessels and vascular tortuousness were calculated, which proved to be associated with some eye diseases. These results and parameters can be helpful for the early screening of some ophthalmic diseases.

Laser Speckle Contrast Imaging (LSCI) as the advantages of high speed, wide field of view, and ease of implementation has been widely used for imaging blood flow and vascular deformation during Vascular targeted Photodynamic Therapy (V-PDT). However, uncorrected direction and or scale kernel failed to improve the visualization of blood vessels derived from poor Signal Noise Ratio (SNR) of micro vessels during V-PDT. In this study, we extracted the adaptive scale and directional kernel using dual-wavelength reflection imaging to enhance laser speckle imaging. The in vivo data showed that the proposed method improved the 4%-29% performance of contrast-to-noise ratio (CNR) and provide a powerful monitoring tool for blood flow and vascular deformation during V-PDT.

Due to the advantages of high sensitivity, high resolution and nondestructive in vivo three-dimensional detection, optical coherence tomography has been widely studied in various fields such as biology, genetics and medicine. Zebrafish is a kind of freshwater fish, whose embryos are easy to reproduce in large numbers and have high transparency for observation. In particular, the genetic homology between zebrafish and human is as high as 70%, which makes zebrafish gradually become an excellent model for studying human development or various serious diseases. In this study, a method for continuous observation of zebrafish embryos using OCT was proposed. In this experiment, the development of zebrafish embryos before hatching (0dpf-3dpf) was continuous observed by OCT, and the proportion of yolk sac to embryo volume was extracted and quantified. The proportion of embryos collected by OCT was compared with the proportion of zebrafish embryos observed by microscope. All experiments were repeated three times. The results show that the method of quantification of zebrafish embryo development by OCT can not only observe the internal development structure of the embryo, but also calculate the volume proportion of embryo development more accurately than microscope. This method provides a more rapid and precise important means for early clinical judgment of embryo development.

In this study, the modified spectral domain polarization-sensitive optical coherence tomography (SD PS-OCT) is proposed for determining the birefringence of the myocardial tissue. In this modified SD PS-OCT, the circular polarization state of light was generated before entering the beam splitter. Thus, the polarization states in the reference and sample arms are both circular, and the symmetry between them is good without using additional Quarter-Wave Plate (QWP), which reduce the dispersion effect. The results demonstrated that theoretical analysis for determination of birefringence including the phase retardance and the fast axis orientation based on Stokes parameters of backscattered from biological tissue, which is different from the traditional SD PS-OCT. In addition, the phase retardance and the fast axis orientation was used to differentiate the myocaridal tissue in the diastole of the cardiac cycle the from that in the systole of the cardiac cycle. The findings suggest that the SD PS-OCT be a potential tool for the real-time monitoring the change of the myocardial wall during the cardiac cycle.

Photoacoustic Microscopy (PAM) is a novel medical imaging technique developed rapidly in recent years with the advantages of high resolution, high contrast, and no ionizing radiation. In this study, we developed a One-Shot functional Photoacoustic Microscopy (OS-fPAM) system for assessing Traumatic Brain Injury (TBI). The OS-fPAM system only employed a single 532 nm nanosecond pulse laser with Raman delay optical path control, which can produce both 532 nm and 553 nm laser beams. The dual-wavelength beams were then coupled via a dichroic mirror and focused on the surface of the experimental sample. Then, the photoacoustic signal was collected and amplified in the ultrasonic signal acquisition module, and the blood oxygen saturation (sO₂) image was calculated by two wavelength photoacoustic images. The feasibility of the OS-fPAM system was performed through the calibration experiments with different ratios of red ink and blue ink. Meanwhile, the OS-fPAM system can monitor microvascular structures and sO₂ in the TBI model on mice for 96 hours in vivo with a spatial resolution of ~40 μm, demonstrating the potential to evaluate rehabilitation and cerebral vascular damage.

Image segmentation is a critical technology in many fields, such as image processing, pattern recognition, and artificial intelligence. It is also the first and critical step in computer vision technology. Tongue diagnosis combined with deep learning for segmentation and extracting pathological features is relatively mature, but deep learning combined with TCM visualization is sporadic. First, We used the U2Net network¹ for segmentation extraction of the sclera in this study. Where the U2Net¹ network1 (based on PyTorch) relies on the extensive use of data enhancements to use the available annotation samples more efficiently, and compared with the U-Net network, the U2Net network¹ updates an RSU module, each RSU module is a small U-net network,merging multiple U-Net outputs to get the merged Mask target. Finally, we applied classical CNN networks to evaluate the segmentation effect, introducing different evaluation metrics such as Miou, Precision, and Recall. We used the publicly available dataset UBIVIS.V12 for our experiments, where our Miou was as high as 97.3%, and U2Net achieved better results among all the networks, which laid the foundation for our subsequent segmentation and extraction of blood filament features.

As a noninvasive biomedical imaging modality, photoacoustic imaging has shown great potential for clinical application recently with simultaneous high contrast and penetration depth. Optical-resolution photoacoustic microscopy is one important branch of photoacoustic imaging, which can achieve high spital resolution. However, this technique suffers from limited depth of field due to the strongly focused Gaussian beam used. With the development of deep learning in various medical imaging techniques, deep learning also has had a significant impact on the field of photoacoustic imaging in recent years. This paper presents a novel method to improve the depth of field of photoacoustic microscope by integrating the U-net semantic segmentation model with the simulation platform of photoacoustic microscopy based on k-Wave. First, we imaged the blood vessels on the simulation platform to obtain the vascular slice B-scan images and the corresponding ground truth images, and then the dataset was randomly divided into the training dataset and the testing dataset in a ratio of 7:1. During the U-Net model training process, the B-scan images serve as the input to the model while corresponding ground truth images serve as the labels. Finally, this study demonstrates the potential of the U-Net model to improve the depth of field of photoacoustic imaging. And this method effectively improves the accuracy of obtaining structural features of tissues, which has significant implications for the diagnosis and treatment of diseases.

To explore better ablation effects and rules, and guide clinical applications, 355nm high-peak-power pulsed laser is being used to replace the 308nm excimer laser in the ablation of coronary thrombus like tissues. It is characterized with the compact structure, low cost and high stability and its higher peak power leads to a higher ablation efficiency. The microparticle size of pig blood clot, chicken tibia clot and pig sebum clot with thrombus-like components were mainly measured after the UV laser ablation. Tissue temperature rising during ablation was also concerned. The particle sizes of blood clot and chicken tibia samples were concentrated at 0.5-1 µm and 1-5 µm, respectively, and the sebum clots became oil droplets. The channel wall of ablation was smooth, and the tissue temperature rise was less than 5°C. 355nm high-peak-power pulsed laser can be effectively applied to coronary artery thrombosis, coexisting atherosclerotic plaques and calcified tissues.

Photothermal therapy is an emerging tumor treatment technology used for precise treatment of malignant tumors. This technology utilizes materials with high photothermal conversion efficiency to aggregate in tumor tissue, where external therapeutic light sources generate light energy and convert it into thermal energy, selectively killing cancer cells. Compared with traditional treatment methods such as surgery, chemotherapy, and radiotherapy, photothermal therapy has the advantages of non-invasive, targeted, minimal side effects, and repeatable treatment. At the same time, the treatment interval of photothermal therapy is shorter, which can further alleviate the treatment pain of patients. Photothermal therapy technology can also improve immune efficacy by combining synergistic effects with other therapies such as immunotherapy. Due to the lack of real-time and accurate temperature measurement technology, it is difficult to adjust the treatment power in real time, leading to consequences such as overtreatment and seriously affecting the effectiveness of photothermal therapy. Therefore, a non-invasive temperature measurement and regulation method is needed. We propose a photothermal therapy system and method based on adaptive neural network and validate the algorithm and system using an agar-based phantom. The experimental results show that the method has good temperature measurement and control characteristics, with a temperature measurement accuracy of 0.76 °C, and achieves a temperature control accuracy of 0.52 °C within a 21 second adjustment time. It is expected to further improve the safety and stability of PTT.

Semi-automatic segmentation of coronary vessels in Digital Subtraction Angiography (DSA) images is more practical than fully automatic segmentation. In this paper, a novel algorithm is proposed to extract coronary vessels semi-automatically. This algorithm is a combination of the traditional livewire algorithm and the gradient vector flow-Frangi (GVF-Frangi) filter. Results showed that the proposed approach is more robust and effective in the semi-automatic segmentation of coronary vessels than the traditional livewire algorithm.

Artifact in photoacoustic tomography is always an issue to be solved. Here, a deep learning based physical model method to remove artifact for limited-data photoacoustic tomography was proposed, termed as PD net. A virtual photoacoustic tomography platform was constructed based on k-Wave, and the dataset required for deep learning was obtained using this virtual platform. The U-Net was used to build a deep learning network to remove artifacts in sparse-view and limited-view photoacoustic tomography. Under sparsity condition, when the number of ultrasonic transducers is 64, the improvement rates of SSIM and PSNR of the network are 274% and 66.34%, respectively, compared with the input of the network, which verifies that this method can remove artifacts in sparse-view photoacoustic tomography. The proposed method can reduce artifacts and enhance anatomical contrast when the number of ultrasonic transducers used is limited, and effectively reduce manufacturing costs of photoacoustic tomography.

Photoacoustic tomography is a new hybrid imaging technology, which combines the high contrast of optics imaging with the high penetration of acoustics imaging, moreover, which provides deep imaging of biological tissue. At present, photoacoustic tomography has been widely applied in biomedical research, such as brain imaging, cancer diagnosis, and vascular imaging. However, constructing a photoacoustic imaging system is costly and complex. In this study, a simulation platform for photoacoustic tomography was established based on the k-wave toolbox. Using this virtual photoacoustic tomography platform, the process of ultrasonic signal generation is simulated, the signals are collected by the same number of ultrasonic transducers at different center frequencies, and multiple groups of reconstructed images are obtained using the back projection algorithm. The relationship between the center frequency and the quality of the reconstructed image is analyzed to show that when an appropriate center frequency is selected, the ultrasonic transducer can effectively receive the photoacoustic signal; when the selected center frequency is too large or too small, the ultrasonic transducer may not effectively receive the photoacoustic signal, leading to the loss of some detailed features of the original image, which can result in an increase in the number of artifacts. Therefore, by selecting an appropriate center frequency of ultrasonic transducers, the quality of reconstructed images can be effectively improved.

Mitochondria functions as a major power source in diverse cell physiological processes. Mitochondria dysfunction is associated with many diseases, such as neurodegenerative diseases and various cancers. It is crucial to understand mitochondrial metabolism in live cells and related dysfunction in diseases. Currently, three-dimensional (3D) cultured tumor spheroids have been widely used in drug development in cancer therapy fields, due to their similarities with the animal model compared with 2D cultured cells. However, characterization methods to monitor metabolism status in tumor spheroids are limited by the lack of techniques to measure mitochondrial metabolic fluxes in a noninvasive and real-time way. Here, we proposed a Fluorescence Lifetime Imaging Microscopy (FLIM) based method to monitor the mitochondria status in 3D cultured cells. MCF-7 tumor spheroids or 2D cultured cells were labelled with a mitochondria specific dye Rhodamine123 (R123). Images of cultured cells were captured with a FLIM system and analyzed with software SPCImage, providing the size and shape of each tumor spheroid as well as the fluorescence lifetime values of mitochondria. In 2D cultured cells, the fluorescence lifetime values of each dye had little fluctuation, indicating similar physiological status. In tumor spheroids labelled with R123, the fluorescence lifetime values of the inner part of spheroids were uneven compared with those of the outer edge. The reason for this uneven lifetime span of mitochondria may be associated with the difference of status between the inner and outer layers of tumor spheroids. In conclusion, this FLIM based method has shown great advantages to characterize the mitochondria status in 3D cultured tumor spheroids, providing a powerful quantitative method in cellular physiology research fields.

At present, approximately one third of adults worldwide are obese or overweight. The phenomenon of adipose browning, that is, adipocyte trans-differentiation from an energy storage role to a heat production role, opens up a new way to reverse obesity. However, current methods for judging whether adipose browning occurs are usually based on invasive histological assessments. Adipose browning has been found to be accompanied by changes in adipose microvascular system. Therefore, our research focused on the above biological feature and used a non-invasive photoacoustic imaging technique that integrates the advantages of optical and acoustic imaging to visualize adipose microvascular system in obese mice treated with cold stimulation in a three-dimensional mode. The results showed that based on hemoglobin components, photoacoustic molecular imaging could not only accurately visually display a full view of increased arteriovenous blood network and enhanced blood oxygen consumption during adipose browning, but also quantitatively analyze it. Moreover, the data was consistent with the results of histological means including hematoxylin-eosin staining and immunofluorescence staining. In summary, we demonstrated the feasibility of photoacoustic molecular imaging for detecting adipose browning. This work will provide a new possibility for non-invasive assessment of adipose browning, which can help researchers and clinicians fight against obesity.

Tissue local temperature information is necessary for guiding treatment parameters in photothermal therapy. Therefore, a temperature monitoring method suitable for the treatment process is needed for monitoring tissue temperature in real time. In this study, a temperature monitoring system based on PID on the photothermal effect of graphene oxide on tissue was proposed. Graphene Oxide (GO) has high photothermal conversion performance and low cytotoxicity under near infrared laser irradiation at 808nm. The photoacoustic imaging system and infrared thermal imager were employed to monitor the effect of GO as a photothermal agent on the photoacoustic signal and temperature of tissues. Firstly, the relationship between the intensity of photoacoustic signal and the temperature of tissues under the action of GO was established. Then, the PID feedback algorithm was applied to monitor and regulate the temperature change of tissues by the intensity of photoacoustic signal, so as to achieve the purpose of photothermal treatment. The results show that GO can enhance the photoacoustic signal of the tissue under laser irradiation and improve the temperature of the irradiated tissue. The system can effectively monitor and regulate the tissue temperature to achieve the therapeutic effect of tumor with little effect on normal tissue.

Photoacoustic Imaging (PAI) is a non-invasive imaging technology. It breaks through the limitation of shallow imaging depth of traditional optical imaging and low imaging contrast of acoustic imaging. It has a potential wide application in early cancer detection. PAI combined with endoscopy has great significance in the early diagnosis of gastric tumors. However, imaging just obtains the local location information. It cannot obtain the information of any location visually, especially in the process of photothermal therapy. In this paper, COMSOL was employed to simulate the visual model of photoacoustic photothermal imaging of gastric tissue and tumor. The physical processes of photoacoustic imaging such as photothermal strain-ultrasound generated by pulsed laser in stomach were simulated. A complete photoacoustic model was established through the coupling of each physical field. Firstly, a simplified gastric tissue model was constructed, and physical parameters were set for grid division. Then several physical field modules in COMSOL were coupled. The light distribution, temperature change and photoacoustic signal generated by thermal expansion during the propagation of light in biological tissues were obtained by simulation. The simulation results showed that the distribution of light in gastric tumor was significantly lower than that around the stomach. The tumor absorbs more light energy, resulting in a significantly higher temperature than surrounding stomach tissue. The photoacoustic signal of thermal expansion caused by laser irradiation of biological tissue was obtained. This study is helpful to understand the propagation and interaction of light in normal tissues and gastric tumors. It also provides a certain reference for the research and application of photoacoustic imaging in gastric cancer.

Photoacoustic imaging is an imaging technology which combines the advantages of high-resolution optical imaging and deep detection depth of acoustic imaging. Photoacoustic imaging combined with hysteroscopy may be a new diagnostic technique for endometrial cancer. However, the energy loss after pulsed laser passing through the hysteroscope is very large. Therefore, the energy of pulsed laser after hysteroscopy based on photoacoustic imaging is worth further discussion. A coupling Program of pulsed laser and hysteroscope based on the optical path of pulsed laser and hysteroscope was designed in this paper. The Program was optimized by ZEMAX simulation, and then the optimal effect of pulsed laser observation through hysteroscopy was verified by phantom experiment. The results show that the pulsed laser can obtain better photoacoustic signals after passing through our coupling module. This method is expected to be applied to the detection of endometrial diseases in clinic.

Photoacoustic imaging has developed rapidly in recent years and can effectively detect tumors, and this paper proposes a non-destructive photoacoustic imaging detection method suitable for uterine tumors based on delayed superposition algorithm. A three-dimensional model of the uterus was designed using Solidworks software and printed on a 3D printing device. Strong absorbers were placed in different parts of the model, the detection of tumors in different parts was simulated, the pulsed laser was transmitted to the uterus by hysteroscopy, and the ultrasound probe was used to receive ultrasound signals in vitro, and image reconstruction was carried out to verify the sustainability of subsequent experiments. The embryonic uterus was made by using a fresh pork belly to cover the uterus model, and the imitation uterus was imaged, and the optical absorption parameters of the tumor and normal uterine tissue were used to reconstruct the uterus image. Aiming at the blurring of the image caused by the noise brought in during data acquisition, the noise cancellation method is used to effectively solve this problem. Experiments have shown that the system can accurately detect the structural size and location depth of the simulated tumor.

The intraluminal filament Middle Cerebral Artery Occlusion (MCAO) model is widely used to study animal models of cerebral ischemia. Laser Speckle Contrast Imaging (LSCI) provides an approach to monitor changes in cortical Cerebral Blood Flow (CBF) in mice through intact skull. However, the MCAO surgery requires a supine position of animals during preparation, where the conventional LSCI systems cannot achieve to provide a real-time high spatio-temporal resolution CBF information. Herein, we implemented an optimized method to uninterruptedly measure the relative CBF during surgical procedures by designing an inverted LSCI (i-LSCI). This approach achieves a real-time high spatio-temporal resolution CBF information during the MCAO surgery, enabling a real-time monitoring of cerebral collateral circulation and assessment of the stability of the ischemic site immediately after MCAO. Moreover, this optimization allows for a detection of Cortical Spreading Depolarization (CSD) in ischemic cortex immediately after MCAO.

In Chinese medicine, eye diagnosis is essential for diagnosis and treatment. However, most current image-processing techniques focus on tongue diagnosis, and most foreign studies on ocular diagnosis focus on segmenting fundus vascular images. Moreover, most of the foreign studies on scleral vessels are focused on identification rather than on TCM discernment. Scleral vessels can significantly characterize the pathological features of the human body’s five internal and six internal organs. Scleral vessels are essential for the objective study of TCM visual diagnosis. However, due to the small size and complex structure of scleral vessels, it is difficult to extract them by existing methods effectively. To achieve more accurate scleral blood vessel extraction, we introduce the residual connection structure and CA-Module attention mechanism in the U2Net1 network to avoid the incompatibility between high-level and low-level features and enhance the information extraction by input fusion and feature extraction of RSU blocks. The experimental results show that Miou achieves an accuracy of 83.3%. The F1-score reaches 91.7%, which is more effective than the existing SOTA fundus vascular segmentation network FR-UNet2 for the experiments. According to the experimental results, Res-U2Net can segment sclerar vessels accurately. In future experiments, we will improve the vessel feature extraction network to increase its accuracy and gradually achieve better results.

Rapid 3D imaging of meso- or macro-scale organs and organisms at sub-micron resolution is challenging in life sciences. We present a static light-sheet microscopy that can achieve imaging sequences at a high speed of up to 100 fps. Our method utilizes a double-ring light modulation module in the illumination optics path to produce a low sidelobe Bessel light-sheet, providing hardware benefits for high-throughput imaging. In our study of the tumor microenvironment in human lymphoma tissue, we achieved triple-color achromatic imaging and used a custom algorithm to analyze cell co-localization, cell distance, and other relevant statistical parameters. Moreover, by combining our light-sheet system with a raster, we achieved uniform high resolution across the entire sample. Our method enables various cellular analyses at the systems level and has the potential to assist scientists in solving scientific problems.

Excessive stress levels can lead to fluctuations in pilot operating techniques, decreased quality of work, increased errors, impulsive behavior, and communication difficulties, resulting in reduced pilot coping ability to the environment, behavioral disorders, and flight accidents. The purpose of this study is to analyze the cerebral blood oxygen characteristics of pilots before and after stress and the relationship between this characteristic and their antiglare performance by functional near-infrared techniques (fNIRs), and to establish an index for the evaluation of pilots' stress response. A total of 15 pilots were recruited in this study, and the stress of the pilots was induced by spin ladder training, while the cerebral blood oxygen of the pilots was collected simultaneously by fNIRs. It was found that the difference in HbO₂ between the left and right brain regions was significantly different before and after the stress stimulation caused by spin-ladder training, and the difference in HbO₂ was elevated by 0.072 (p ⪅ 0.05) after the stimulation. In contrast, the difference in Hb decreased by 0.068 (p ⪅ 0.05) after the stimulation. Furthermore, the value of the change in HbO₂ showed a significant negative correlation with the level of anti-vertigo in the subjects: r = -0.925, p ⪅ 0.01. The results of this study can be used to indicate the level of rotational stress in pilots and to predict and assess the anti-glare potential of flight trainees.

Sleep problems have become a social problem worldwide. Currently, drug therapies commonly used to treat sleep problems are usually accompanied by addiction and other side effects. Therefore, non-invasive brain stimulation techniques have received a lot of attention due to their non-invasive, target-adjustable and non-addictive advantages. Among these techniques, Transcutaneous Electrical Nerve Stimulation (TENS) has been proved to be effective in neuromodulation and can be used to relieve various types of pain and improve sleep quality. However, the sleep modulation mechanism and evaluation index of TENS are still unclear. In this study, four TENS modes were proposed to stimulate the nerves of the neck by superimposing high and low frequency signals to investigate their effects on assisting sleep. The electroencephalogram (EEG) signals of the subjects were collected before and after stimulation, processed and analyzed. The effect of TENS was evaluated by combined time-frequency domain analysis and Gravity Frequency (GF). The results showed that there was a significant difference in the gravity frequency before and after stimulation (p-values ⪅0.05). The highest increase in delta power was observed after each mode of stimulation. Delta power enhancement was concentrated in the frontal and occipital lobes. Therefore, all four modes may have some facilitation effect on sleep, and the two modes (mode I and mode IV) had the most significant sleep-assisting effect. Meanwhile, this study provides a new idea to determine the TENS stimulation modes and targets and to quantitatively evaluate the TENS effect.

The kidney is an important organ in the body to excrete metabolic waste, and the glomerulus is an essential structure for the kidney to play a role in blood filtration. Abnormal glomerular numbers are associated with nephropathy or circulatory disease. With the development of imaging technology, mesoscopic optical imaging can obtain whole kidney images at single cell resolution. The detection of glomeruli from images is very crucial for understanding the renal function and studying nephropathy. Existing detection methods cannot balance both accuracy and efficiency, so we proposed a deep learning-based glomeruli detection method. First, we imaged an entire mouse kidney with High-Definition fluorescent Micro-Optical Sectioning Tomography (HD-fMOST) and obtained a three-dimensional (3D) kidney image at cellular resolution. Then, we designed an end-to-end 3D convolutional neural network based on the morphological features of glomeruli in the kidney image, which can directly read 3D images and predict the precise coordinates of glomeruli. We used the acquired kidney dataset to train the network and validated the effect of glomeruli detection. Finally, we applied our approach to detect glomeruli in large-scale mouse kidney. The results showed that the proposed method reached the state-of-the-art level, which is more efficient and accurate compared to similar methods. The proposed method will provide a powerful tool for kidney-related research.

Three-dimensional dose prediction is an important step in automatic radiotherapy planning. Using deep learning combined with Knowledge-Based Planning methods (KBP) can achieve dose distribution prediction on CT images. Convolutional Neural Networks (CNN) are an important branch of deep learning algorithms. This article will briefly introduce the application of convolutional neural networks and other advanced algorithm structures in dose prediction. And there are different studies that evaluate the model output results by studying different transformation models, different patient data and data of different treatment methods, and find the optimal dose prediction model. However, the research on dose prediction models is not the most complete. There is still room for further research in terms of input tumor types, treatment methods, etc. Moreover, automatic radiotherapy plan generation is the ultimate goal, and further research is needed.

Differential Phase Contrast (DPC) imaging plays an important role in the family of quantitative phase measurement. However, the reconstruction algorithm for quantitative DPC (qDPC) imaging is not yet optimized, as it does not incorporate the inborn properties of qDPC imaging. In this research, we propose a simple but effective image prior, the Dark-field Sparse Prior (DSP), to facilitate the phase reconstruction quality for all DPC-based phase reconstruction algorithms. The DSP is based on the key observation that most pixel values for an idea differential phase contrast image are zeros since the subtraction of two images under anti-symmetric illumination cancels all background components. With this DSP prior, we formed a new cost function in which L0-norm was used to represent the DSP. Further, we developed the algorithm based on the Half Quadratic Splitting to solve this NP-hard L0-norm problem. We tested our new model on both simulated and experimental data and compare it against State-Of-The-Art (SOTA) methods including L2-norm and total variation regularizations. Results show that our proposed model is superior in terms of phase reconstruction quality and implementation efficiency, which significantly increases the experimental robustness, while maintaining the data fidelity. In general, the DSP supports high-fidelity qDPC reconstruction without any modification of the optical system, which simplifies the system complexity and benefit all qDPC applications.

In this paper, we review research progress on bio-fluorescence imaging and microscopy using Superconducting Nanowire Single-Photon Detectors (SNSPDs) and introduce the fractal SNSPDs that feature high detection efficiency, low polarization sensitivity, fast operating speed, high timing resolution, and broad-spectrum response. These combined merits make them ideal candidates for light sensors in bio-fluorescence imaging and microscopy.

Diffuse Optical Tomography (DOT) is a promising noninvasive imaging method which quantifies optical parameters to achieve functional characteristics of biological tissues. Since boundary measurements are limited and the light propagation in biological tissues is highly diffusive, DOT image reconstruction is ill-posed and ill-conditioned. Traditional iteration-based regularization methods, such as Tikhonov regularization, tend to produce low-quality images with severe artifacts. Deep learning reconstruction methods based on convolutional neural networks may ignore the structure information of finite element mesh during feature extraction and cause the loss of spatial features. In order to overcome these problems, we propose a novel neural network based on graph convolution to improve the quality of reconstructed DOT images. Graph convolution can make full use of the spatial structure information of finite element mesh and deal with the potential correlation between optical features effectively. To verify the performance of the proposed algorithm, simulation experiments were performed. We also compared the proposed algorithm with Tikhonov regularization method and deep learning method based on convolutional neural network. Experimental results show that proposed algorithm obtains better reconstruction results in terms of ABE, MSE, PSNR and SSIM.

Cherenkov-Excited Luminescence Scanned Tomography (CELST) is a new emerging imaging modality, which uses the Cherenkov light to excite fluorophores for tomographic imaging. In order to improve the imaging depth and spatial resolution, a rotational CELST was developed to scan the imaging object to produce sinogram data, and a Filtered Back Projection (FBP) was used to recover the distribution of fluorophores. However, the images reconstructed by FBP are usually corrupted by artifacts due to measurements from limited angles. To reduce the artifacts, we propose a deep learning-based reconstruction algorithm (SAM-Unet), which is based on a fully convolutional deep neural network with U-Net structure, and a spatial attention module was added between the encoder and the decoder. The image features extracted by the spatial attention module are transferred to the decoder through a skip connection structure. The effectiveness of the proposed SAM-Unet is verified by numerical experiments, and the results show that the SAM-Unet can improve the mean square error (MSE) (97.5%), Peak Signal-To-Noise Ratio (PSNR) (81.9%) and Structure Similarity Index Measure (SSIM) (63.4%) compared with the FBP algorithm. Compared with the deep learning method U-Net, the MSE improved 39.8%, the PSNR improved 8.0% and SSIM improved 2.6%.

Light scattering flow cytometry has been demonstrated for label-free particle and cell analysis. The high-throughput imaging of cells or particles in flow cytometry is fundamentally challenging as motion blur may occur for weak-light measurements. Here we perform light scattering measurements on 3.87 μm and 4.19 μm microparticles in diameters with our light scattering flow cytometer. Two-dimensional (2D) light scattering patterns are imaged by a Complementary Metal Oxide Semiconductor (CMOS) sensor. The hydrodynamic focusing effect is studied for better high-throughput measurements. Motion-blur images are obtained at 2.4 mm/s flow rate with 10 ms exposure time, and a deblurring algorithm is adopted for analysis. The experimental 2D light scattering patterns agree well with the Mie theory simulation. Moreover, the number of 3.87 μm and 4.19 μm microparticles in flow can be determined, where the error is less than 5% compared with the theoretical results.

Chlorella is a unicellular spherical green microalga with alternate colors from blue green to yellowish or red due to different components of innate pigments. Light and salinity are two important environmental factors in Chlorella culture. Light conditions directly affect the growth and biochemical composition of microalgae, while salinity change could influence the pigment composition of Chlorella. Therefore, it has crucial research significance to monitor the response of Chlorella to salinity stress under different light conditions. Recently, Fluorescence Lifetime Imaging Microscopy (FLIM) technology has been widely applied into biological fields, providing fluorescence lifetime values for quantitative analysis. Here, FLIM method was used to observe the autofluorescence of a freshwater microalga, Chlorella sp.. Chlorella cells were treated with a series of salinity concentrations (control sample in normal culture medium, 3S sample with an additional 3× salinity, 7S sample with an additional 7× salinity, respectively) under light (12 h/12 h light/dark cycles) or dark (0 h/24 h light/dark cycles) treatments. After one day, images of the microalgae cells from each group were obtained with FLIM system, followed by an analysis with SPCImage software. The results showed that 3× salinity condition had little effect on Chlorella in both light/dark conditions, suggesting the adaptive capacity of Chlorella to seawater salinity. By contrast, the mean fluorescence lifetime values in 7S samples under light conditions were significantly decreased compared to that of the control. Interestingly, similar lifetime values were observed in 7S samples and the control samples under dark conditions, which indicated a potential high salinity resistance induced by different light/dark conditions. In conclusion, FLIM could work as a fast evaluation method of the physiological status of living Chlorella sp. under different culture conditions in a quantitative way.

Inorganic arsenic (iAs) is one of the most toxic metalloids which could accumulate in marine species, especially in clams, causing serious ecological risk. Marine clams accumulate high level of iAs in different tissues. Currently, Fluorescence Lifetime Microscopy (FLIM) technique has provided quantitative information in biochemical diagnosis. In this study, we applied FLIM method into analyzing Hematoxylin and eosin (H&E) stained sections of arsenic exposed Ruditapes philippinarum. The clams were exposed under different concentrations of As(Ⅲ) and As(V) for thirty days. Fluorescent images of the H&E stained hepatopancreas tissue were obtained with FLIM system, followed with data analysis for fluorescence lifetime values. The average fluorescence lifetime of sections in the control group was around 250 ps. The average lifetime value in the As(V) group was slightly increased to around 280-300 ps. The average lifetime value in the As(III) group achieved a significant increase to around 340 ps. These results suggested a higher extent of structural change in As(Ⅲ) exposed group than As(V) group. As a result, this work has provided quantitative evaluation standard for the toxicity of marine mollusk based on fluorescence lifetime imaging method.