This PDF file contains the front matter associated with SPIE Proceedings Volume 11553, including the Title Page, Copyright information, and Table of Contents.

The brain is the most complex and significant organ, but little is known regarding to the mechanisms of its function, which is related to brain anatomy. Conventional anatomical methods based on brain slices fail to reconstruct the neural projection in axial direction at single-cell resolution. To solve the problem, my lab has spent more than ten years developing Brain-wide Positioning System (BPS), a novel solution combining microscopic optical imaging and physical sectioning to obtain the tomographic information of a whole brain with sub-micron voxel resolution. BPS includes several generations such as Micro-Optical Sectioning Tomography (MOST) and several types of fluorescence MOST (fMOST). In this talk, I will introduce the principles of BPS and demonstrate how to locate and visualize the labelled neurons and neuronal networks in the whole brain. The pipeline includes whole-brain sample preparation, whole-brain optical imaging, and massive brain image processing and analyzation. BPS may play a crucial role and usher in a new era of Brainsmatics. Brainsmatics refers to the integrated, systematic approaches of measuring, analyzing, managing, and displaying brain spatial data, including but not limited to the concepts of digital mapping and visualization of the brain neuronal/vascular networks, brain atlas, brain connectome and projectome, brainnetome, neuroinformatics, and neuroimaging. Brainsmatics will provide comprehensive and systematic information to understand the brain, defeat the brain disease, and develop the brain-inspired intelligence.

Interferometric single-molecule localization microscopy (iPALM, 4Pi-SMS) uses multiphase interferometry to localize single molecules and currently achieves the highest axial resolution of all 3D superresolution approaches. In theory, 3D sub-10 nm resolution can be achieved with only 250 photons collected in each objective for an individual molecule. However, the resolution achievable with the current image analysis workflow is substantially worse than the theoretical limit. Here, we developed an experimental PSF fitting method for the interference 4Pi-PSF. As the interference phase is not fixed with respect to the shape of the PSF, we developed a new 4Pi-PSF model, which decouples the phase term from the shape of the PSF. Using a spline-interpolated experimental PSF model and by fitting all 3 or 4 phase images globally, we showed on simulated data that we can achieve the theoretical limit of 3D resolution, the Cramér-Rao lower bound (CRLB), also for 4Pi microscope.

Fiber bundle coupler is a key part in a probe-based endomicroscope used to couple laser in each core of the fiber bundles precisely when the laser scan unit is performing a high speed confocal scanning. Common fiber connector is usually used in communication application with single fiber core. But for image transmitting, common connector such as SMA must be manually adjusted in 5 axes to locate all fiber cores of the bundle. A micron precision fiber bundle coupler is introduced in this article. This coupler is special designed for an endomicroscope. This coupler can locate the position of the fiber cores of a bundle in micro precision in all 3 dimensions with the help of the mechanical structure and focusing mechanism. The coupler has a plug and a socket component. A polished fiber bundle is installed in the center of the plug. A connection core is located in the center of the socket component to make sure the plug and the coupling objective can stay in a same axis, so that the fiber bundle can also located in the same axis. A number of arms distributed symmetrically can be pushed by the operating cover to apply a constant pressure on the plug through a spring to lock the plug. The coupling objective can move along the axis inside the connection core by a linear actuator. An image evaluation algorithm can help the actuator to find a proper location to achieve auto focus. The coupler can work smoothly and automatically. It is very easy for clinical use.

Acoustic-resolution photoacoustic microscopy (AR-PAM) is a promising tool for microvascular imaging. In AR-PAM, a focused transducer is typically used. Limited by acoustic diffraction, in-focus lateral resolution is dependent on the center frequency and numerical aperture of the transducer. On the other hand, out-of-focus lateral resolution will deteriorate, which can be restored to in-focus lateral resolution by synthetic aperture focusing technique (SAFT). Previously, we demonstrated that with prior knowledge of the point-spread function of the AR-PAM imaging system, combined SAFT and Richard-Lucy deconvolution can be applied to achieve super resolution (SR) beyond acoustic diffraction limit and to enhance signal-to-noise ratio (SNR) in both focal and out-of-focus regions. However, SNR of the original AR-PAM image highly affects the performance. Moreover, discontinuities arise in the line pattern that is originally continuous. In this study, we propose a novel algorithm, which combines a novel SAFT method and a directional model-based (D-M) deconvolution method, to break the acoustic diffraction limit. By using our algorithm, FWHM of 20 ~µm for AR-PAM system over DOF of ~1.8 mm is experimentally achieved. Compared with our previous work using Richard-Lucy deconvolution, the D-M deconvolution demonstrates the advantages in high SNR, and good line continuity. Compared with the directional SAFT method, our algorithm achieves SR and higher SNR.

Adenosine aggravates pain via the interactions of adenosine A2A receptor (A2AR) and adenosine A1 receptor (A1R). And heteromerization of A2AR and A1R can put adenosine into operation. Simultaneously, a growing body of information indicates Ca2+ participate in pain management. Our team proved the interaction between A1R and A2AR by Iem-spFRET method. In the present study, we estimated the influence of the dimerization on the concentration of intracellular Ca2+. These dates demonstrate that the interaction of pain-related receptors (A2AR and A1R) has a significant effect on intracellular Ca2+ signaling.

Tissue optical clearing techniques have provided important tools for large-volume imaging. Here, m-xylylenediamine (MXDA) is firstly introduced into tissue clearing and used to develop a rapid, highly efficient aqueous clearing method with robust lipophilic dyes compatibility, termed MXDA-based Aqueous Clearing System (MACS). MACS can render whole adult brains highly transparent within 2.5 d and is also applicable for other intact organs. Meanwhile, MACS possesses ideal compatibility with multiple probes, especially for lipophilic dyes. MACS achieves three-dimensional (3D) imaging of the intact neural structures.

Crohn's disease (CD) and gastrointestinal luminal tuberculosis (ITB) are two kinds of similar inflammatory bowel diseases, whose incidences are growing rapidly worldwide. Due to the lack of a general gold standard to distinguish between CD and ITB samples, misdiagnosis often occurs in clinical detections, leading to inappropriate treatments and side-effects. The characteristic features of both CD and ITB tissues include tuberculosis and surrounding fibrous structures, which can be quantitatively evaluated by polarimetric techniques. In this study, we apply the transmission Mueller matrix microscope developed in our previous study on the CD and ITB tissue samples to attain their 2D Mueller matrix images. We calculate the Mueller matrix polar decomposition and transformation parameters, which can provide information about the location, density and distribution behavior of the tuberculosis areas surrounded by fibrous structures. In order to evaluate the different distribution behaviors of the fibrous structures quantitatively, we analyzed the retardance related Mueller matrix derived parameters images, which show different features between the CD and ITB tissues, using the Tamura images processing method (TIPM). The preliminary results show that the TIPM analysis of the retardance related parameters can provide some quantitative parameters to describe the different textures of fibers in the CD and ITB tissues. Moreover, we use the machine learning method based on Mueller matrix derived parameters to distinguish between CD and ITB tissues. It is demonstrated that the Mueller matrix derived parameters combined with machine learning methods can be helpful for clinical diagnosis.

Cardiovascular disease is one of the major diseases that cause human death. Atherosclerotic plaque rupture is the main pathogenesis of cardiovascular disease, which leads to coronary heart disease, such as myocardial ischemia, myocardial infarction, internal carotid atherosclerosis and other cardiovascular and cerebrovascular diseases. Intravascular optical coherence tomography (IVOCT) plays an important role in the diagnosis and treatment of cardiovascular diseases because of its high speed and high resolution. In the current various imaging examination method, clinician evaluate the coronary atherosclerotic plaque structural change by using intravascular ultrasound (IVUS) and IVOCT. IVOCT can provide micron size resolution, which is ten times of IVUS. However, the collected images have motion artifacts during the catheter is pulled back due to the periodic heartbeats. The artifacts would reduce the recognition accuracy of the plaques type and affect the preoperative planning and postoperative follow-up of percutaneous coronary intervention surgery. Based on the comprehensive analysis of the mechanism of motion artifacts and the characteristics of periodic cardiac motion, we designed a new algorithm to correct the rigid motion artifacts of coronary IVOCT images that caused by the heartbeat. This new algorithm can compensate part of translation to suppress of motion artifacts. Compared with the electrocardiogram control method, this algorithm does not need to discard the useful frames in the cardiac cycle, thus ensuring the integrity of the images.

Optical coherence tomography (OCT) is an indispensable diagnostic tool in ophthalmology which can provide crosssectional anatomic and functional information of the eyes. For analysis of the optical imaging performance of the eye, the ZEMAX ray tracing software can be used to establish the refractive model and to simulate the light spot and wavefront in ocular fundus. In our study, by combination of OCT and ray tracing technique, the imaging performance of mouse eye was evaluated and the central and peripheral image qualities were quantitatively analyzed. The whole mouse eyes were imaged by a customized OCT system and the OCT images were corrected for distortions caused by the refraction of light on the ocular surface. Parameters of mouse eye, both at central and peripheral areas, including corneal thickness, radius of curvature, lens thickness, etc., were then measured. Finally, the imaging performance of the ophthalmology system was evaluated by using ZEMAX software for ray tracing. The light spot size, the defocus blur parameter and the wavefront aberration were quantitatively evaluated. Our study may provide a method for research on myopia and hyperopia.

Because of its similar genetic makeup with humans, zebrafish are an available and well-established human disease model in vivo for various human diseases as well as the drug safety-evaluation process. However, few optical imaging methods could effectively visualize the structure of adult zebrafish due to their limited penetration depth. In this paper, in vivo high-resolution and long-term characterization of various kinds of human disease models based on zebrafish were achieved with optical coherence tomography (OCT). The capability of three-dimensional OCT imaging was also played important role in visualization of zebrafish disease model. The combination of zebrafish and OCT demonstrated gratifying results in vivo characterization of human disease models based on adult zebrafish with optical coherence tomography.

As a traditional Chinese medicine practice, cupping therapy has been widely used for thousands of years to promote blood circulation and release symptoms of some diseases. The actual effect, however, has been debatable due to the lack of scientific evidence. Aiming to objectively assess the treatment effect, in this study we introduce optical-resolution photoacoutic microscopy to monitor the structural and functional changes of microenvironment parameters pre- and post-cupping through facial cups. Whilst further investigation is in demand, this pilot study provides a new imaging perspective to understand the mechanism and evaluate the effect of cupping therapy.

This study investigates the fluence rate effect of photodynamic therapy (PDT) by using photoacoustic imaging method, which enables subtle biological responses, including vascular damage, inflammation reaction and self-healing response to be studied. Our results reveal the correlations between fluence rate and PDT efficacy/self-healing magnitude, indicating that vascular injuries induced by high fluence rate PDT are more likely to recover and by low fluence rates are more likely to be permanent. There exists a turning point (278mW), above which PDT practically produces no permanent therapeutic effect and damaged vessels can fully recover. These findings have practical significance in clinical setting. For cancer-related diseases, the ‘effective fluence rate’ is useful to provoke permanent destruction of tumor vasculature. Likewise, the ‘non effective range’ can be applied when PDT is used in applications such as opening the blood brain barrier to avoid permanent brain damage.

In this study, we proposed the linear array ultrasound transducer based photoacoustic imaging (PAI) method for peroxynitrite flux study when having Epilepsy. To our best knowledge, this is the first study using high-speed and high-resolution imaging system that can achieve study of the dynamic flux of peroxynitrite in the whole brain. A novel PAI probe was designed based on high frequency linear array ultrasound transducer for high-resolution brain imaging. Sparse sampling based compressed sensing combined with arc-rotary scanning strategy was proposed to achieve high speed PAI of the whole Brain. A mice epileptic model was established, which was continuously monitored for the dynamic flux of peroxynitrite within the brain using a peroxynitrite sensitive photoacoustic probe: Manganese (II) Texaphyrin (MMn). The dynamic neurochemical mechanisms in the process of epileptic seizure was investigated and the correlation between epilepsy burst and severe neuron injury was explored.

Photoacoustic mesoscopy is an emerging noninvasive imaging modality, which offers high resolution 3D images with optical absorption contrast at depths beyond the light diffusion limit. Ultrasound sensor based on a Fabry-Perot (FP) polymer cavity has the following advantages: broadband frequency response, wide angular coverage and small footprint. We present a photoacoustic mesoscope based on a tunable Fabry-Perot interferometer, which offers the potential for reducing system cost and making array of such sensor. A cw diode laser working at 650nm was used to heat the sensor, offering an active tune range of 5nm by elongating the cavity. Ex-vivo and in-vivo imaging experiments demonstrated the imaging capability of this PA mesoscope, showing great potential in biological and medical applications.

The survival rate for renal cancer patients is closely related to the surgical margin status. Thus, accurate and rapid detection of renal cancer is needed. Here, we integrated photoacoustic tomography (PAT) with ultrasound imaging in a single system, which achieved tissue imaging depth about 3 mm and imaging speed about 3.5 cm2/min. We used the wavelength at 1064 nm and 1197 nm to map both blood and lipid distribution in 16 normal and 17 clear cell renal cell carcinoma (ccRCC) tissues, collected from nephrectomy. Our results indicated that the photoacoustic signal from lipids, but not blood, was significantly higher in ccRCC tissues than that in normal tissues. Moreover, based on the quantification of lipid area ratio, we were able to differentiate normal and ccRCC with 100% sensitivity, 80% specificity, and area under receiver operating characteristic curve of 0.95. Our findings show promise of using multimodal PAT for intraoperative ccRCC detection.

Pancreatic cancer is an extremely malignant disease with high mortality rate. Currently there is no effective therapeutic strategy for highly metastatic pancreatic cancers. Laser immunotherapy (LIT) is a combination therapeutic approach of targeted phototherapy and immunotherapy, which could destroy treated primary tumors with elimination of untreated metastases. Here, we investigate the antitumor immune response of LIT on a highly aggressive phenotype Pnac02-H7 mouse pancreatic tumor model. LIT affords a remarkable efficacy in suppressing tumor growth in pancreatic tumors in mice, and results in complete tumor regression in many cases. LIT could synergize targeted phototherapy and immunological effects of immunoadjuvant, which represent a promising treatment modality to induce systemic antitumor response through a local intervention, paving the way for the treatment of highly metastatic pancreatic cancers.

Cancer theranostic agents with a combination of cancer therapy and real-time imaging/diagnosis, provide a promising prospect for individualized and precise cancer treatment. Here, a series of structure-inherent multifunctional fluorescent small molecules, namely heptamethine cyanine dyes, were developed simultaneously for tumor-targeting, near-infrared imaging and therapy. Obtainment of cancer theranostic agents based on structure-inherent multifunctional small molecules, is a simple and straightforward way, and small molecules have advantages of easy, low-cost and large-scale synthesis, including good repeatability and quality control. Therefore, our findings may provide an alternative approach to develop multifunctional cancer theranostic agents.

Neurosurgery is currently a common solution for decreasing the brain tumor burden, but it relies heavily on surgeons' experience, and intraoperative guidance still cannot be researched. To implement the real-time minimally invasive theranostics of brain tumors, the research on the intraoperative precision diagnosis and therapeutics need to be conducted. Here, we develop an optical minimally invasive theranostics system, which uses multimodal optical image including an optical coherence tomography (OCT), photoacoustic imaging (PAI) to visualize brain tumors, and uses supercontinuum laser to ablate brain tumors. The Fourier domain algorithm is employed to compute the optical attenuation coefficient (OAC) of OCT images for quantitatively identifying the brain cancerous tissue. Furthermore, we design an intelligent reseau-based optical theranostic method to integrate the intraoperative OCT imaging and laser ablation for treating brain tumors in vivo.

In situ quantification of photosensitizer is critical in photodynamic therapy (PDT) and photodiagnosis (PD). Fluorescence detection is a feasible approach for the quantification of fluorescent photosensitizer. However, due to the interference of tissue absorption and scattering on the fluorescence spectrum of photosensitizer, it is still challenging to perform in situ fluorescence quantification. In this preliminary study, a Monte Carlo (MC)-based method was used to simulate the fluorescence spectrum and diffuse reflection spectrum of different biological tissues. A calibration algorithm was developed for the correction of the influence of tissue absorption and scattering on protoporphyrin IX (PpIX) fluorescence. Under the excitation of blue light of 405 nm the dispersion coefficient of the original PpIX fluorescence spectrum of the soft tissue phantoms was 28%, which was reduced to 3% after the correction using the calibration algorithm. Under the excitation of red light of 635 nm, the dispersion coefficient of the original PpIX fluorescence spectrum of the soft tissue phantoms was 25%, which was reduced to 1.5% after the correction using the calibration algorithm. The results show that the MC-based method can effectively improve the accuracy of PpIX fluorescence measurement.

Dual-tracer PET signal separation is a typical problem in the field of PET imaging. Previous studies mainly focused on separating dual-tracer PET signals on activity image reconstructed by traditional iterative methods, which ignore the influence of quality of reconstructed images and need additional time for reconstruction pro- cedure. In this work, dual-tracer PET signal separation is cracked as simultaneously direct reconstruction and separation from mixed sinogram based on deep learning, we introduced a three-dimensional encoder-decoder net- work to achieve it. The network can learn and combine temporal information and spatial information properly, and spatiotemporal information plays an important role in signal separation and structure reconstruction respec- tively. We evaluated the proposed method both in Monte Carlo simulation experiment and SD rats experiment. Experimental results show that the proposed method can obtain better single tracer PET activity image than another method also based on deep learning and belongs to direct reconstruction.

We propose a spatiotemporal modulation method to achieve super-resolution imaging at a depletion power two orders of magnitude lower than traditional counterpart. By increasing the pulse interval between excitation and depletion lasers, the fluorescence lifetime data contain the spatiotemporal information of confocal and STED photons at the same time. Two kinds of information are bounded by depletion pulse in a period of the pulse trains, and their intensity difference represents the stimulated emission intensity by donut-shaped depletion laser. Finally, low-power STED imaging with high image quality is realized by subtracting the enhanced stimulated emission intensity from the confocal one.

In eukaryotic cell, various organelles and cytoskeletons are very dynamic, yet highly organized to orchestrate complex cellular functions. Visualizing these interactions requires noninvasive, long duration imaging of the intracellular environment at high spatial and temporal resolution. However, the tradeoffs between spatial and temporal resolution, and low phototoxicity/photobleaching in current super-resolution imaging techniques prevent biologists from accurately characterizing these dynamic processes. To achieve these normally opposing goals, in this talk I will discuss our latest developments in grazing incidence structured illumination microscopy (GI-SIM), and lattice light sheet microscopy (LLSM).

The resolution of an optical imaging system, a microscope, is always theoretically limited due to the physics of diffraction. Memorial to Ernst Karl Abbe, who approximated the diffraction limit of a microscope as, d=λ/2nsinθ, λ is the light wavelength, n is the index of refraction of the medium being imaged in, and the term “nsinθ” representing the numerical aperture. The resolution of multiphoton (N-photon) fluorescence microscopy is described by the formula d=λ/(2nsinθN^1/2), (N≥2), which can theoretically help to improve the resolution. However, it is challenging in practice to address the paradox that higher-order nonlinearity (N) always results in longer excitation wavelength (λ). Photon upconversion nanoparticles (UCNPs) are capable of converting low-intensity near-infrared light to UV and visible emission through the synergistic effects of light excitation and interionic energy transfer. To break the limit of multiphoton imaging resolution, we proposed visible-to-visible four-photon microscopic imaging by using a 730-nm CW laser diode to excite the Nd³⁺-sensitized UCNPs with a 161-nm sub-diffraction resolution obtained. The stimulated emission depletion (STED) microscopy that has broken the diffraction limit of optical microscopic imaging has become crucial methods for molecularly-resolved imaging in the life sciences and beyond, with the resolution governed by d=λ/(2nsinθ(1+I/Isat)^1/2). However, application of ultrahigh beam intensity in STED, imposed by the ultrafast spontaneous emission nature of commonly used fluorescent organic probes, often causes phototoxicity, photobleaching, and re-excitation background. In 2015, we firstly realized the optical emission depletion of near infrared UCNPs and demonstrated its large potential for super-resolution microscopy. In 2017, we developed a novel low-power CW laser enabled superresolution using designed UCNPs. We have experimentally achieved low-power, nonphotobleaching cytoskeleton STED imaging at subcellular scale. Can we break the theoretical limit of Isat, like breaking the diffraction limit? Yes, in our very recent progress we have successfully broken the theoretical limit of Isat by two orders to sharply pull down the laser power for super-resolution. Our approach using new depletion mechanism circumvents the fundamental high-intensity constraint of STED imaging and provides background-free, contrast-enhanced imaging at a spatial resolution of 1/38th of the excitation wavelength.

Structured illumination microscopy (SIM) achieves doubled spatial resolution through exciting the specimen with high-contrast, high-frequency sinusoidal patterns. Such an illumination pattern can be generated by laser interference or incoherent structured pattern. Opto-electronic devices, such as Spatial Light Modulator (SLM) or Digital Micro-mirror Device (DMD), can provide rapid switch of illumination patterns for SIM. Although DMD is much more cost-effective than SLM, it was previously restricted in association with incoherent light sources. To extend its application with coherent illumination, we model the DMD as a blazed grating, and simulate the effect with DMD pattern changes in SIM. Based on the simulation, we report a fast, high-resolution and cost-efficient SIM with DMD. Our home-built laser interference-based DMD-SIM (LiDMD-SIM) reveals the nuclear pore complex and microtubule in mammalian cells with doubled spatial resolution.

We live amid the long-term risk of novel coronavirus (COVID-19), and need to find any new approaches to conquer globe COVID-19 epidemics including any effective vaccines, precise sensing tools, ultraviolet C wavelength (UVC) disinfection technologies. Up-to-date, it is still elusive to acquire point-of-care clinical diagnosis tools or personalized nanophotonic sensors for tracing end-expiration COVID-19, due to the principle limit of evanescent-field sensing technologies and detection sensitivities among nano- to pico-molar, despite dual-functional plasmonic photothermal biosensors for highly accurate severe acute respiratory syndrome coronavirus 2 detection with the limit of 0.22 pico-molar within several minutes in air is recently published in ACS Nano., antibodies, DNA strands or enzymes to capture and detect a COVID-19 target molecule, or analyte in a sample are widely applied. Miniaturizing everything, automating it, simplifying it and making it clinically reliable is the final goal of developing the next generation of approaches to drive personalized precision medicine. To meet these demands, atto-level nanobiophotonic sensing approach using silicon technology beyond evanescent-field principle limits is postulated herein. This approach is based on quantum-field principle, wherein single biophoton donors and acceptors are self-assembled by photoluminescent nanomedicine crystals on silicon substrates. The design of novel diagnostics based on nanobiophotonic sensing approach using silicon technology based on quantum-field principle is opening new avenues for personalized medicine, optical label-free biosensing, directly real-time analyzing biomolecular interactions, further exploiting biomedical applications, boosting the development of precise and individualized therapies for COVID-19 infection etc. worldwide serious diseases and facilitating the widespread administration of such personalized approaches.

High-resolution imaging technologies, such as multiphoton imaging (MPM) and optical coherence tomography (OCT), are capable of high-speed imaging of biological tissues in vivo with subcellular resolution. In brain cancer surgery, it is challenging to distinguish cancer from noncancer intraoperatively. This study shows that MPM can provide label-free images with histological details. Increased cellularity, microvascular proliferation, nuclear pleomorphism and collagen deposition, can be clearly visualized in cancerous human brain tissues. Photodynamic therapy (PDT) is an effective treatment for cancers. The change of tumor vasculatures, including a newly-formed microvascular, in response to PDT, is a key assessment parameter for optimizing the treatment effect. We demonstrated the in vivo imaging of PDT effects on mouse tumor model with an ultrahigh-resolution functional OCT. The technologies have shown significant translational potential for cancer detection and PDT treatment assessment.

In the context of cutaneous carcinoma in vivo diagnosis, Diffuse Relectance (DR) and skin endogenous fluores- cence (AF) spectra, acquired using Spatially Resolved (SR) multimodal optical biopsy, can be analysed to discard healthy from pathological areas. Indeed, carcinogenesis induces morphological and metabolic changes affecting endogenous fluorophores such as for instance elastosis and enzymatic degradation of collagen fluorescence in the dermis or decreased NADH fluorescence in the epidermis. The present contribution aims at studying the path and propagation depth distribution of DR and AF photons in skin in the perspective of analyzing how these photons contribute to the corresponding acquired spectra carrying local physiological information. Modified CudaMCML-based simulations were performed on a five-layer human skin optical model with (i) wavelength resolved scattering, absorption and endogenous fluorescence properties and (ii) multiple fiber optic probe ge- ometrical configuration of a SR-DR and -AF spectroscopic device. The simulation results provided numerical evidences of the behaviour of detected photons in the tissue. In particular, we succeeded in linking the character- istic penetration depth of the detected photons to their wavelengths and the source-sensor distance. In addition, we managed to identify the region where the fluorescence events associated with the AF spectrum photon take place. The study provides qualitative and quantitative tools that can be useful during the design of an optical multimodal biopsy device.

Parallel confocal spectroscopy can significantly expand the analytical capacity of single biological cells and Raman hyperspectral imaging. Here, we report the development of a compressive sensing technique for single-acquisition multifocal Raman spectroscopy, which is capable of improving the speed of conventional confocal Raman spectroscopy by 2-3 orders of magnitude. The technique generates a 2-D multifocus excitation pattern and simultaneously record the Raman spectra from the multi-foci by projecting their scatterings along both the vertical and horizontal direction of the CCD camera, and both a pseudo-inverse and a hierarchical sparsity algorithms are developed to retrieve the individual spectra. The performance was validated by Raman spectroscopy of multiple trapped cells as well as by large-scale Raman imaging.

Polarization imaging has shown great potentials in biomedical studies, due to its sensitivity to microstructural changes and anisotropic optical effects in biological tissues and contrast between lesion site and normal tissue. However, for total polarization measurement, no matter Muller matrix or Stokes vectors, their imaging processes are both time-consuming and not suitable for dynamic physiological monitoring. In this study, we proposed a method to obtain rapid continuous Stokes images and present it application in tissue characterization with clearing. We use single rotating retarder configuration to attain 2D Stokes vector images, the tissue clearing process and the Stokes measurement are carried synchronously on our vertical experimental device. According to the representation of the Stokes vectors on the Poincare sphere, we extract the linear-retardance and circular-depolarization parameters to characterize the microstructures of biological samples, and valid them by two tissue experiments. We show the dynamic Stokes imaging sequences of two types of in vitro tissue samples with clearing on the resolution board, and evaluate the clearing induced temporal variations quantitatively based on the polarization parameters. These experimental results demonstrate the feasibility of our rapid Stokes imaging based on Stokes measurement, implying a potential application in the tissue administration and physiological process.

Voltage imaging has become an emerging technique to record membrane potential change in living cells. Yet, compared to electrophysiology, microscopy approaches are still limited to relative membrane voltage changes, lacking important information conveyed by absolute membrane voltage. This talk will cover a spectroscopy approach to tackle this challenge. A spectroscopic signature of membrane potential was identified through stimulated Raman scattering (SRS) imaging, which enabled label-free, sub-cellular voltage imaging of mammalian neurons. We employed pre-resonance electronic absorption to enhance SRS imaging sensitivity and specificity. microbial rhodopsin voltage sensors, providing a quantitative approach to measure membrane potential. Quantitative voltage imaging by SRS has enabled mapping of absolute voltage in a neural network and has great potential in neurology and brain sciences.

In this work, the possibility of using machine learning in the spectral analysis of exhaled breath for early diagnosis of diseases is considered. Experimental setup consists of a quantum cascade laser with a tuning range of 5.4–12.8 μm and Herriot astigmatic gas cell. A shallow convolutional neutral network and principal component analysis is used to identify biomarkers and its mixtures. A minimum detectable concentration for acetone and ethanol at sub-ppm level is obtained for optical path length up to 6 m and signal-to-noise less than 3. It is shown that neural networks in comparison with statistical methods give a lower detection limits for the same signal-to-noise ratio in the measured spectrum.

Necrosis is a form of cell death caused by an external factor of the cell, such as hypoxia. It is usually associated with rapidly growing malignancies in the breast, colon, brain, lungs, kidney, and pancreas. Multiphoton microscopy (MPM) based on intrinsic nonlinear optical signals were used to monitor the morphological changes of biological tissues and identify tumor tissue necrosis in breast cancer patients, as well as surrounding tumor cells and collagen. In this study, we performed MPM imaging of the breast tissue and found that there were two types of necrosis in the breast tissue, namely intraluminal necrosis and interstitial necrosis. Different types of necrosis may have different effects on the prognosis. It means MPM may provide a new assistant tool for pathologists to quickly and effectively identify tumor necrosis. It is expected that rapid identification of tumor necrotic areas can provide prognostic information for early recurrence or death, thus helping to diagnose and treat cancer.

Photoacoustic microscopy with large depth of focus is significant to the biomedical research. The conventional opticalresolution photoacoustic microscope (OR-PAM) suffers from limited depth of field (DoF) since the employed focused Gaussian beam only has a narrow depth range in focus, little details in depth direction can be revealed. Here, we developed a computed extended depth of field method for photoacoustic microscope by using wavelet transform image fusion rules. Wavelet transform is performed on the max amplitude projection (MAP) images acquired at different axial positions by OR-PAM to separate the low and high frequencies, respectively. The fused low frequency coefficients is taking the average of the low-frequency coefficients of the low-frequency part of the images. And maximum selection rule is used in high frequency coefficients. Wavelet coefficient of the MAP images are compared and select the maximum value coefficient is taken as fused high- frequency coefficients. And finally the wavelet inverse transform is performed to achieve large DoF. Simulation was performed to demonstrate that this method can extend the depth of field of PAM two times without the sacrifice of lateral resolution. And the in vivo imaging of the mouse cerebral vasculature with intact skull further demonstrates the feasibility of our method.

Photoacoustic image has recently emerged as a promising imaging modality for imaging prostate cancer.This paper made a qualitative analysis of photoacoustic signal generation according to the relationship between photoacoustic signal and the changes of light absorption energy. A 3D prostate optical model embedded tumors was established based on human prostate morphology through programming. The light energy distribution in the prostate with pulsed light was obtained by use of Monte Carlo method. The time-dependent spatial distribution of light absorption energy was obtained for photoacoustic signal generation at different positions. Comparison has been made between each other. Meanwhile, photoacoustic imaging experiment has been carried out. Our work might also be helpful for future simulation of photoacoustic imaging and investigation of detection sensitivity and imaging depth of photoacoustic imaging system.

Independent emission-spectral unmixing fluorescence resonance energy transfer, Iem-spFRET, is a novel and powerful tool for measuring FRET efficiency in real time. In this paper, we simulate the measurement error of the Iem-spFRET by introducing random noise in sample data, donor fingerprint, and acceptor fingerprint. The random noise intensity is set from 0.0005 to 0.0025, corresponding to 5% - 25% of the maximum donor fingerprint intensity. The simulated results show the effect of random noise on apparent FRET efficiency (E_fD) is less than on receptor-to-donor concentration ratio (R_c). Random noise with 10% maximum donor fingerprint intensity only leads to 0.33% variation of when the noise is added to both sample and fingerprints. These results indicate that Iem-spFRET is a robust method and could be applied on cases with weak FRET signal.

In recent years, bladder cancer has been a serious health concern around the world. As a rapidly growing imaging technique, photoacoustic imaging (PAI) was now being explored as an alternative for bladder imaging due to its non-invasive and non-ionizing nature. It was essential to know absorbed light distribution in bladder tissue which would influence the imaging depth and range of PAI. In the paper, optical model of human bladder was established, in which diffused light source was delivered through the urethra into the bladder cavity for endoscopic illumination. And Monte Carlo simulation method was adopted to calculate the light absorption distribution (LAD) in the bladder tissue. The shape and wavelength of light source were investigated in the simulation. The relevant conclusions would be significant for optimizing the light illumination in a PAI system for bladder cancer detection.

An adaptive equivalent focal length optimization algorithm is proposed. With a complete 4π stereo angle wide field of view, this kind of imaging distortion correction simulation system is established, and the multi-level effectiveness strategy is developed. And the simulation results based on this algorithm are given in many environments.

Light scattering caused by Tiffen ProMist photographic filters of various grades at different laser radiation 640 nm, 532 nm and 405 nm was objectively studied and compared. The lack of removal of speckles from the scene of laser radiation scattering did not allow an accurate assessment of the effect of scattering on the decrease in image contrast. Subjective deterioration in visual acuity using various contrast optotypes and contrast sensitivity was assessed by scattering induction using filters up to grade #5. Vision contrast sensitivity diminishes within all studied spatial frequency range 0.5-18 cpd. The degradation of the impact factor to visual acuity without scattering filters when the contrast of the optotype was reduced from 100% to 12.5% for optotypes with black-white letters was up to 30%, which was similar to the level of degradation (25% for a #5 degree filter) from scattering caused by filters modeling cataracts.

During the development of tumors, some protein molecules, secreted proteins, are secreted, which are closely related to the proliferation, invasion and metastasis of malignant tumor cells. Therefore, the study of tumor cell secreted proteins not only helps to understand the molecular mechanism of tumorigenesis and development, but also helps to find new tumor markers for early screening of cancer and monitoring of high-risk populations. Surface-enhanced Raman spectroscopy (SERS) and partial least squares-support vector machine (PLS-SVM) data processing methods were used to characterize secreted proteins from human liver cancer cells HepG2 and normal human liver cells LO2 cells in this paper. The discriminative sensitivity and specificity of secreted proteins reach 100%, respectively. These results show that SERS technology combined with PLS-SVM data processing method can effectively distinguish normal cells from cancer cells and provide new ideas for finding biomarkers of cancer cells.

Esophageal carcinoma is a common cancer worldwide with a high mortality. Early diagnosis and treatment is critical to reduce the mortality of esophageal cancer patients. In this work, we developed a novel method for detection of esophageal cancer by Raman spectroscopy measurements of extracellular fluid taken from esophageal tissue. The extracellular fluid samples were prepared by sliding the esophageal tissue over an aluminum plate substrate, and then the Raman spectra of the air-drying extracellular fluid samples from 10 esophageal cancer patients and 10 healthy volunteers were successfully recorded. Difference spectrum analysis combined with the assignment of Raman bands indicated that there were subtle but distinct changes between esophageal cancer and normal tissues, which could be associated with the special changes of nucleic acid, protein, lipid and other biological molecules during the process of canceration. To further investigate the diagnostic ability of extracellular fluid taken from human esophageal tissue, the spectral data was combined with multivariate analysis processes. Principal component analysis (PCA), as a spectral dimensionality reduction approach, and in conjunction with the linear discriminant analysis (LDA) algorithm, was employed to identify the esophageal cancer samples, and the diagnostic sensitivity and specificity of 90% and 80%, respectively, could be achieved for classification between normal and cancer groups. Moreover, receiver operating characteristic (ROC) curves further confirmed the effectiveness of the diagnostic algorithm based on PCA-LDA diagnostic algorithm. The results of this exploratory study demonstrated the great potential of esophageal cancer screening based on the analysis of extracellular fluid of tissue, and provided a rapid and label-free tool for clinical cancer detection.

In clinical, researchers have shown an increasing effort in low-dose PET (LdPET) which reduces the risk of radiotracer while maintaining an acceptable image quality and is challenging in practice. To address this issue, regularized model-based image reconstruction (MBIR) is widely applied and the convolutional neural network (CNN) has been demonstrated the efficiency of noise reduction. In this study, we proposed a deep Alternating Direction Method of Multipliers (ADMM) network with residual CNNs. Human brain data pairs of Poisson sampled sinogram and full-dose MLEM reconstructed image was used as the input and ground truth in training phase respectively.Results showed that ADMM-TV-Net outperformed the traditional EM reconstruction and existing algorithms for LdPET, such as nonlocal mean (NLM) and TV in terms of normalized mean square error (NMSE) and reconstruction speed.

Combining serum albumin via adsorption-exfoliation on hydroxyapatite particles (HAp) with surface-enhanced Raman scattering (SERS), we developed a novel quantitative analysis of albumin method from blood serum for breast cancer screening applications. For adults, the normal range of serum albumin is defined as 3.5-5.0 g/dL, and the levels <3.5 g/dL is called hypoalbuminemia. The quantitatively analysis obtained by our HAp method had a good linear relationship from 1 to 10 g/dL. More importantly, the lower limit of detection was less than the albumin prognostic factor for disease (3.5 g/dL). Serum albumin was adsorbed and exfoliated by HAp from serum samples of breast cancer patients and healthy volunteers, and then mixed with silver colloids to perform SERS spectral analysis. Subtle changes in the SERS spectra of serum proteins indicated that some specific biomolecular contents and albumin secondary structures change with cancer progression. Principal component analysis (PCA), as a spectral dimensionality reduction method, combining with a linear discriminant analysis (LDA) was employed to screen and classify breast cancer. Based on the PCA-LDA algorithm, yielding the diagnostic sensitivity and specificity of breast cancer patients were 95% and 90%, respectively. This exploratory work demonstrated that HAp adsorbed-exfoliated serum proteins combined with SERS spectroscopy has great potential for label-free and non-invasive screening of breast cancer.

A portable laser-induced fluorescence detector based on module design, which includes an excitation source of 405nm fibre-coupled stable spectra diode laser,a fluorescence collection module based on a fibre fluorescence optical fibre probe with an improved confocal optical arrangement,a fluorescence analysis module of Mini Fiber Grating Spectrometer. The advantages of the detector is compact, small size, low cost, high sensitivity, easy to operate.The performance of the detector is evaluated by fluorescein sodium. Water Raman peak S / N is 935.67. LOD of fluorescein sodium was 1.9×10-11g/L.Correlation of the fluorescence intensity was 0.9998 with the concentration from 10^-10 to 10^-12 g/L, the linear dynamic range was over 3 order. RSD of the fluorescence wavelength and intensity repeatability was 0.14% and 3.36% respectively. It is concluded that the 405nm LIFD has highly sensitivity,good repeatability,a wide linear range.

Mast cells (MCs) degranulation have an extremely momentous role in the progresses of immunoreaction, anaphylaxis as well as the variation of the tumor microenvironment (TME). The emergence of the substances due to MCs degranulation will arouse multiple changes of optical characteristics, such as energy transfer, fluorescence and spectra, etc. In this study, we implement the simultaneous spectral unmixing of excitation and emission by adjusting the cube filters and optical path to solely trigger the donor excitation and obtain the acceptor fluorescence emission. In addition, we add another channel to collect the real-time spectra with a portable and mobile spectroscopy equipment. Here, we developed graphene oxide (GO) and reduced GO (rGO)-based fluorescence resonance energy transfer (FRET) biosensors for MCs degranulation to verify the performance of the dual-channel system on an Inverted Fluorescence Microscope. MCs undergo degranulation can rapidly release tryptase, one proteases of the highest concentration in cytozoic pre-formed mediator. The acceptor fluorescence emission and spectra are detected simultaneously in real-time by tryptase-sensitized FRET biosensor on the dual-channel system. Moreover, the dual-channel can be switched by rapid adjusting optical channel during excitation at any moment. Results showed that the MCs degranulation could be monitoring in real-time on the dual-channel optical system. In particular, the minimal changes of the initial degranulation also could be measured with high response rate. Consequently, this dual-channel system may serve as a potential tool for the investigation of protein-protein interaction, single molecule dynamics and the working mechanism of membrane proteins using FRET-related techniques.

In the work, a set of photoacoustic detection system of glucose based on different influence factors was established. In the system, a kind of Nd: YAG pumped 532nm OPO pulsed laser was used as the excitation source, and a focusing ultrasonic transducer with central frequency of 9.5MHz was used to capture the photoacoustic signals of glucose. In the experiments, the time-resolved photoacoustic signals and peak-to-peak values of glucoses with different concentrations and other three factors (temperature, excitation energy, and flow velocity) were obtained by using the orthogonal experiment method. To precisely predict the photoacoustic peak-to-peak values and glucose concentrations, the back propagation neural network (BPNN) was used to build a nonlinear models between the multiple factors and the photoacoustic peak-to-peak values and glucose concentrations. In BPNN, 108 train samples and 36 test samples were used. To further improve the prediction accuracy, BPNN combined with intelligent optimization algorithms, i.e., genetic algorithm (GA) and particle swarm optimization (PSO) algorithms were used. At the same time, the parameters adjusting for all algorithms were performed. Results show that the prediction root-mean-square error (RMSE) values of peak-to-peak values and glucose concentration were all improved, the RMSE of peak-to-peak values improve 19.9%, and the RMSE of glucose concentrations improve 34.62%. Therefore, it is found that the BPNN combined with intelligent optimization algorithms has an efficient effect on the prediction of photoacoustic detection for glucose concentration.

To evaluate the development stage of skin cancer accurately is very important for prompt treatment and clinical prognosis. In this paper, we used the FLIM system based on time-correlated single-photon counting (TCSPC) to acquire fluorescence lifetime images of skin tissues. In the cases of full sample data, three kinds of sample set partitioning methods, including bootstrapping method, hold-out method and K-fold cross-validation method, were used to divide the samples into calibration set and prediction set, respectively. Then the binary classification models for skin cancer were established based on random forest (RF), K-nearest neighbor (KNN)，support vector machine (SVM) and linear discriminant analysis (LDA) respectively. The results showed that FLIM combining with appropriate machine learning algorithms can achieve early and advanced canceration classification of skin cancer, which could provide reference for the multi-classification, clinical staging and diagnosis of skin cancer.

Breast tumor microenvironment is composed of tumor cells, tumor-related cells, blood vessels and a series of extracellular matrix fibers. Tumor-infiltrating lymphocytes (Tils) in the microenvironment can directly or indirectly influence other components in the microenvironment, thus promoting the occurrence and development of tumors. Multiphoton microscopy (MPM) is based on two-photon excited fluorescence (TPEF) and second harmonic generation (SHG). And it does not require the use of exogenous probes or staining of tissue. In this study, large-size images with subcellular resolution of the breast tumor tissue was performed using MPM. The results showed that the MPM could clearly distinguish intraepithelial Tils (iTils) and stromal compartments Tils (sTils) by comparing the signal strength and morphological difference. It demonstrated that MPM could be used as a means of pathological diagnosis and in clinical application.

Macrophages and collagen fibers are important components of the tumor microenvironment. Macrophages would secrete extracellular matrix degrading enzymes to degrade collagen, which is conducive to the formation of local infiltration and distant metastasis of tumor cells. During tumor progression, macrophages are actively recruited into tumors where they alter the tumor microenvironment to accelerate tumor progression. A high density of these tumor-associated macrophages may correlates with poor prognosis. In this work, multiphoton microscopy (MPM) using two-photon excited fluorescence combined with second harmonic generation imaging was used to monitor the changes in collagen fibers around macrophages. The experimental results show that this microscope has the ability to directly monitor the collagen changes induced by the invasion of macrophages in the absence of labels. Moreover, collagen content around macrophages in the matrix can be quantitatively calculated by image processing, and quantitative results show that the collagen content in the tumor microenvironment will significantly reduce with the appearance of macrophages. Therefore, MPM has the potential to be used as a new auxiliary tool for pathologists to quickly and effectively evaluate collagen changes in breast tumor microenvironment.

Tumor photothermal therapy technology has received a lot of attention in recent years due to its non-invasive and targeted properties. However, how to ensure the safety and effectiveness of the photothermal treatment process poses new challenges to researchers. The field of photothermal therapy urgently needs a non-contact and accurate temperature detection method. In this paper, we have proposed a precise temperature detection technology based on photoacoustic and ultrasonic dual mode which can provide accurate and non-contact temperature measurement, and the temperature information of the light-induced ultrasound signals was fused and applied to temperature detection. To validate our method, temperatures of phantom was measured within the temperature range that simulates the heating process of photothermal therapy, and the calculated temperature measurement error was finally within 1 °C. In particular, it was also verified that the measurement accuracy of this method is 30% higher than that of single photoacoustic temperature detection. The results suggested that our method can be potentially used for temperature monitoring during photothermal therapy.

The presence of blood vessel invasion (BVI) in breast tumor microenvironment has been recognized as an unfavorable prognostic factor. Invasion of cancer cells into vessels is one of the critical steps for metastasis. Therefore, visualization of BVI is vital for comprehending the progress of tumor. Multiphoton microscopy (MPM) based on second harmonic generation (SHG) and two-photon excited fluorescence (TPEF) can monitor morphological changes in biological tissues. In this study, we found significant differences in morphology between normal breast blood vessel and abnormal blood vessel encountered with tumor invasion using label-free MPM. Our study demonstrated that MPM has the ability to not only identify BVI in breast tumor environment but also reveal the morphological changes of breast blood vessel. By comparing with the hematoxylin and eosin (HE) stained image, it was confirmed that MPM provides a new assistant tool for pathologists to identify BVI effectively. Keywords:

Diffuse Optical Tomography (DOT) is a promising non-invasive optical imaging technology that can provide structural and functional information of biological tissues. Since the diffused light undergoes multiple scattering in biological tissues, and the boundary measurements are limited, the reverse problem of DOT is ill-posed and ill-conditioned. In order to overcome these limitations, two types of neural networks, back-propagation neural network (BPNN) and stacked autoencoder (SAE) were applied in DOT image reconstruction, which use the internal optical properties distribution and the boundary measurement of biological tissues as the input and output data sets respectively to adjust the neural network parameters, and directly establish a nonlinear mapping of the input and output. To verify the effectiveness of the methods, a series of numerical simulation experiments were conducted, and the experimental results were quantitatively assessed, which demonstrated that both methods can accurately predict the position and size of the inclusion, especially in the case of higher absorption contrast. As a whole, SAE can get better reconstructed image results than BPNN and the training time was only a quarter of BPNN.

Ultrasound-modulated optical tomography (UOT) combines optical and acoustic techniques, and has high spatial resolution of ultrasonic location and high sensitivity of optical detection. In this technique, a focused ultrasound is used to locate and label the scattered light. It determines the spatial resolution of UOT and the modulation efficiency of the scattering light. Four kinds of acousto-optic signals modulated by 1, 2.25, 5, and 10 MHz center frequencies of impulse ultrasound are obtained in this letter. The frequency spectrum of these four kinds of acousto-optic signals are achieved by Fast Fourier Transform (FFT). By analyzing the spectrum information of acousto-optic signals modulated by ultrasound at different frequencies, we can find useful feature information and choose an appropriate parameter of ultrasonic probe to improve the signal-to-noise ratio and sensitivity of UOT.

Fluorescence pharmacokinetics can analyze the absorption, distribution, metabolism and other pharmacokinetic processes of fluorescence agents in biological tissues over time, which can provide more specific and quantitative physiological and pathological information for the evaluation of organ function. This paper is devoted to studying pharmacokinetics of indocyanine green (ICG) in healthy mice and mice with acute alcoholic liver injury based on a home-made dynamic diffuse fluorescence tomography system that possesses high sensitivity and large dynamic measurement range on account of digital lock-in-photon-counting technique. In this study, four-week-old Kunming mice were randomly divided into experimental and control groups. The time-varying distribution of ICG in mice was obtained by diffuse fluorescence tomography reconstruction, and the pharmacokinetic parameters were further extracted from the ICG concentration-time curve. The results showed that the dynamic diffuse fluorescence tomography system successfully captured the ICG metabolism process in mouse liver, and the ICG excretion rate demonstrated an obvious difference between healthy mice and the mice with acute alcoholic liver injury.

The study of submicron particles becomes increasing important in biomedical field. For example, some biological substances such as extra-cellular vesicle have been reported to play important roles in understanding of cancer. Twodimensional (2D) light scattering technology has been previously applied for the analysis of micro-size cells or particles. In this paper, we develop the 2D light scattering technology for the analysis of submicron particles. Light sheet technology is adopted to provide a uniform and high intensity excitation. Two-dimensional light scattering patterns of polystyrene beads (250 nm and 510 nm in diameter) are imaged by a complementary metal oxide semiconductor (CMOS) sensor through a 40X objective lens. The experimental results are found to be similar with our 2D light scattering simulations based on Mie theory. Our results show that the submicron particles can be well detected by 2D light scattering technology, which is expected to have future applications in the field of biomedicine.

Serum proteins contain many biomarkers of diseases, such as cancer. It would be important to purify the serum proteins for disease diagnosis. In this paper, cellulose acetate (CA) membrane was employed to purify serum proteins from human serum while removing other serum constituents and exogenous substances. The purified serum proteins were mixed with silver nanoparticles for SERS measurement. A total of 42 SERS spectra were recorded from purified serum proteins obtained from 20 liver cancer patients and 22 healthy volunteers. Subtle but discernible spectral changes of the two groups could be observed in the SERS spectra. Principal components analysis (PCA) and linear discriminate analysis (LDA) algorithm were introduced to analyze the difference between the two groups. Additionally, this method is nondestructive, fast and easy to operate, which is greatly important for clinical serum sample detection. These results indicated that SERS signal of serum proteins purified with CA membrane has a good prospect in liver cancer screening.

Photothermal therapy of tumors has become an important method. In recent years, the method has been widely studied in tumor therapy, and the corresponding results has been obtained well. However, it is still not been solved the effects of the heat on the tumor and its surrounding tissues, and the temperature control in corresponding tissues during the treatment process. In our study, the mouse skin was chosen as the research object. Infrared thermal imager and optical coherence tomography (OCT) were combined to monitor the photothermal therapy in real time in vivo. Temperature and morphological structure were obtained during the photothermal therapy process. The results will provide effective guidance for the photothermal therapy of tissue.

Photoacoustic (PA) tomography (PACT) ’s capability is evidently influenced by the availability of imaging probes such as the genetically encoded proteins with near-infrared optical absorption (NIR-GEP). We present a new PACT screening platform specially designed for high throughput imaging and quantification of PA signal strengths from randomly mutated NIR-GEP candidates expressed in Escherichia coli colonies.The new platform holds promise to facilitate research on the next generation NIR PA imaging probes.

Two-dimensional (2D) light scattering has the capability for label-free single cell analysis. Recent development of flow cytometry has demonstrated the obtaining of high-content images. Here we demonstrate a flow cytometer for the obtaining of high-content 2D light scattering patterns of single cells. In our flow cytometer, single cells are flowing in a hydrodynamic focusing unit and their 2D light scattering patterns are recorded via a long working distance objective by using a high-speed complementary metal oxide semiconductor (CMOS) sensor. Big data of the 2D light scattering patterns from two types of cervical carcinoma cell lineage cells (HeLa and C33-A) are obtained with a rate of 60 frames per second. Deep learning is adopted for the classification of these two types of cells, and a high recognition accuracy is obtained. The results show that our high-content 2D light scattering flow cytometry together with deep learning can collect label-free single-cell information at high speed and has strong analytical capabilities, which may in future be used for early diagnosis of cervical carcinoma.

Photoacoustic tomography (PAT) is a fast-evolving biomedical imaging modality in recent years, which has unique applications in a range of biomedical fields. In PAT, image reconstruction is a critical step to produce high-quality optical absorption images from photoacoustic projections. To date, algorithms based on back projection are the most widely used image reconstruction techniques due to their simplicity and computational efficiency. However, images reconstructed by back projection contain negative intensities, which have no physical meanings and are essentially undesired artifacts. Here we study the formation mechanism, fundamental causes of the negativity artifacts in backprojection based PAT. Results show that limited detector bandwidth and limited view angle are two fundamental causes of the negativity artifacts. When the bandwidth of the detector is limited, back-projection signals will be distorted due to the loss of frequency contents and negativity artifacts thus occur. When the view angle of the detector is limited, photoacoustic signals propagating in three-dimensional space cannot be captured completely, resulting in negativity artifacts. This work provides a comprehensive understanding of the characteristics of negativity artifacts, which may promote the development of artifact-free image reconstruction algorithms.

Photoacoustic imaging (PAI) has unique structural and functional imaging capabilities and has attracted widespread attention in clinical diagnosis. However, in the case of fast or real-time imaging, the reconstruction of sparse-view sampling data of photoacoustic data is still a challenge. In this paper, we present our study on simultaneous algebraic photoacoustic reconstruction technique based on total variation. The proposed algorithm constructs an accurate projection matrix based on the detection sensitivity of the array element. Combining simultaneous algebraic reconstruction technique (SART) and total variation (TV) to optimize sparse-view sampling photoacoustic image reconstruction results. Numerical simulation experiment results show that the algorithm reconstructs high-quality photoacoustic images from sparse-view sampling data, effectively eliminates under-sampling artifacts, and preserves edge details. Compared with traditional algorithms, this algorithm may be a practical and effective algorithm for sparse-view PAI reconstruction.

DNA fragments in circulation released from apoptotic and necrotic cells were regarded as a novel prognostic or predictive biomarker for clinical diagnosis in recent years. However, DNA concentration in plasma ranged between 1 and 10 ng ml^-1, which needed a single-molecule technology to analyze the base pair and concentration of DNA fragments. In this study, a series of different lengths of DNA fragments were studied, which showed that a good linear relationship between the DNA concentration and the molar concentration. The results suggested fluorescence correlation spectroscopy could access the nanomolar concentration of DNA labelled by SYBR Green I. Moreover, the relationship between the length of DNA fragment and the diffusion coefficient of DNA was scaled with the standard samples. The results demonstrates fluorescence correlation spectroscopy is a highly sensitive method for DNA detection.

Breast diseases with many distinct histopathological types are showing a rising trend in incidence for decades worldwide. The proliferation of cells and the remodeling of collagen fibers in breast carcinoma tissues may be used to predict breast disease diagnosis, prognosis of treatment, and patient survival. Pathologists can label related typical pathological features as cell nuclei, aligned collagen, and disorganized collagen in hematoxylin and eosin (HE) sections of breast tissues. In this study, we apply the Mueller matrix microscopic imaging to various breast pathological section samples, and calculate corresponding polarimetry basis parameters (PBPs). A pixel-based extraction approach of polarimetry feature parameters (PFPs) is proposed using a mutual information (MI) method and a linear discriminant analysis (LDA) classifier. The three PFPs derived by the proposed learning algorithm are the simplified linear combinations of PBPs with physical meanings, and provide quantitative characterization of the three pathological features in different breast tissues respectively. We present results of the three PFPs of tissue samples from a cohort of 32 clinical patients diagnosed as normal, breast fibroma, breast ductal carcinoma in situ, invasive ductal carcinoma, and breast mucinous carcinoma with analysis of 210 regions-of-interest (ROI). The results demonstrate that the three PFPs of each breast disease tissue have specific value ranges, which has a potential to quantitatively distinguish typical pathological features between different breast tissues. This technique has good prospects for automation of the microstructure identification and prediction of breast disease diagnosis, resulting in the reduction of pathologists’ workload.

Wide-field fluorescence microscopy (WFFM) is widely adopted in biomedical studies. However, axial resolution in most WFFM is poor due to the absence of optical-sectioning capability. To achieve wide-field optical-sectioning, several methods have been proposed, most of which need at least two images to reconstruct one optical sectioning image. So, the frame rate of current wide-field optical sectioning microscopy is no more than half of that of conventional WFFM, which may not meet the speed requirement of fast biodynamic studies. We introduce a novel high-speed, wide-field optical sectioning method based on local contrast weighting function and two-dimensional Hilbert-Huang transform, in which only one structured image is required to reconstruct an optical sectioning image. In this way, the loss of temporal resolution in conventional wide-field optical sectioning microscopy is compensated. We validated this method with the imaging of mouse brain slices.

Skin cancers including carcinomas and melanomas are currently the most common cancers in fair skinned humans. Histopathology, requiring invasive tissue biopsy and processing, is the gold standard for cancer diagnosis. Optical Coherence Tomography (OCT) has emerged as a label-free, non-traumatic and non-invasive method that can be used in vivo to image skin tissues (from stratum corneum to dermis) and therefore contribute to skin cancer diagnosis. Some of the major limitations of OCT imaging techniques are a lower resolution compared to histology and a limited penetration depth due to skin tissues’ strong optical scattering. Optical clearing has been investigated for several years as one of the solutions to overcome these problems by inducing a reversible decreased scattering and thus allowing a better contrast and an improved light penetration depth within biological tissues. Clearing is achieved using optical clearing agents (OCA) combined with chemical enhancers (used to better pass through the stratum corneum layer). As a first step, the current study aims at defining the quantitative features (intensity profile, image statistics, texture descriptors) that are best suited to quantify optical clarification kinetics from images acquired using Linefield Confocal-OCT (LC-OCT) device. This will help analyzing the relationship between visible optical clearing and OCT devices resolution.

Optical properties of biological tissue are key parameters of optical imaging, and also provide useful information of the tissues under different physiological conditions. The optical attenuation coefficient (OAC) related to the optical properties can be calculated from optical coherence tomography(OCT) data. OCT has the advantages of high-resolution and fast imaging speed, and can image the tissues in vivo and in real-time. Due to the lack of blood perfusion, renal ischemia is accompanied by changes in microstructure of the kidney, which the OAC is sensitive to. We applied OCT to detect the OAC variation during ischemia-reperfusion process of rabbit kidney, and further estimated the ischemiareperfusion (I/R) injury. In order to study the temporal relationship between I/R injury and ischemia, 12 New Zealand rabbits were divided averagely into 4 groups (ischemia 30/60/90/120 min group). The kidneys were observed in vivo using a spectral domain OCT (SD-OCT) which light source centered at 900 nm. Three-dimensional OCT images of the kidney were obtained before the occlusion of renal artery and several time points after the blood reperfusion. The OAC were obtained by exponential fitting of OCT A-lines. Mapping attenuation coefficient (MAC) of each 3D OCT data set was performed to get the attenuation coefficient distribution in the kidney. The OAC curve with reperfusion time showed that the OAC was sensitive to ischemia and helpful for the estimation of ischemia-reperfusion injury. The distribution of attenuation coefficient in MAC image could reflect the local status of kidney.

Optical coherence tomography (OCT) is a useful non-invasive optical tool for imaging various biological tissues. As OCT imaging is based on interferometry, speckle noises are inherent and can degrade the quality of OCT image. The objective of this study was to evaluate the effectiveness of conventional denoising algorithms for OCT image denoising and for improving image quality. OCT images of human skin were obtained from a swept source OCT of 1300 nm. Three image denoising algorithms, including median filtering, mean filtering and Gaussian bilateral filtering, were applied for denoising OCT images of different quality. Five quality evaluation criteria, including signal to noise ratio (SNR), equivalent number of looks (ENL), contrast-to-noise ratio (CNR), cross correlation (XCOR), and peak signal to noise ratio (PSNR) were used for comparing the effectiveness of each denoising process. In terms of improving local contrast, three denoising algorithms showed similar effect. In terms of the equivalent views, Gaussian bilateral filtering algorithm showed the most significant increase and therefore caused certain degrees of blurry. For signal to noise ratio, all three denoising algorithms showed improvement while Gaussian bilateral filtering algorithm had better protection effect of the effective information and edge of the original image. Gaussian bilateral filtering algorithm provides better denoising outcomes for OCT image processing.

Skin cancer is one of the most common cancers. Most skin cancers are not life threatening, but malignant melanoma is fatal. Currently, it still remains a challenge to discriminate malignant melanoma from benign melanoma by using conventional diagnostic techniques, such as ultrasonography, computed tomography, magnetic resonance imaging and positron emission tomography. As a new type of bio-optical imaging technology, hyperspectral imaging (HSI) has become the focus of research. It can provide information about hemoglobin and melanin content for the differentiation of various skin diseases. In this study, we propose a hyperspectral imaging system based on push-broom imaging spectrometer to image skin-pigmented nevus, and then segment the nevus out from surrounding normal skin through pixel-wised spectrum classification with deep learning techniques. The HIS system can produce hyperspectral image over the spectral range of 465-630nm and with a spectral resolution of 2.1 nm. Meanwhile, we evaluated the performance of K-means, Gaussian Mixture Model (GMM) and Hierarchical Clustering (HAC) in detecting the nevus with manual segmentation as the gold standard. The results show that these three techniques all have a good accuracy in differentiating the nevus from normal skin, which proves that the hyperspectral system combined with classification techniques has a good potential to detect the pigmented nevus on the skin.

Melanoma is one of the most serious skin cancers in the world. As circulating tumor cells have been proved to be an important marker of early metastasis of cancer, the detection of circulating tumor cells of melanoma is of great significance for early diagnosis and the monitoring of tumor progression. In vivo photoacoustic flow cytometry (PAFC) is constructed to achieve real-time and non-invasive detection of circulating melanoma cells in vivo. However, as the photoacoustic signals acquired in the detection process are disturbed by various kinds of noise, it is difficult to accurately distinguish the photoacoustic signals of background and circulating tumor cells by the traditional triple mean square deviation method. Therefore, a photoacoustic signal classification method is proposed based on convolutional neural network, which can greatly improve the accuracy of detection. Features of signals are extracted by the convolutional neural network to distinguish photoacoustic signals of melanoma cells and background. We construct a convolutional neural network based on one-dimensional input signals. For training the classifier, a large number of samples are selected. The accuracy rate in the test set can reach 95%. Besides, a neural network is built based on VGG16 model and transfer learning, and the trained classifier can realize the accuracy of 98% in the test set. Experiments show that the method of photoacoustic signal classification based on convolutional neural network greatly improves the accuracy of signal classification, and realizes the rapid and accurate analysis of a large number of data.

Two-dimensional (2D) light scattering microscopy is one of the label-free imaging technologies that allows to image the particles out of focal plane. In this manuscript, we try to develop a volume scanning-based 2D light scattering microscopy that aims to fast determine the 3D location of single particles by taking the advantage of defocusing strategy, which is expected to improve the efficiency of common refocusing-based localization methods. To demonstrate the principle of our volume scanning-based 2D light scattering microscopy, the standard beads are used to perform the experiments. The vertical position of individual beads can be determined by measuring the area of the scattering patterns. The volume scanning-based 2D light scattering microscopy is proposed to provide a fast and label-free method for 3D localization of single particles.

Evaluation of the concentration of glycation end products (AGE) in the human body utilizing autofluorescence is widely used in medical practice. The autofluorescence level is usually measured from the skin of the hand. However, this measurement can hardly be used for some of the human skin phototypes. On the other hand, the sclera of the eye also consists of collagen fibers that can be affected by glycation. Sclera autofluorescence can also be investigated in vivo by "hand-held" fluorescence meter. In this case, there are no effects of the skin phototype and no effect of pressure made by the hand on the fluorescence meter. Therefore, the study of correlation between the fluorescence of the skin of the hand and the sclera fluorescence is in the area of practical interest. In this research the results of the simultaneous measurements of skin and sclera autofluorescence made on 34 humans are presented. The "hand-held" fluorescence meter and an upgraded slit lamp were used as instruments. The value of the Pearson correlation coefficient was 0.89, which can be considered as successful validation of using fluorescence for assessment of the AGE content in the sclera.

The diagnostic potential of advanced glycation end products (AGE) estimation by its autofluorescence is restricted by not sufficient specificity and high level of variability even in case of the same individual. One of the reasons for this restriction is melanin influence on the signal measured by diagnostic fluorescence meter, as melanin is a strong endogenous chromophore. In addition, the AGE autofluorescence is excited by ultraviolet or violet radiation, the penetration depth of which into the skin is small. In order to decrease such influence, the green LED with a peak wavelength of 530 nm was implemented. The ratio of the fluorescent radiation intensity to the product of the two intensities of the elastically scattered skin radiation was used. One of them was scattering of ultraviolet LED light with a peak wavelength of 365 nm that excited autofluorescence, the other was green LED light. This approach results in two times lower melanin influence on AGE autofluorescence.