This PDF file contains the front matter associated with SPIE Proceedings Volume 13503, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

One of the issues impeding the application of chimeric antigen receptor T-cell (CAR-T) therapy is that some patients have T cell dysfunction, making treatment ineffective. To objectively screen these patients, we developed 3D fluorescence image analysis algorithm for quantifying cytotoxic behavior of CAR-T cells. Indicators include the area of immune synapses (IS), the actin depletion coefficient of IS, the actin migration rate, and the centrosome polarization angle. Our results demonstrate that these indicators are capable of differentiate CAR-T cell function.

This paper presents a microsphere-assisted ultramicroscopic objective comprised of a dielectric microsphere lens, a polymer plano-convex lens, and an objective lens with an adjustable distance between the microsphere and the objective lens. The dielectric microsphere lens and the polymer plano-convex lens are combined on a soda-lime glass substrate, which is then bonded to the objective lens using a 3D printed adapter. To prevent contamination and damage to the sample, when using a liquid immersion objective, the immersion liquid is placed on the opposite side of the soda-lime glass substrate from the microspheres. With this ultramicroscopic objective, imaging of grating structures on commercial Bluray discs (spacing: 100 nm) and pore structures on anodized aluminum oxide (spacing: 120 nm) was successfully achieved.

At present, there have been many studies using OCT technology to assist subretinal injection surgery. One of the research directions is to perceive the depth of needle penetration by collecting A-Line signal of tissue in real time. Most of the OCT systems in such solutions use a common optical path architecture, which basically does not require dispersion compensation, and the structure is simple, but the working distance is fixed. In this paper, a needle tip depth sensing SDOCT system was developed. Two types of ball lens fiber probes emitting quasi-parallel light were designed for use in air and water. The ball lens fiber probe used in water was fabricated. A probe, called a single-mode fiber probe, was manufactured by cutting the light emitting surface of a single-mode fiber using a fiber cleaver. Under the same conditions, in the water, the A-Line signal of the mirror collected by the SD-OCT system integrated with the ball lens fiber probe used in the water is stronger than that collected by the SD-OCT system integrated with the single-mode fiber probe. When collecting the A-Line signal of the mirror in water, the SD-OCT system integrated with the ball lens fiber probe used in the water has a longer working distance than the CP-SDOCT system integrated with the single-mode fiber probe. Finally, the needle tip depth sensing function of the system was tested through experiments.

Fluorescence diffuse optical tomography (FDOT) is an emerging optical imaging tool for in-vivo observation of organisms and small-animal. This modality retrieves the distribution of the concentration of the fluorescent biomarkers noninvasively and quantitatively, which is related to the interior cellular and molecular events. After the FDOT system is established, its performance parameters should be comprehensively and systematically evaluated. This paper conducts an experimental study on the parameters such as spatial resolution and sensitivity of our FDOT system. The fluorescent reagent used in the experiment was indocyanine green, and the phantom material was polyformaldehyde.

Cavity-enhanced absorption spectroscopy (CEAS) has become increasingly important in gas detection for air pollutants, human breath gases analysis, and monitoring industrial processes. However, the accuracy and sensitivity of CEAS are limited by noise and function optimization algorithms. To solve this problem, An improved particle swarm optimization (PSO) algorithm for CEAS was developed in this paper. The sensitivity of CEAS can be improved by combining various noise reduction filtering algorithms with PSO. Compared with traditional nonlinear fitting algorithms, the absorption coefficient can be achieved to 1.06×10^-8 cm^-1 , with a minimum detectable limit of 5.31ppm, which is improved by 2.2 times. This research contributes to enhancing the precision of trace gas detection.

With the rapid development in advanced imaging techniques, massive image data have been acquired for various biomedical applications, posing significant challenges to their efficient storage, transmission, and sharing. Classical model- or learning-based compression algorithms are optimized for specific dimensional data and neglect the semantic redundancy in multidimensional biomedical data, resulting limited compression performance. Here, we propose a Semantic redundancy based Implicit Neural Compression guided with Saliency map (SINCS) approach which achieves high quality compression of various types of multi-dimensional biomedical images. Based on the first-proved semantic redundancy of biomedical data in the implicit neural function domain, we accomplished saliency-guided implicit neural compression, thereby notably improving the compression efficiency for large-scale image data in arbitrary dimensions. We have demonstrated that SINCS surpasses the alternative compression approaches in terms of image quality, compression ratio, and structure fidelity. Moreover, with using weight transfer and residual entropy coding strategies, SINCS improves compression speed while maintaining high-quality compression. It yields compression with high compression ratio on biomedical images of diverse targets, and ensures reliable downstream tasks, such as object segmentation and quantitative analyses.

The human eye wavefront aberrator based on the Shack-Hartmann wavefront sensor (SHWFS) has become a common device for detecting eye aberrations in modern ophthalmology clinics. In order to eliminate the problem of spot and subaperture matching in traditional methods, we use deep learning method to directly map Hartmann spot pattern and corresponding Zernike coefficient, so as to expand the dynamic range of measurement. The lightweight network realizes to fully extract high dimensional feature information and achieves high precision measurement of diopter and astigmatism. The experimental results show that the proportion of the network falling into the tolerant error range (±0.25D) in diopter and astigmatism measurement reaches 94.2% and 100%. This method can measure the low order aberrations of human eyes effectively without changing the SHWFS setting, and at the same time ensure the accuracy and dynamic range, which has been verified by the real machine.

Color difference threshold is an important parameter to evaluate human color vision characteristic, which is of great significance for early diagnosis and inspection of ophthalmic disease. The test of color vision characteristics has experienced the process from qualitative to semi-quantitative to quantitative. Based on the analysis of existing color vision test methods, a new method is proposed by using three-integrating sphere system and specially made color modulators with optical modulation principle, which can generate required test targets of different luminance and color. Then an automated measurement system is established which can realize the color difference threshold measurement quantitatively. The output parameters can be precisely adjusted and achieved by real-time control, and be traceable to the existing luminance and color standards with metrological control. By metrological evaluation, the color coordinate repeatability is 0.0003 and reproducibility is 0.004 for color modulation, and the color difference repeatability is within 0.30 and reproducibility is not over 1.0 for color vision test targets generated. Experiments show that the measurement system can generate required color vision test targets and realize the color visual threshold measurement well. With the advantage of large color modulation range and adjustable test target, the system can meet test needs of personalized human color vision in many related scientific research fields.

The healthiness of the retinal vascular network plays an essential role in maintaining healthy vision. Numerous efforts have been devoted to developing accessible high-resolution retinal vascular network mapping instruments for ocular disease diagnosis and systematic disorder screening. Although optical coherence tomography (OCT) is the current standard-of-care high-resolution noninvasive retinal imaging technique, it only provides 3D structural retinal images. To enable the noninvasive visualization and assessment of the retinal vascular network, OCT angiography (OCTA) was invented to analyze differences within repeatedly scanned cross-sectional areas to form volumetric retina blood flow maps. With these unprecedented advantages, OCTA has been quickly employed by ophthalmic clinics. Nevertheless, the inconsistencies in current imaging protocols, data analysis metrics, and clinical practices result in difficulties in OCTA function evaluation during product registration, periodic calibration, and inspection. A retinal vascular phantom could facilitate the OCTA product performance evaluation to promote the standardization of OCTA instruments. In this work, we designed and fabricated a retinal multivascular phantom with an eleven-layer structure and a diseased multivascular network with microaneurysms and vein occlusions to evaluate OCTA instrument performance. To mimic the blood flow in the retina, an intralipid solution was injected into the vascular network phantom with an infusion pump. OCT and OCTA images of the retina phantom were obtained with a commercial OCTA system. Critical retinal vascular network parameters of the phantom were also examined to validate its quantitative evaluation function. Results prove that the diseased multivascular network retinal phantom could provide an assessment of the OCTA function.

Light-sheet fluorescence microscopy (LSFM) is an advanced three-dimensional microscopic imaging technique, possessing unique imaging advantages for samples of various scales. One the one hand, for mesoscale samples, its tomographic sheet-like illumination allows the acquisition of information from a whole plane at a time, greatly increasing the imaging throughput (the amount of information obtained per unit time). On the other hand, for nanoscale samples, its three-dimensional resolution and photobleaching performance are both superior to those of traditional widefield illumination optical microscopes. However, traditional LSFM's dual-objective setup limits mounting options and speed. Single-objective LSFM (OPM) addresses this, but still has drawbacks like limited field of view and need for additional objectives. Addressing these limitations is crucial for LSFM's full potential in biomedical research. Herein, we propose double-beam interference Microscopy, a novel single-objective LSFM design which could realize horizontal plane illumination using two oblique laser sheets, compatible with various sample mounts. It eliminates remote correction, enabling imaging from organelles to mouse brains by switching objectives. Its sharp optical sectioning and full-aperture detection offer superior spatiotemporal resolution. We showcase its versatility in neuroanatomy and immunology imaging, enabling coarse-to-fine imaging, automatic coordinate mapping, and anatomical annotation correlation.

Liver cancer is a serious threat to people all over the world. Surgical resection is the preferred treatment for long-term survival. The residual malignant tissue will cause frequent recurrence of the disease and lead to high mortality. Therefore, the accurate determination of tumor resection margin is very important for surgical treatment. On the one hand, the ultimate goal of surgical resection is to remove all liver cancer tissue as much as possible to avoid residual; On the other hand, it is necessary to preserve as much healthy tissue as possible and maintain the basic functions of the organs. Hyperspectral imaging technology can quickly and accurately capture the spatial and spectral information of liver tumor tissue, and realize the accurate division of liver tumor incisal margin during surgery. The two goals mentioned above essentially correspond to the sensitivity and specificity in performance evaluation. In this work, we used the idea of contrastive learning to improve the classical U-net backbone network and optimize the overall performance of the liver tumor classification network. Specifically, a dataset containing 36 specimens was collected from 19 patients and pathological results were used as ground truth. The mean value of the improved network classification results increased and the standard deviation decreased in terms of accuracy, sensitivity and specificity , especially the sensitivity and specificity were significantly improved, with the overall sensitivity reaching 96.61% and the specificity reaching 95.00%, which met the original intention of surgical resection, and was of great significance for the practical application.