This PDF file contains the front matter associated with SPIE Proceedings Volume 12834, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Zebrafish are a widely used developmental model because of their transparent embryos and external development. These distinctive characteristics provide valuable insights into embryonic development. Optical coherence tomography (OCT) offers label-free structural imaging and has emerged as a preferred tool for embryonic imaging. On the other hand, light sheet fluorescence microscopy (LSFM) enables time-lapse molecular imaging of multi-hour to multi-day developmental processes due to its low phototoxicity and photobleaching compared to traditional confocal fluorescence microscopy. We developed a multimodal imaging system to obtain concurrent structural and molecular information by combining OCT and LSFM for embryonic imaging. A Michelson-type swept-source OCT system with a central wavelength of 1050 nm, the bandwidth of 100 nm, and sweep rate of 100 kHz captured the structural information with a lateral resolution of ~15 μm and an axial resolution of ~7 μm. The LSFM system captured the molecular information with a transverse resolution of ~2.1 μm and an axial resolution of ~13 μm. The optically co-aligned OCT and LSFM beams were scanned through the same scan head for trivial co-registration of the multimodal images. We imaged 1-5 μm green fluorescence microbeads to show the capability of this system. We then conducted imaging of zebrafish vasculature development with a transgenic line, Tg(kdrl:EGFP), where the erythroblasts express GFP. The results show that the multimodal system enables us to provide co-registered zebrafish structural and functional imaging.

We hypothesize that strain of the synovial membrane of joints and surrounding tissues due to altered force ratios can be optically detected at cellular level in vivo to infer acting forces. This is crucial because immune cells adapt their function to disbalanced mechanical load. Time series of metacarpophalangeal joints in mouse paws are imaged using multimodal multiphoton microscopy. A three-dimensional method based on the Gaussian-Laplacian pyramid and an optical flow algorithm is used to determine morphological shift between consecutive time points, effectively overcoming image processing challenges. From this, the force field in bulk tissue is approximated based on Hooke's law.

Zebrafish is a well-established animal model for developmental and disease studies. Its optical transparency at early developmental stages is ideal for tissue visualization. Interaction of light with zebrafish tissues provides information on their structure and properties. In this study, we developed a microscopic imaging system for improving the visualization of unstained zebrafish tissues on tissue slides, with two different setups: polarized light imaging and polarized hyperspectral imaging. Based on the polarized light imaging setup, we collected the RGB images of Stokes vector parameters (S0, S1, S2, and S3), and calculated the Stokes vector derived parameters: the degree of polarization (DOP), the degree of linear polarization (DOLP)). We also calculated Stokes vector data based on the polarized hyperspectral imaging setup. The preliminary results demonstrate that Stokes vector data in two imaging setups (polarized light imaging and polarized hyperspectral imaging) are capable of improving the visualization of different types of zebrafish tissues (brain, muscle, skin cells, blood vessels, and yolk). Using the images collected from larval zebrafish samples by polarized light imaging, we found that DOP and DOLP could show clearer structural information of the brain and of skin cells, muscle and blood vessels in the tail. Furthermore, DOP and DOLP parameters derived from images collected by polarized hyperspectral imaging could show clearer structural information of skin cells developing around yolk as well as the surrounding blood vessel network. In addition, polarized hyperspectral imaging could provide complementary spectral information to the spatial information on Stokes vector data of zebrafish tissues. The polarized light imaging & polarized hyperspectral imaging systems provide a better insight into the microstructures of zebrafish tissues.

Imaging Cherenkov photons emitted during radiation therapy can provide real-time information of the external beam field. It is well established that Cherenkov emission is correlative to dose deposited; however, differential photon energies and tissue attenuation properties, along with complicated camera geometries, entangle this relationship and introduce variability in the Cherenkov emission-to-dose ratio from patient-to-patient. This study aims to better understand the effects of optical properties, skin color, and patient-specific geometries (i.e. angle of camera incidence and curvature) on the Cherenkov emission-to-dose relationship. To do so, a series of phantom experiments were conducted with tissuesimulating optical phantoms and an andromorphic breast phantom in which optical properties, curvature, and camera angle of incidence were all examined as a function of normalized Cherenkov emission-to-dose. To acquire clinical Cherenkov data along with patient skin color, Cherenkov images and OSLD measurements for the ground-truth surface dose were collected weekly on 13 whole-breast radiotherapy patients, alongside high-resolution 3D color and texture scans. Phantom results suggest there to be a moderately strong correlation between dose percent error and patient curvature (R² = 0.57), as well as angle of camera incidence (R² = 0.56). Initial patient results suggest there to be a correlation between the redness of a patient’s skin, and the Cherenkov emission-to-dose ratio, with higher amounts of redness correlating to lower Cherenkov signal. By better characterizing these trends, we are potentially able to find generalizable optics-based corrections that improve the accuracy in mapping Cherenkov emission to real-time skin dose.

Photothermal therapy (PTT) is a minimally invasive tumor destruction method that avoids surgery, chemotherapy, and radiation by eradicating diseased tissue using near infrared laser light delivered through interstitial optical fibers. As such, it represents an ideal solid tumor treatment without the high personal and institutional costs of conventional therapy. Deep tissue PTT response monitoring and guidance is currently performed using MRI thermometry, aiming for complete tumor destruction while limiting unwanted collateral damage to critical tissues. However, MRI thermometry suffers from slow imaging rates, motion induced errors and limited resolution. Further, it is resource intensive, limiting its use to large health care centers. We previously reported on a new frequency optimized Photoacoustic (PA) thermal imaging capability with unprecedented bulk tissue temperature sensitivity and field of view, ideal for clinical PTT guidance. A major remaining challenge for quantitative PA thermal guidance is the dynamically changing tissue environment during PTT, including hemoglobin oxygenation, edema, and protein denaturation, all of which contribute to PA thermometry errors. In this work, we explore Diffuse Optical Tomography (DOT) measurements and PAI with Deep Learning and conventional image reconstruction frameworks that factor in tissue dynamics, absolute measures of tissue optical properties and an accurate estimate of the PAI laser fluence, all factors needed for quantitative PAI imaging. With the known high sensitivity of DOT measurements to bulk tissue changes, particularly thermally induced coagulation, it is expected that our dual modality PTT guidance platform will provide affordable, accurate thermal image guidance during PTT.

Atrial fibrillation is a global epidemic linked to millions of deaths each year. One increasingly relevant treatment for the disease is catheter ablation. In this procedure, an electrophysiologist burns lesions to isolate pathogenic tissue in the pulmonary veins from initiating an ectopic heartbeat. Long term efficacy of the procedure still needs to improve. Current intraprocedural feedback does not allow the clinician to properly visualize individual lesions. We developed an integrated polarization sensitive optical coherence tomography (PSOCT) and near infrared spectroscopy (NIRS) catheter to measure lesions during an ablation procedure. By combining both modalities, we overcome their individual limitations and provide complementary metrics. Using the PSOCT-NIRS catheter to analyze lesions, we show that we can mitigate the imaging depth limitations of PSOCT and inform spectral measurements made by NIRS to provide a more informative view of lesion quality.

The incidence of skin cancer, including melanoma, has been steadily increasing over the past decades. Early-stage melanoma often exhibits minimal symptoms, making it challenging to detect. However, when it progresses to later stages and spreads to the lymph nodes, the chances of survival significantly decrease. The current diagnostic gold standard involves invasive and time-consuming procedures, such as visual examination, excision, and histological examination of tissue samples. As an alternative, we developed a new multimodal optical system that addresses these challenges by integrating ultrasound (US), photoacoustic tomography (PAT), optical coherence tomography (OCT) and Raman spectroscopy (RS) into a single measurement unit. The optical coherence tomography OCT delivers detailed structural and depth information for thin skin lesions, while US and PAT enable the assessment of penetration depth in thicker lesions, and Raman spectroscopy analyzes the chemical composition of skin lesions, aiding in the differentiation between benign and malignant cases. The US and PAT are seamlessly integrated using an acoustical reflector inside a custom-made water tank, enabling C-mode measurements at the same position as OCT and RS without the need to switch scanning heads. Our system offers a fast and non-invasive approach to measure the dignity and maximal depth of skin lesions, which can help the dermatologists to make informed decisions regarding excision margins. The exemplary imaging capabilities of the presented multimodal setup are demonstrated in vivo on human nevus, which was excised after the measurement. The obtained results are compared with corresponding histological images for comprehensive evaluation.

Flow cytometry is a widely used analysis technique in biomedical sciences. It has found extensive utilization in both clinical diagnostics and cutting-edge biological research. As the method has been gaining greater recognition, its underlying technologies have undergone rapid development to further expand its range of applications. A notable trend is the introduction of imaging modalities to flow cytometry to expand the information content of the analyzed sample.

The introduction of a camera component to the already well-established detectors, such as photo multiplier tubes (PMTs) or avalanche photo diodes (APDs), adds intricacy to the arrangement of optical subsystem in flow cytometer. Moreover, it brings forth additional requirements for effectively coordinating information capture among different detector types. An appealing alternative to address this challenge is hyperspectral imaging – a technique which enables capturing of the spatial and spectral information simultaneously. Yet, there has not been much research performed to study applications of hyperspectral imaging in combination with narrow bandwidth illumination commonly used in flow cytometry.

In this work, we investigate the applicability of hyperspectral imaging to flow cytometrical systems, where a multiple wavelength laser system is utilized for sample illumination. A four-wavelength laser illumination platform developed by Modulight Corporation is utilized as the light source. Our main objective is to assess the hyperspectral imaging component's ability to distinguish between the illuminating light and the fluorescence emitted by the sample. Furthermore, we carefully evaluate the quality of the obtained hyperspectral images and explore the potential to differentiate samples based on the collected spatial data.

Diffuse optical tomography (DOT) is a promising non-invasive optical imaging technology that can provide functional information of biological tissues. Since the diffused light undergoes multiple scattering in biological tissues, and the boundary measurements are limited, the inverse problem of DOT is ill-posed and ill-conditioned. To overcome these limitations, inverse problems in DOT are often mitigated using regularization techniques, which use data fitting and regularization terms to suppress the effects of measurement noise and modeling errors. Tikhonov regularization, utilizing the L2 norm as its regularization term, often leads to images that are excessively smooth. In recent years, with the continuous development of deep learning algorithms, many researchers have used Model-based deep learning methods for reconstruction. However, the reconstruction of DOT is solved on mesh, arising from a finite element method for inverse problems, it is difficult to use it directly for convolutional network. Therefore, we propose a model-based graph convolutional network (Model-GCN). Overall, Model-GCN achieves better image reconstruction results compared to Tikhonov, with lower absolute bias error (ABE). Specifically, for total hemoglobin (HbT) and water, the average reduction in ABE is 68.3% and 77.3%, respectively. Additionally, the peak signal-to-noise (PSNR) values are on average increased by 6.4dB and 7.0dB.