This PDF file contains the front matters associated with SPIE Proceedings Volume 12371, including the Title Page, Copyright information, Table of Contents and Conference Committee lists.

Fluorescent two-photon selective-plane illumination microscopy (2P-SPIM) enables deep imaging of cellular information such as proliferation, type identification, and signaling using fluorescence. Optical coherence tomography (OCT) can capture complementary structural information based on intrinsic optical scattering. We developed a specialized multimodal high-resolution embryonic imaging system combining the benefits of OCT with 2P-SPIM. The OCT and 2P-SPIM beams were optically co-aligned and scanned using the same scanners and the same objective lens. The resulting light sheet thickness was ~13 µm with a transverse resolution of ~2.1 µm. The OCT system was based on a 1050 nm centered swept source laser with a bandwidth of ~100 nm and a sweep rate of 100 kHz. The OCT system utilized a Michelson-style interferometer and had a lateral resolution of ~15 µm and an axial resolution of ~7 µm. The capabilities of the multimodal imaging system were demonstrated using images of fluorescent microbeads and a fluorescently tagged mouse embryo at gestational day 9.5. Due to the co-alignment of the OCT and 2P-SPIM systems, image registration was simple and allowed for high-throughput multimodal imaging without the use of sophisticated registration methods.

Histology is a well-known examination technique to study the biological cell and tissue structures. For histological assessment, imaging throughput, contrast, resolution, and quantification of morphology are crucial parameters. Although, there are techniques available which can scan the whole slide, but they lack specificity and quantification. In present study, we introduce a photonic chip based platform for multimodal imaging of FFPE tissue sections. Here, the photonic chip platform was integrated with Linnik type QPM module, which enables high contrast TIRF imaging and optical thickness of the specimen over scalable FOV. The proposed system has been used as a high throughput microscopy platform to study the functional and morphological features of FFPE human placenta tissue sections. The investigation of the tissue sections facilitates the identification and diagnosis of the various diseases, which can provide direction for treatment and can assist the prognosis of clinical outcome.

A novel MRI-guided near-infrared spectroscopic tomographic imaging system (NIRST) has been developed for breast cancer detection. NIRST imaging for an entire breast can be simultaneously carried out during MRI scanning in less than 4 minutes. Reconstructed phantom images showed clear contrast of a 20 mm inclusion to the background, and the total hemoglobin (HbT) and water concentration values estimated from the reconstructed images of a normal subject were in the same range as those obtained in our previous imaging studies.

Reconstructions in 3D widefield Diffuse Optical Tomography (DOT) suffer from poor spatial resolution. Therefore, widefield DOT techniques benefit from incorporating structural priors from a complementary modality, such as the micro-CT. Unfortunately, traditional Laplacian-based methods to integrate the priors in the inverse problem are highly time-consuming. Therefore, we propose a Deep Neural Network based end-to-end inverse solver that combines features from AUTOMAP and Z-net and utilizes the micro-CT priors in the training stage. Initial in silico and experimental phantom results demonstrate that the proposed network accurately reconstructs, in 3D, the absorption contrast with a high resolution.

The rate of skin cancer incidence including melanoma has been steadily increasing in the last decades. While melanoma often show little to no symptoms in the early stages, they can spread to the lymph nodes and drastically reduce survival chances in the later stages. The current gold standard for diagnosis is visual examination, excision, and histological examination of the sample tissue, which is an invasive, costly and time-consuming process. As an alternative to this procedure, we introduce a novel multimodal optical system that integrates ultrasound (US), photoacoustic tomography (PAT), and optical coherence tomography (OCT) with Raman spectroscopy (RS). The setup allows quick and non-invasive skin lesion diagnosis and the determination of 3D lesion depth, helping the dermatologists make a decision on the excision margins. The OCT delivers structural and depth information of thin skin lesions, while the US and PAT measure the penetration depth of thicker lesions and the RS analyzes the chemical composition that can be used to distinguish between benign and malignant skin lesions. In our setup, the RS and OCT share the optical path and the scanning elements, which allows colocalized measurements. The US and PAT are integrated with an acoustical reflector, which enables B-mode measurements at the same position as OCT and RS without switching the scanning head. We demonstrate the imaging capabilities of the multimodal setup on custom made agar phantoms and present first measurements on ex vivo mouse and in vivo human skin samples. We compare the results with the corresponding histological images.

The emergence of the Halcyon linear accelerator has allowed for increased patient throughput and improved treatment times for common treatment sites in radiation oncology. However, it has been shown that this can lead to increased surface dose in sites like breast cancer compared with treatments on conventional machines with flattened radiation beams. Cherenkov imaging can be used to estimate surface dose by detection of Cherenkov photons emitted in proportion to energy deposition from high energy electrons in tissue. Phantom studies were performed with both square beams in reference conditions and with clinical treatments, and dosimeter readings and Cherenkov images report higher surface dose (25% for flat phantom entrance dose, 5.9% for breast phantom treatment) from Halcyon beam deliveries than for equivalent deliveries from a TrueBeam linac. Additionally, the first Cherenkov images of a patient treated with Halcyon were acquired, and superficial dose was estimated.

Pancreatic neuroendocrine tumors (PNETs) are a rare but increasingly more prevalent cancer with heterogeneous clinical and pathological expression. Surgery is the preferred treatment for most PNETs, but existing intraoperative localization imaging techniques have poor tumor contrast and resolution. Our work tests the suitability of combined somatostatin receptor imaging (SRI) and multiphoton microscopy (MPM) for localizing PNETs, combining the labeled technique of SRI with the label-free technique of MPM for enhanced contrast and sensitivity. Our results suggest that this approach could be a valuable clinical tool for surgical localization and treatment of PNETs.

Fluorescence microscopic imaging of tissues is widely used for pathological diagnosis of diseases and biomedical research purposes. In addition to the exogenous fluorescent signal that is targeted for analysis, some molecules within biological tissues exhibit intrinsic fluorescence referred to as autofluorescence. This tissue optical property interferes with the detection and quantification of the fluorescent signal used to detect and assess biological tissues. To overcome this, hyperspectral imagers with increased spectral and spatial resolution have the capacity to provide greater structural and molecular information. Algorithm-based analysis platforms capable of analyzing large biomedical hyperspectral datasets are unmet needs and can extract useful spectral-spatial information from complex tissues. We present an open-source data analysis approach to exploit the potential of hyperspectral autofluorescence imaging and to extract unbiased and useful spectral-spatial information from the eye. Using an Image Mapping Spectrometer (from 528 nm to 836 nm); mounted on a fluorescence microscope, a non-destructive and label-free approach to evaluate the retina, choroid, and scleral tissues in eye sections is presented. The segmentation of the tissues is based on their respective autofluorescence spectral profiles and are compared using Analysis of Variance (ANOVA) and functional ANOVA. We demonstrate distinctly different autofluorescence spectra for individual eye tissue types. Furthermore, the systematic segmentation method is used to classify tissue types based on their divergent autofluorescence spectra. This study provides the metrics for further construction of spectral profile signatures in eye conditions and diseases. Furthermore, this hyperspectral-based semi-automatic segmentation approach can be expanded for application to other tissues in health and disease.

Multispectral imaging is becoming a key technique for biomedical research, but the crosstalk between autofluorescence and fluorescent material severely affects the interpretation of fluorescence images. Spectral unmixing is an effective technique for removing autofluorescence and separating fluorescent targets in multispectral fluorescence imaging. However, the effectiveness of most methods of spectral unmixing has a strong relationship with the noise in the image. In this work, we propose a multispectral fluorescence unmixing method based on a priori information to obtain the pure spectra and their corresponding abundance coefficients in the images. First, the obtained multispectral image is segmented into several superpixels using a superpixel segmentation method, and then the relative pure spectra are extracted using a spectral extraction algorithm on the superpixels. Since the autofluorescence distribution is spread over the whole body, the extracted spectra in which the autofluorescence can be considered as pure spectra are used as a priori knowledge for unmixing. The pixel spectral data that are similar to the set of relatively pure spectra are selected as the pure spectral candidate set. Then the pure spectra can be obtained using the Non-negative matrix factorization method with prior knowledge(NMF-upk). Finally, the abundance corresponding to each spectral feature can be obtained through the least square method. The proposed unmixing method is tested on simulated data and the results show that our unmixing algorithm outperforms other methods.