In this document, we study the problem of optimally placing a mixture of directional and omnidirectional cameras. In our solution, the workspace is represented by an occupancy grid map [1]. Then, using surface-projected workspace and camera perception models, we develop a binary integer programming algorithm. The results of the algorithm are applied successfully to a variety of simulated scenarios.

Intravascular optical coherence tomography (IV-OCT) allows evaluation of atherosclerotic plaques; however, plaque characterization is performed by visual assessment and requires a trained expert for interpretation of the large data sets. Here, we present a novel computational method for automated IV-OCT plaque characterization. This method is based on the modeling of each A-line of an IV-OCT data set as a linear combination of a number of depth profiles. After estimating these depth profiles by means of an alternating least square optimization strategy, they are automatically classified to predefined tissue types based on their morphological characteristics. The performance of our proposed method was evaluated with IV-OCT scans of cadaveric human coronary arteries and corresponding tissue histopathology. Our results suggest that this methodology allows automated identification of fibrotic and lipid-containing plaques. Moreover, this novel computational method has the potential to enable high throughput atherosclerotic plaque characterization.

Purpose: In glioma surgery, it is critical to maximize tumor resection without compromising adjacent non-cancerous brain tissue. Optical Coherence Tomography (OCT) is a non-invasive, label-free, real-time, high-resolution imaging modality that has been explored for glioma infiltration detection. Here we report a novel artificial intelligence (AI) assisted method for automated, real-time, in situ detection of glioma infiltration at high spatial resolution. Experimental Design: Volumetric OCT datasets were intraoperatively obtained from resected brain tissue specimens of 21 patients with glioma tumors of different stages and labeled as either non-cancerous or glioma-infiltrated based on histopathology evaluation of the tissue specimens (gold standard). Labeled OCT images from 12 patients were used as the training dataset to develop the AI assisted OCT-based method for automated detection of glioma-infiltrated brain tissue.

Fluorescence lifetime imaging (FLIM) offers a noninvasive approach for characterizing the biochemical composition of biological tissue. There has been an increasing interest in the application of multispectral FLIM for medical diagnosis. Central to the clinical translation of FLIM technology is the development of compact and high-speed endoscopy systems. Unfortunately, the predominant multispectral FLIM approaches suffer from limitations that impede the development of endoscopy systems that are suitable for in vivo tissue imaging. We present a compact wide-field time-gated FLIM flexible endoscope capable of continuous lifetime imaging of up to three fluorescence emission bands simultaneously. This novel endoscope design will facilitate the evaluation of FLIM for in vivo applications.

Simultaneous quantification of multifarious cellular metabolites and the extracellular matrix in vivo has been long sought. Simultaneous label-free autofluorescence and multi-harmonic (SLAM) microscopy has achieved simultaneous four-channel nonlinear imaging to study tissue structure and metabolism. In this study, we implemented two laser systems and directly compared SLAM microscopy with conventional two-photon microscopy for in vivo imaging. We found that three-photon imaging of adenine dinucleotide (phosphate) (NAD(P)H) in SLAM microscopy using our tailored laser source provided better resolution, contrast, and background suppression than conventional two-photon imaging of NAD(P)H. We also integrated fluorescence lifetime imaging with SLAM microscopy, and enabled differentiation of free and bound NAD(P)H. We imaged murine skin in vivo and showed that changes in tissue structure, cell dynamics, and metabolism can be monitored simultaneously in real-time. We also discovered an increase in metabolism and protein-bound NAD(P)H in skin cells during the early stages of wound healing.

In some applications of biomedical imaging, a linear mixture model can represent the constitutive elements (end-members) and their contributions (abundances) per pixel of the image. In this work, the extended blind end-member and abundance extraction (EBEAE) methodology is mathematically formulated to address the blind linear unmixing (BLU) problem subject to positivity constraints in optical measurements. The EBEAE algorithm is based on a constrained quadratic optimization and an alternated least-squares strategy to jointly estimate end-members and their abundances. In our proposal, a local approach is used to estimate the abundances of each end-member by maximizing their entropy, and a global technique is adopted to iteratively identify the end-members by reducing the similarity among them. All the cost functions are normalized, and four initialization approaches are suggested for the end-members matrix. Synthetic datasets are used first for the EBEAE validation at different noise types and levels, and its performance is compared to state-of-the-art algorithms in BLU. In a second stage, three experimental biomedical imaging applications are addressed with EBEAE: m-FLIM for chemometric analysis in oral cavity samples, OCT for macrophages identification in post-mortem artery samples, and hyper-spectral images for in-vivo brain tissue classification and tumor identification. In our evaluations, EBEAE was able to provide a quantitative analysis of the samples with none or minimal a priori information.INDEX TERMS Blind linear unmixing, constrained optimization, fluorescence lifetime imaging microscopy, hyperspectral imaging, optical coherence tomography.

ObjectiveImpaired diabetic wound healing is one of the serious complications associated with diabetes. In patients with diabetes, this impairment is characterized by several physiological abnormalities such as metabolic changes, reduced collagen production, and diminished angiogenesis. We designed and developed a multimodal optical imaging system that can longitudinally monitor formation of new blood vessels, metabolic changes, and collagen deposition in a non-invasive, label-free manner.Research design and methodsThe closure of a skin wound in (db/db) mice, which presents delayed wound healing pathologically similar to conditions in human type 2 diabetes mellitus, was non-invasively followed using the custom-built multimodal microscope. In this microscope, optical coherence tomography angiography was used for studying neovascularization, fluorescence lifetime imaging microscopy for nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) assessment, fluorescence intensity changes of NAD(P)H and flavin adenine dinucleotide (FAD) cofactors for evaluating metabolic changes, and second harmonic generation microscopy for analyzing collagen deposition and organization. The animals were separated into four groups: control, placebo, low concentration (LC), and high concentration (HC) treatment. Images of the wound and surrounding areas were acquired at different time points during a 28-day period.ResultsVarious physiological changes measured using the optical imaging modalities at different phases of wound healing were compared. A statistically significant improvement in the functional relationship between angiogenesis, metabolism, and structural integrity was observed in the HC group.ConclusionsThis study demonstrated the capability of multimodal optical imaging to non-invasively monitor various physiological aspects of the wound healing process, and thus become a promising tool in the development of better diagnostic, treatment, and monitoring strategies for diabetic wound care.

We have demonstrated the feasibility of accurate simultaneous OCT/FLIM morphological and biochemical characterization of coronary plaques at spatial resolutions and acquisition speeds compatible with catheter-based intravascular imaging. The success of this pilot study sets up future development of a multimodal intravascular imaging system that will enable studies that could help improve our understanding of plaque pathogenesis.