Travis William Sawyer

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• Duan, S., Sawyer, T. W., Sontz, R. A., Wieland, B. A., Diaz, A. F., & Merchant, J. L. (2022). GFAP-directed Inactivation of Men1 Exploits Glial Cell Plasticity in Favor of Neuroendocrine Reprogramming. Cellular and molecular gastroenterology and hepatology, 14(5), 1025-1051.
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  Efforts to characterize the signaling mechanisms that underlie gastroenteropancreatic neoplasms (GEP-NENs) are precluded by a lack of comprehensive models that recapitulate pathogenesis. Investigation into a potential cell-of-origin for gastrin-secreting NENs revealed a non-cell autonomous role for loss of menin in neuroendocrine cell specification, resulting in an induction of gastrin in enteric glia. Here, we investigated the hypothesis that cell autonomous Men1 inactivation in glial fibrillary acidic protein (GFAP)-expressing cells induced neuroendocrine differentiation and tumorigenesis.
• Knapp, T. G., Duan, S., Merchant, J. L., & Sawyer, T. W. (2022). Quantitative characterization of duodenal gastrinoma autofluorescence using multiphoton microscopy. Lasers in surgery and medicine.
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  Duodenal gastrinomas (DGASTs) are neuroendocrine tumors that develop in the submucosa of the duodenum and produce the hormone gastrin. Surgical resection of DGASTs is complicated by the small size of these tumors and the tendency for them to develop diffusely in the duodenum. Endoscopic mucosal resection of DGASTs is an increasingly popular method for treating this disease due to its low complication rate but suffers from poor rates of pathologically negative margins. Multiphoton microscopy can capture high-resolution images of biological tissue with contrast generated from endogenous fluorescence (autofluorescence [AF]) through two-photon excited fluorescence (2PEF). Second harmonic generation is another popular method of generating image contrast with multiphoton microscopy (MPM) and is a light-scattering phenomenon that occurs predominantly from structures such as collagen in biological samples. Some molecules that contribute to AF change in abundance from processes related to the cancer disease process (e.g., metabolic changes, oxidative stress, and angiogenesis).
• Sawyer, T. W., Taylor-Williams, M., Tao, R., Xia, R., Williams, C., & Bohndiek, S. E. (2022). Opti-MSFA: a toolbox for generalized design and optimization of multispectral filter arrays. Optics express, 30(5), 7591-7611.
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  Multispectral imaging captures spatial information across a set of discrete spectral channels and is widely utilized across diverse applications such as remote sensing, industrial inspection, and biomedical imaging. Multispectral filter arrays (MSFAs) are filter mosaics integrated atop image sensors that facilitate cost-effective, compact, snapshot multispectral imaging. MSFAs are pre-configured based on application-where filter channels are selected corresponding to targeted absorption spectra-making the design of optimal MSFAs vital for a given application. Despite the availability of many design and optimization approaches for spectral channel selection and spatial arrangement, major limitations remain. There are few robust approaches for joint spectral-spatial optimization, techniques are typically only applicable to limited datasets and most critically, are not available for general use and improvement by the wider community. Here, we reconcile current MSFA design techniques and present Opti-MSFA: a Python-based open-access toolbox for the centralized design and optimization of MSFAs. Opti-MSFA incorporates established spectral-spatial optimization algorithms, such as gradient descent and simulated annealing, multispectral-RGB image reconstruction, and is applicable to user-defined input of spatial-spectral datasets or imagery. We demonstrate the utility of the toolbox by comparing against other published MSFAs using the standard hyperspectral datasets Samson and Jasper Ridge, and further show application on experimentally acquired fluorescence imaging data. In conjunction with end-user input and collaboration, we foresee the continued development of Opti-MSFA for the benefit of the wider research community.
• Schwartz, D., Sawyer, T. W., Thurston, N., Barton, J., & Ditzler, G. (2022). Ovarian cancer detection using optical coherence tomography and convolutional neural networks. Neural computing & applications, 34(11), 8977-8987.
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  Ovarian cancer has the sixth-largest fatality rate in the United States among all cancers. A non-surgical assay capable of detecting ovarian cancer with acceptable sensitivity and specificity has yet to be developed. However, such a discovery would profoundly impact the pace of the treatment and improvement to patients' quality of life. Achieving such a solution requires high-quality imaging, image processing, and machine learning to support an acceptably robust automated diagnosis. In this work, we propose an automated framework that learns to identify ovarian cancer in transgenic mice from optical coherence tomography (OCT) recordings. Classification is accomplished using a neural network that perceives spatially ordered sequences of tomograms. We present three neural network-based approaches, namely a VGG-supported feed-forward network, a 3D convolutional neural network, and a convolutional LSTM (Long Short-Term Memory) network. Our experimental results show that our models achieve a favorable performance with no manual tuning or feature crafting, despite the challenging noise inherent in OCT images. Specifically, our best performing model, the convolutional LSTM-based neural network, achieves a mean AUC (± standard error) of 0.81 ± 0.037. To the best of the authors' knowledge, no application of machine learning to analyze depth-resolved OCT images of whole ovaries has been documented in the literature. A significant broader impact of this research is the potential transferability of the proposed diagnostic system from transgenic mice to human organs, which would enable medical intervention from early detection of an extremely deadly affliction.
• Taylor-Williams, M., Mead, S., Sawyer, T. W., Hacker, L., Williams, C., Berks, M., Murray, A., & Bohndiek, S. E. (2022). Multispectral imaging of nailfold capillaries using light-emitting diode illumination. Journal of biomedical optics, 27(12), 126002.
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  The capillaries are the smallest blood vessels in the body, typically imaged using video capillaroscopy to aid diagnosis of connective tissue diseases, such as systemic sclerosis. Video capillaroscopy allows visualization of morphological changes in the nailfold capillaries but does not provide any physiological information about the blood contained within the capillary network. Extracting parameters such as hemoglobin oxygenation could increase sensitivity for diagnosis and measurement of microvascular disease progression.
• Kiekens, K. C., Vega, D., Thurgood, H. T., Galvez, D., McGregor, D. J., Sawyer, T. W., & Barton, J. K. (2021). Effect of an Added Mass on the Vibration Characteristics for Raster Scanning of a Cantilevered Optical Fiber. Journal of engineering and science in medical diagnostics and therapy, 4(2), 021007.
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  Piezoelectric tube actuators with cantilevered optical fibers have enabled the miniaturization of scanning image acquisition techniques for endoscopic implementation. To achieve raster scanning for such a miniaturized system, the first resonant frequency should be of the order of 10 s of Hz. We explore adding a mass at an intermediate location along the length of the fiber to alter the resonant frequencies of the system. We provide a mathematical model to predict resonant frequencies for a cantilevered beam with an intermediate mass. The theoretical and measured data match well for various fiber lengths, mass sizes, and mass attachment locations along the fiber.
• Sawyer, D. M., Sawyer, T. W., Eshghi, N., Hsu, C., Hamilton, R. J., Garland, L. L., & Kuo, P. H. (2021). Pilot Study: Texture Analysis of PET Imaging Demonstrates Changes in F-FDG Uptake of the Brain After Prophylactic Cranial Irradiation. Journal of nuclear medicine technology, 49(1), 34-38.
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  Prophylactic cranial irradiation (PCI) is used to decrease the probability of developing brain metastases in patients with small cell lung cancer and has been linked to deleterious cognitive effects. Although no well-established imaging markers for these effects exist, previous studies have shown that structural and metabolic changes in the brain can be detected with MRI and PET. This study used an image processing technique called texture analysis to explore whether global changes in brain glucose metabolism could be characterized in PET images. F-FDG PET images of the brain from patients with small cell lung cancer, obtained before and after the administration of PCI, were processed using texture analysis. Texture features were compared between the pre- and post-PCI images. Multiple texture features demonstrated statistically significant differences before and after PCI when texture analysis was applied to the brain parenchyma as a whole. Regional differences were also seen but were not statistically significant. Global changes in brain glucose metabolism occur after PCI and are detectable using advanced image processing techniques. These changes may reflect radiation-induced damage and thus may provide a novel method for studying radiation-induced cognitive impairment.
• Fitzpatrick, C. R., Wilson, A., Sawyer, T. W., Christopher, P. J., Wilkinson, T. D., Bohndiek, S. E., & Gordon, G. S. (2020). Robustness to misalignment of low-cost, compact quantitative phase imaging architectures. OSA continuum, 3(10), 2660-2679.
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  Non-interferometric approaches to quantitative phase imaging could enable its application in low-cost, miniaturised settings such as capsule endoscopy. We present two possible architectures and both analyse and mitigate the effect of sensor misalignment on phase imaging performance. This is a crucial step towards determining the feasibility of implementing phase imaging in a capsule device. First, we investigate a design based on a folded 4f correlator, both in simulation and experimentally. We demonstrate a novel technique for identifying and compensating for axial misalignment and explore the limits of the approach. Next, we explore the implications of axial and transverse misalignment, and of manufacturing variations on the performance of a phase plate-based architecture, identifying a clear trade-off between phase plate resolution and algorithm convergence time. We conclude that while the phase plate architecture is more robust to misalignment, both architectures merit further development with the goal of realising a low-cost, compact system for applying phase imaging in capsule endoscopy.
• Sawyer, T. W., Koevary, J. W., Howard, C. C., Austin, O. J., Rice, P. F., Hutchens, G. V., Chambers, S. K., Connolly, D. C., & Barton, J. K. (2020). Fluorescence and Multiphoton Imaging for Tissue Characterization of a Model of Postmenopausal Ovarian Cancer. Lasers in surgery and medicine, 52(10), 993-1009.
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  To determine the efficacy of targeted fluorescent biomarkers and multiphoton imaging to characterize early changes in ovarian tissue with the onset of cancer.
• Vega, D., Sawyer, T. W., Pham, N. Y., & Barton, J. K. (2020). Use of embedded and patterned dichroic surfaces with reflective optical power to enable multiple optical paths in a micro-objective. Applied optics, 59(22), G71-G78.
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  We demonstrate the use of patterned dichroic surfaces with reflective optical power to create multiple optical paths in a single lens system. The application of these surfaces enables a micro-endoscope to accommodate multiple imaging technologies with only one optical system, making the packaging more compact and reliable. The optical paths are spectrally separated using different wavelengths for each path. The dichroic surfaces are designed such that the visible wavelengths transmit through the surfaces optically unaffected, but the near-infrared wavelengths are reflected in a telescope-like configuration with the curved dichroic surfaces providing reflective optical power. We demonstrate wide-field visible monochromatic imaging and microscopic near-infrared imaging using the same set of lenses. The on-axis measured resolution of the wide-field imaging configuration is approximately 14 µm, and the measured resolution of the microscopic imaging configuration is approximately 2 µm. Wide-field white-light imaging of an object is also demonstrated for a qualitative perspective on the imaging capabilities. Other configurations and applications in fields such as optical metrology are discussed to expand on the versatility of the demonstrated optical system.
• Zhao, L. L., Tronsgaard, R., Trahan, R., Szymkowiak, A. E., Shao, M., Sawyer, T. W., Sawyer, D. M., Riva, M., Petersburg, R. R., Pawluczyk, R., Pariani, G., Ong, J. M., Nemati, B., Mccracken, T. M., Llama, J., Leet, C., Jurgenson, C. A., Genoni, M., Fournier, P., , Fischer, D. A., et al. (2020). Performance Verification of the EXtreme PREcision Spectrograph. The Astronomical Journal, 159(5), 238. doi:10.3847/1538-3881/ab811d
• Gordon, G. S., Joseph, J., Alcolea, M. P., Sawyer, T., Williams, C., Fitzpatrick, C. R., Jones, P. H., di Pietro, M., Fitzgerald, R. C., Wilkinson, T. D., & Bohndiek, S. E. (2019). Quantitative phase and polarization imaging through an optical fiber applied to detection of early esophageal tumorigenesis. Journal of biomedical optics, 24(12), 1-13.
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  Phase and polarization of coherent light are highly perturbed by interaction with microstructural changes in premalignant tissue, holding promise for label-free detection of early tumors in endoscopically accessible tissues such as the gastrointestinal tract. Flexible optical multicore fiber (MCF) bundles used in conventional diagnostic endoscopy and endomicroscopy scramble phase and polarization, restricting clinicians instead to low-contrast amplitude-only imaging. We apply a transmission matrix characterization approach to produce full-field images of amplitude, quantitative phase, and resolved polarimetric properties through an MCF. We first demonstrate imaging and quantification of biologically relevant amounts of optical scattering and birefringence in tissue-mimicking phantoms. We present an entropy metric that enables imaging of phase heterogeneity, indicative of disordered tissue microstructure associated with early tumors. Finally, we demonstrate that the spatial distribution of phase and polarization information enables label-free visualization of early tumors in esophageal mouse tissues, which are not identifiable using conventional amplitude-only information.
• Gordon, G. S., Joseph, J., Sawyer, T., Macfaden, A. J., Williams, C., Wilkinson, T. D., & Bohndiek, S. E. (2019). Full-field quantitative phase and polarisation-resolved imaging through an optical fibre bundle. Optics express, 27(17), 23929-23947.
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  Flexible optical fibres, used in conventional medical endoscopy and industrial inspection, scramble phase and polarisation information, restricting users to amplitude-only imaging. Here, we exploit the near-diagonality of the multi-core fibre (MCF) transmission matrix in a parallelised fibre characterisation architecture, enabling accurate imaging of quantitative phase (error
• Sawyer, T. W., Koevary, J. W., Rice, F. P., Howard, C. C., Austin, O. J., Connolly, D. C., Cai, K. Q., & Barton, J. K. (2019). Quantification of multiphoton and fluorescence images of reproductive tissues from a mouse ovarian cancer model shows promise for early disease detection. Journal of biomedical optics, 24(9), 1-16.
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  Ovarian cancer is the deadliest gynecologic cancer due predominantly to late diagnosis. Early detection of ovarian cancer can increase 5-year survival rates from 40% up to 92%, yet no reliable early detection techniques exist. Multiphoton microscopy (MPM) is a relatively new imaging technique sensitive to endogenous fluorophores, which has tremendous potential for clinical diagnosis, though it is limited in its application to the ovaries. Wide-field fluorescence imaging (WFI) has been proposed as a complementary technique to MPM, as it offers high-resolution imagery of the entire organ and can be tailored to target specific biomarkers that are not captured by MPM imaging. We applied texture analysis to MPM images of a mouse model of ovarian cancer. We also conducted WFI targeting the folate receptor and matrix metalloproteinases. We find that texture analysis of MPM images of the ovary can differentiate between genotypes, which is a proxy for disease, with high statistical significance (p 
• Sawyer, T. W., Rice, P. F., Sawyer, D. M., Koevary, J. W., & Barton, J. K. (2019). Evaluation of segmentation algorithms for optical coherence tomography images of ovarian tissue. Journal of medical imaging (Bellingham, Wash.), 6(1), 014002.
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  Ovarian cancer has the lowest survival rate among all gynecologic cancers predominantly due to late diagnosis. Early detection of ovarian cancer can increase 5-year survival rates from 40% up to 92%, yet no reliable early detection techniques exist. Optical coherence tomography (OCT) is an emerging technique that provides depth-resolved, high-resolution images of biological tissue in real-time and demonstrates great potential for imaging of ovarian tissue. Mouse models are crucial to quantitatively assess the diagnostic potential of OCT for ovarian cancer imaging; however, due to small organ size, the ovaries must first be separated from the image background using the process of segmentation. Manual segmentation is time-intensive, as OCT yields three-dimensional data. Furthermore, speckle noise complicates OCT images, frustrating many processing techniques. While much work has investigated noise-reduction and automated segmentation for retinal OCT imaging, little has considered the application to the ovaries, which exhibit higher variance and inhomogeneity than the retina. To address these challenges, we evaluate a set of algorithms to segment OCT images of mouse ovaries. We examine five preprocessing techniques and seven segmentation algorithms. While all preprocessing methods improve segmentation, Gaussian filtering is most effective, showing an improvement of . Of the segmentation algorithms, active contours performs best, segmenting with an accuracy of compared with manual segmentation. Even so, further optimization could lead to maximizing the performance for segmenting OCT images of the ovaries.
• Sawyer, T. W. (2018). Alignment of sensor arrays in optical instruments using a geometric approach. Applied optics, 57(4), 794-801.
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  Alignment of sensor arrays in optical instruments is critical to maximize the instrument's performance. While many commercial systems use standardized mounting threads for alignment, custom systems require specialized equipment and alignment procedures. These alignment procedures can be time-consuming, dependent on operator experience, and have low repeatability. Furthermore, each alignment solution must be considered on a case-by-case basis, leading to additional time and resource cost. Here I present a method to align a sensor array using geometric analysis. By imaging a grid pattern of dots, I show that it is possible to calculate the misalignment for a sensor in five degrees of freedom simultaneously. I first test the approach by simulating different cases of misalignment using Zemax before applying the method to experimentally acquired data of sensor misalignment for an echelle spectrograph. The results show that the algorithm effectively quantifies misalignment in five degrees of freedom for an F/5 imaging system, accurate to within ±0.87  deg in rotation and ±0.86  μm in translation. Furthermore, the results suggest that the method can also be applied to non-imaging systems with a small penalty to precision. This general approach can potentially improve the alignment of sensor arrays in custom instruments by offering an accurate, quantitative approach to calculating misalignment in five degrees of freedom simultaneously.
• Sawyer, T. W., Chandra, S., Rice, P. F., Koevary, J. W., & Barton, J. K. (2018). Three-dimensional texture analysis of optical coherence tomography images of ovarian tissue. Physics in medicine and biology, 63(23), 235020.
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  Ovarian cancer has the lowest survival rate among all gynecologic cancers due to predominantly late diagnosis. Optical coherence tomography (OCT) has been applied successfully to experimentally image the ovaries in vivo; however, a robust method for analysis is still required to provide quantitative diagnostic information. Recently, texture analysis has proved to be a useful tool for tissue characterization; unfortunately, existing work in the scope of OCT ovarian imaging is limited to only analyzing 2D sub-regions of the image data, discarding information encoded in the full image area, as well as in the depth dimension. Here we address these challenges by testing three implementations of texture analysis for the ability to classify tissue type. First, we test the traditional case of extracted 2D regions of interest; then we extend this to include the entire image area by segmenting the organ from the background. Finally, we conduct a full volumetric analysis of the image volume using 3D segmented data. For each case, we compute features based on the Grey-Level Co-occurence Matrix and also by introducing a new approach that evaluates the frequency distribution in the image by computing the energy density. We test these methods on a mouse model of ovarian cancer to differentiate between age, genotype, and treatment. The results show that the 3D application of texture analysis is most effective for differentiating tissue types, yielding an average classification accuracy of 78.6%. This is followed by the analysis in 2D with the segmented image volume, yielding an average accuracy of 71.5%. Both of these improve on the traditional approach of extracting square regions of interest, which yield an average classification accuracy of 67.7%. Thus, applying texture analysis in 3D with a fully segmented image volume is the most robust approach to quantitatively characterizing ovarian tissue.
• Sawyer, T. W., Hawkins, K. S., & Damento, M. (2017). Using confidence intervals to evaluate the focus alignment of spectrograph detector arrays. Applied optics, 56(18), 5295-5300.
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  High-resolution spectrographs extract detailed spectral information of a sample and are frequently used in astronomy, laser-induced breakdown spectroscopy, and Raman spectroscopy. These instruments employ dispersive elements such as prisms and diffraction gratings to spatially separate different wavelengths of light, which are then detected by a charge-coupled device (CCD) or complementary metal-oxide-semiconductor (CMOS) detector array. Precise alignment along the optical axis (focus position) of the detector array is critical to maximize the instrumental resolution; however, traditional approaches of scanning the detector through focus lack a quantitative measure of precision, limiting the repeatability and relying on one's experience. Here we propose a method to evaluate the focus alignment of spectrograph detector arrays by establishing confidence intervals to measure the alignment precision. We show that propagation of uncertainty can be used to estimate the variance in an alignment, thus providing a quantitative and repeatable means to evaluate the precision and confidence of an alignment. We test the approach by aligning the detector array of a prototype miniature echelle spectrograph. The results indicate that the procedure effectively quantifies alignment precision, enabling one to objectively determine when an alignment has reached an acceptable level. This quantitative approach also provides a foundation for further optimization, including automated alignment. Furthermore, the procedure introduced here can be extended to other alignment techniques that rely on numerically fitting data to a model, providing a general framework for evaluating the precision of alignment methods.
• Sawyer, T. W., Luthman, A. S., & Bohndiek, S. E. (2017). Evaluation of illumination system uniformity for wide-field biomedical hyperspectral imaging. Journal of Optics, 19(4), 045301. doi:10.1088/2040-8986/aa6176
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  Hyperspectral imaging (HSI) systems collect both spatial (morphological) and spectral (chemical) information from a sample. HSI can provide sensitive analysis for biological and medical applications, for example, simultaneously measuring reflectance and fluorescence properties of a tissue, which together with structural information could improve early cancer detection and tumour characterisation. Illumination uniformity is a critical pre-condition for quantitative data extraction from an HSI system. Non-uniformity can cause glare, specular reflection and unwanted shading, which negatively impact statistical analysis procedures used to extract abundance of different chemical species. Here, we model and evaluate several illumination systems frequently used in wide-field biomedical imaging to test their potential for HSI. We use the software LightTools and FRED. The analysed systems include: a fibre ring light; a light emitting diode (LED) ring; and a diffuse scattering dome. Each system is characterised for spectral, spatial, and angular uniformity, as well as transfer efficiency. Furthermore, an approach to measure uniformity using the Kullback–Leibler divergence (KLD) is introduced. The KLD is generalisable to arbitrary illumination shapes, making it an attractive approach for characterising illumination distributions. Although the systems are quite comparable in their spatial and spectral uniformity, the most uniform angular distribution is achieved using a diffuse scattering dome, yielding a contrast of 0.503 and average deviation of 0.303 over a ±60° field of view with a 3.9% model error in the angular domain. Our results suggest that conventional illumination sources can be applied in HSI, but in the case of low light levels, bespoke illumination sources may offer improved performance.
• Sawyer, T. W., Luthman, A. S., & Bohndiek, S. E. (2017). Evaluation of illumination systems for wide-field hyperspectral imaging in biomedical applications. Proceedings of SPIE, 10068. doi:10.1117/12.2250633
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  Hyperspectral imaging (HSI) systems collect both morphological and chemical characteristics from a sample by simultaneously acquiring spatial and spectral information. HSI has potential to advance cancer diagnostics by characterizing reflectance and fluorescence properties of a tissue, as well as extracting microstructural in- formation, all of which are altered through the development of a tumor. Illumination uniformity is a critical pre-condition for extracting quantitative data from an HSI system. Spatial, angular, or spectral non-uniformity can cause glare, specular reflection and unwanted shading, which negatively impact statistical analysis techniques used to extract abundance of different chemical species. This is further exacerbated when imaging three-dimensional structures, such as tumors, whose appearance can cast shadows and form other occlusions. Furthermore, as HSI can be used simultaneously for white light and fluorescence imaging, a flexible system, which multiplexes narrowband and broadband illumination is necessary to fully utilize the capabilities of a biomedical HSI system. To address these challenges, we modeled illumination systems frequently used in wide-field biological imaging with the software LightTools and FRED. Each system is characterized for spectral, spatial, and angular uniformity, as well as total efficiency. While all three systems provide high spatial and spectral uniformity, the highest angular uniformity is achieved using a diffuse scattering dome, yielding a contrast of 0.503 and average deviation of 0.303 with a 3.91% model error. Nonetheless, results suggest that conventional systems may not be suitable for low-light-level applications, where tailoring illumination to match spatial and spectral requirements may be the best approach to maximize the performance.
• Sawyer, T. W., Petersburg, R., & Bohndiek, S. E. (2017). Tolerancing the alignment of large-core optical fibers, fiber bundles and light guides using a Fourier approach. Applied optics, 56(12), 3303-3310.
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  Optical fiber technology is found in a wide variety of applications to flexibly relay light between two points, enabling information transfer across long distances and allowing access to hard-to-reach areas. Large-core optical fibers and light guides find frequent use in illumination and spectroscopic applications, for example, endoscopy and high-resolution astronomical spectroscopy. Proper alignment is critical for maximizing throughput in optical fiber coupling systems; however, there currently are no formal approaches to tolerancing the alignment of a light-guide coupling system. Here, we propose a Fourier alignment sensitivity (FAS) algorithm to determine the optimal tolerances on the alignment of a light guide by computing the alignment sensitivity. The algorithm shows excellent agreement with both simulated and experimentally measured values and improves on the computation time of equivalent ray-tracing simulations by two orders of magnitude. We then apply FAS to tolerance and fabricate a coupling system, which is shown to meet specifications, thus validating FAS as a tolerancing technique. These results indicate that FAS is a flexible and rapid means to quantify the alignment sensitivity of a light guide, widely informing the design and tolerancing of coupling systems.

Proceedings Publications

• Sawyer, T. W., Salcin, E., Friedman, J. S., & Diaz, A. (2021). Extraction of precise object orientation and position from LIDAR data using maximum-likelihood methods. In Laser Radar Technology and Applications XXVI.
• Sawyer, T. W., Salcin, E., Friedman, J. S., & Diaz, A. (2021). Using principle component analysis to estimate geometric parameters from point cloud LIDAR data. In Laser Radar Technology and Applications XXVI, 11744.
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  Light Detection and Ranging (LIDAR) is a popular sensing technique to measure static and dynamic objects with applications in many areas of defense technology including robotics, aircraft navigation and guidance systems, autonomous vehicles and aircraft landing systems, as well as tracking and measuring attitude of hypersonic objects. Despite widespread use of LIDAR to map out objects and environments, there remains a need for advanced analytic techniques to recover quantitative information about objects from LIDAR data, for example, the position and trajectory of a foreign object. One major class of LIDAR systems are those that produce so-called point-cloud data, which is a threedimensional sampling of a scene. Technical demands for extraction of geometric parameters from point-cloud spatial models are increasing as 3D LIDAR sensors and their application technology is continuously developed and popularized. While classical techniques for feature extraction and estimation exist, these existing techniques are currently inadequate to recover geometric parameters with desired accuracy for precision applications. To address this challenge, we developed an algorithm based on principal component analysis (PCA) to extract precise geometric parameters from LIDAR point-cloud data of objects including pitch, yaw, roll and xyz-position, as well as the rates of change of these parameters. We present the basis of this algorithm, as well as initial results using point cloud data of a rotating cylindrical object. The results suggest that PCA-based analysis could provide a robust and high precision approach for recovering object position and orientation, particularly when combined with other analytical approaches such as machine learning.
• Williams, C., Taylor-williams, M., Sawyer, T. W., Murray, A. K., Mead, S., Hacker, L., Bohndiek, S. E., & Berks, M. (2021). A low-cost LED-based multispectral capillaroscopy system for oximetry of the nailfold. In Optical Diagnostics and Sensing XXI: Toward Point-of-Care Diagnostics, 11651.
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  Nailfold capillaroscopy is a technique for imaging the capillary bed in the finger nailfold, that is used in the diagnosis of scleroderma. Knowledge of the capillary oxygenation profile would be a substantial advantage in disease evaluation. A compact, low-cost LED-illuminated capillaroscopy system was conceived based on inexpensive parts and optical hardware. The system uses a compact Raspberry Pi to control a custom-designed LED ring light, with white-light LEDs interleaved with three narrowband LEDs, and a Raspberry Pi camera. Capillary visualisation and distinction of haemoglobin contrast is demonstrated, suggesting future promise for application of multispectral nailfold capillaroscopy in low-resource settings.
• Yoon, J., Wilson, A., Waterhouse, D. J., Sawyer, T. W., Fitzgerald, R. C., Bohndiek, S. E., & Pietro, M. D. (2020). Development of a compact multimodal imaging system for rapid characterisation of intrinsic optical properties of freshly excised tissue (Conference Presentation). In Multimodal Biomedical Imaging XV, 11232.
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  Advanced optical endoscopic imaging techniques, including hyperspectral and holographic endoscopy, have shown promise in the improved diagnosis of the early stage of cancer. However, clinical applications of these imaging systems are still limited due to unclear diagnostic optical properties. Here, we developed a compact multimodal imaging system that enables hyperspectral imaging, spatial-frequency domain imaging, and 3D profilometric imaging to characterise the optimal optical features for the early detection of lesions. Optical properties of fresh specimens obtained from patients were measured within 30 minutes, and then histopathological assessment of specimens was performed to link extracted optical features to gold-standard diagnosis. With further sample collection and system refinements, this system can be used for high-throughput optical characterisation of fresh tissue specimens, allowing us to determine the optical signatures of early-stage disease.
• Sawyer, T. W., Williams, C., & Bohndiek, S. E. (2019). Spectral Band Selection and Tolerancing for Multispectral Filter Arrays. In Frontiers in Optics + Laser Science APS/DLS.
• Gordon, G. S., Sawyer, T. W., Bohndiek, S. E., Wilkinson, T. D., & Fitzpatrick, C. R. (2018). Wide-field phase imaging for the endoscopic detection of dysplasia and early-stage esophageal cancer. In Endoscopic Microscopy XIII, 10470.
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  © 2018 SPIE. Esophageal cancer has a 5-year survival rate below 20%, but can be curatively resected if it is detected early. At present, poor contrast for early lesions in white light imaging leads to a high miss rate in standard-of-care endoscopic surveillance. Early lesions in the esophagus, referred to as dysplasia, are characterized by an abundance of abnormal cells with enlarged nuclei. This tissue has a different refractive index profile to healthy tissue, which results in different light scattering properties and provides a source of endogenous contrast that can be exploited for advanced endoscopic imaging. For example, point measurements of such contrast can be made with scattering spectroscopy, while optical coherence tomography generates volumetric data. However, both require specialist interpretation for diagnostic decision making. We propose combining wide-field phase imaging with existing white light endoscopy in order to provide enhanced contrast for dysplasia and early-stage cancer in an image format that is familiar to endoscopists. Wide-field phase imaging in endoscopy can be achieved using coherent illumination combined with phase retrieval algorithms. Here, we present the design and simulation of a benchtop phase imaging system that is compatible with capsule endoscopy. We have undertaken preliminary optical modelling of the phase imaging setup, including aberration correction simulations and an investigation into distinguishing between different tissue phantom scattering coefficients. As our approach is based on phase retrieval rather than interferometry, it is feasible to realize a device with low-cost components for future clinical implementation.
• Sawyer, T. W., & Bohndiek, S. E. (2017). Towards a simulation framework to maximize the resolution of biomedical hyperspectral imaging. In Diffuse Optical Spectroscopy and Imaging VI, 10412.
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  When light is incident upon tissue, imaging contrast can be obtained from a range of interactions including absorption, scattering and fluorescence. Clinical optical imaging systems are typically optimized to report on a single contrast source, for example, using standard RGB cameras to produce white light reflectance images or filter-based approaches to extract fluorescence emissions. Hyperspectral imaging has the potential to over-come the need for specialized instrumentation, by sampling spatial and spectral information simultaneously. In particular, spectrally resolved detector arrays (SRDAs) now monolithically integrate spectral filters with CMOS image sensors to provide a robust, compact and low cost solution to video rate hyperspectral imaging. However, SRDAs suffer from a significant limitation, which is the inherent tradeoff between spatial and spectral resolution. Therefore, the properties of the SRDA including the number of filters, their wavelength and bandwidth, needs be optimized for tissue imaging. To achieve this, we have developed a software framework to optimize spectral band selection, simulating the hyperspectral sample illumination, data acquisition and spectral unmixing processes. Our approach shows early promise for selecting appropriate spectral filters, which allows us to maintain high spatial resolution for imaging.