Thesis (Ph. D.)--University of Chicago, Dept. of Radiology, August 2000. / Includes bibliographical references. Also available on the Internet.

Developments in modern bio-imaging techniques have allowed the routine collection of a vast amount of data from various techniques. The challenges lie in how to build accurate and efficient models to draw conclusions from the data and facilitate scientific discoveries. Fortunately, recent advances in statistics, machine learning, and deep learning provide valuable tools. This thesis describes some of our efforts to build scalable Bayesian models for four bio-imaging applications: (1) Stochastic Optical Reconstruction Microscopy (STORM) Imaging, (2) particle tracking, (3) voltage smoothing, (4) detect color-labeled neurons in c elegans and assign identity to the detections.

Fluorescence microscopy is a powerful imaging technique used in the biological sciences to identify labeled components of a sample with specificity. This is usually accomplished through labeling with fluorescent dyes, isolating these dyes by their spectral signatures with optical filters, and recording the intensity of the fluorescent response. Although these techniques are widely used, fluorescence intensity images can be negatively affected by a variety of factors that impact the fluorescence intensity. Fluorescence lifetime imaging microscopy (FLIM) is an imaging technique that is relatively immune to intensity fluctuations and also provides the unique ability to directly monitor the microenvironment surrounding a fluorophore. Despite the benefits associated with FLIM, the applications to which it is applied are fairly limited due to long image acquisition times and high cost of traditional hardware. Recent advances in complementary metal-oxide-semiconductor (CMOS) single-photon avalanche diodes (SPADs) have enabled the design of low-cost imaging arrays that are capable of recording lifetime images with acquisition times greater than one order of magnitude faster than existing systems. However, these SPAD arrays have yet to realize the full potential of the technology due to limitations in their ability to handle the vast amount of data generated during the commonly used time-correlated single-photon counting (TCSPC) lifetime imaging technique. This thesis presents the design, implementation, characterization, and demonstration of a high speed FLIM imaging system. The components of this design include a CMOS imager chip in a standard 0.13 μm technology containing a custom CMOS SPAD, a 64-by-64 array of these SPADs, pixel control circuitry, independent time-to-digital converters (TDCs), a FLIM specific datapath, and high bandwidth output buffers. In addition to the CMOS imaging array, a complete system was designed and implemented using a printed circuit board (PCB) for capturing data from the imager, creating histograms for the photon arrival data using field-programmable gate arrays, and transferring the data to a computer using a cabled PCIe interface. Finally, software is used to communicate between the imaging system and a computer.The dark count rate of the SPAD was measured to be only 231 Hz at room temperature while maintaining a photon detection probability of up to 30\%. TDCs included on the array have a 62.5 ps resolution and a 64 ns range, which is suitable for measuring the lifetime of most biological fluorophores. Additionally, the on-chip datapath was designed to handle continuous data transfers at rates capable of supporting TCSPC-based lifetime imaging at 100 frames per second. The system level implementation also provides sufficient data throughput for transferring up to 750 frames per second from the imaging system to a computer. The lifetime imaging system was characterized using standard techniques for evaluating SPAD performance and an electrical delay signal for measuring the TDC performance. This thesis concludes with a demonstration of TCSPC-FLIM imaging at 100 frames per second -- the fastest 64-by-64 TCSPC FLIM that has been demonstrated. This system overcomes some of the limitations of existing FLIM systems and has the potential to enable new application domains in dynamic FLIM imaging.

Thesis (Ph. D.)--State University of New York at Binghamton, Department of Chemistry, 2007. / Includes bibliographical references.

Nous nous intéressons dans cette thèse au problème de l'estimation dense du mouvement dans des séquences d'images, également désigné sous le terme de flot optique. Les approches usuelles exploitent une paramétrisation locale ou une régularisation globale du champ de déplacement. Nous explorons plusieurs façons de combiner ces deux stratégies, pour surmonter leurs limitations respectives. Nous nous plaçons dans un premier temps dans un cadre variationnel global, et considérons un filtrage local du terme de données. Nous proposons un filtrage spatialement adaptatif, optimisé conjointement au mouvement, pour empêcher le sur-lissage induit par le filtrage spatialement constant. Dans une seconde partie, nous proposons un cadre générique d'agrégation pour l'estimation du flot optique. Sous sa forme générale, il consiste en une estimation locale de candidats de mouvements, suivie de leur combinaison à l'étape d'agrégation avec un modèle global. Ce schéma permet une estimation efficace des grands déplacements et des discontinuités de mouvement. Nous développons également une méthode générique de gestion des occultations. Notre méthode est validée par une analyse expérimentale conséquente sur des bases de données de référence en vision par ordinateur. Nous démontrons la supériorité de notre méthode par rapport à l'état de l'art sur les séquences présentant de grands déplacements. La dernière partie de la thèse est consacrée à l'adaptation des approches précédentes à des problématiques d'imagerie biologique. Les changements locaux importants d'intensité observés en imagerie de fluorescence sont estimés et compensé par une adaptation de notre schéma d'agrégation. Nous proposons également une méthode variationnelle avec filtrage local dédiée au cas de mouvements diffusifs de particules. / This thesis is concerned with dense motion estimation in image sequences, also known as optical flow. Usual approaches exploit either local parametrization or global regularization of the motion field. We explore several ways to combine these two strategies, to overcome their respective limitations. We first address the problem in a global variational framework, and consider local filtering of the data term. We design a spatially adaptive filtering optimized jointly with motion, to prevent over-smoothing induced by the spatially constant approach. In a second part, we propose a generic two-step aggregation framework for optical flow estimation. The most general form is a local computation of motion candidates, combined in the aggregation step through a global model. Large displacements and motion discontinuities are efficiently recovered with this scheme. We also develop a generic exemplar-based occlusion handling to deal with large displacements. Our method is validated with extensive experiments in computer vision benchmarks. We demonstrate the superiority of our method over state-of-the-art on sequences with large displacements. Finally, we adapt the previous methods to biological imaging issues. Estimation and compensation of large local intensity changes frequently occurring in fluorescence imaging are efficiently estimated and compensated with an adaptation of our aggregation framework. We also propose a variational method with local filtering dedicated to the case of diffusive motion of particles.

Phosphorescent transition metal complexes make up an important group of compounds that continues to attract intense research owing to their intrinsic bioimaging applications that arise from bright emissions, relatively long excited state lifetimes, and large stokes shifts. Now for biomaging assay a model organism is required which must meet certain criteria for practical applications. The organism needs to be small, with a high turn-over of progeny (high fecundity), a short lifecycle, and low maintenance and assay costs. Our model organism C. elegans met all the criteria. The ideal phosphor has low toxicity in the model organism. In this work the strongly phosphorescent platinum (II) pyrophosphito-complex was tested for biological applications as a potential in vivo hypoxia sensor. The suitability of the phosphor was derived from its water solubility, bright phosphorescence at room temperature, and long excited state lifetime (~ 10 µs). The applications branched off to include testing of C. elegans survival when treated with the phosphor, which included lifespan and fecundity assays, toxicity assays including the determination of the LC50, and recovery after paraquat poisoning. Quenching experiments were performed using some well knows oxygen derivatives, and the quenching mechanisms were derived from Stern-Volmer plots. Reaction stoichiometries were derived from Job plots, while percent scavenging (or antioxidant) activities were determined graphically. The high photochemical reactivity of the complex was clearly manifested in these reactions.

Laser Ablation Inductively coupled plasma mass spectrometry (LA-ICP-MS) and Raman spectroscopy are both powerful imaging techniques. Their applications are numerous and extremely potential in the field of biology. In order to improve upon LA-ICP-MS an in-house built cold cell was developed and its effectiveness studied by imaging Brassica napus seeds. To further apply LA-ICP-MS and Raman imaging to the field of entomology a prong gilled mayfly (Ephemeroptera: Leptophlebiidae) from the Róbalo River, located on Navarino Island in Chile, was studied. Analysis of both samples showcased LA-ICP-MS and Raman spectroscopy as effective instruments for imaging trace elements and larger molecules in biological samples respectively.

The global transmission of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) has resulted in over 677 million infections and 6.88 million tragic deaths worldwide as of March 10th, 2023. During the pandemic, the ability to effectively combat SARS-CoV-2 had been hindered by the lack of rapid, reliable, and cost-effective testing platforms for readily screening patients, discerning incubation stages, and accounting for variants. The limited knowledge of the viral pathogenesis further hindered rapid diagnosis and long-term clinical management of this complex disease. While effective in the short term, measures such as social distancing and lockdowns have resulted in devastating economic loss, in addition to material and psychological hardships. Therefore, successfully reopening society during a pandemic depends on frequent, reliable testing, which can result in the timely isolation of highly infectious cases before they spread or become contagious. Viral loads, and consequently an individual's infectiousness, change throughout the progression of the illness. These dynamics necessitate frequent testing to identify when an infected individual can safely interact with non-infected individuals. Thus, scalable, accurate, and rapid serial testing is a cornerstone of an effective pandemic response, a prerequisite for safely reopening society, and invaluable for early containment of epidemics. Given the significant challenges posed by the pandemic, the power of artificial intelligence (AI) can be harnessed to create new diagnostic methods and be used in conjunction with serial tests. With increasing utilization of at-home lateral flow immunoassay (LFIA) tests, the National Institutes of Health (NIH) and Centers for Disease Control and Prevention (CDC) have consistently raised concerns about a potential underreporting of actual SARS-CoV-2-positive cases. When AI is paired with serial tests, it could instantly notify, automatically quantify, aid in real-time contact tracing, and assist in isolating infected individuals. Moreover, the computer vision-assisted methodology can help objectively diagnose conditions, especially in cases where subjective LFIA tests are employed. Recent advances in the interdisciplinary scientific fields of machine learning and biomedical engineering support a unique opportunity to design AI-based strategies for pandemic preparation and response. Deep learning algorithms are transforming the interpretation and analysis of image data when used in conjunction with biomedical imaging modalities such as MRI, Xray, CT scans, confocal microscopes, etc. These advances have enabled researchers to carry out real-time viral infection diagnostics that were previously thought to be impossible. The objective of this thesis is to use SARS-CoV-2 as a model virus and investigate the potential of applying multi-class instance segmentation deep learning and other machine learning strategies to build pandemic preparedness for rapid, in-depth, and longitudinal diagnostic platforms. This thesis encompasses three research tasks: 1) computer vision-assisted rapid serial testing, 2) infected cell phenotyping, and 3) diagnosing the long-term consequences of infection (i.e., long-term COVID). The objective of Task 1 is to leverage the power of AI, in conjunction with smartphones, to rapidly and simultaneously diagnose COVID-19 infections for millions of people across the globe. AI not only makes it possible for rapid and simultaneous screenings of millions but can also aid in the identification and contact tracing of individuals who may be carriers of the virus. The technology could be used, for example, in university settings to manage the entry of students into university buildings, ensuring that only students who test negative for the virus are allowed within campus premises, while students who test positive are placed in quarantine until they recover. The technology could also be used in settings where strict adherence to COVID-19 prevention protocols is compromised, for example, in an Emergency Room. This technology could also help with CDC’s concern on growing incidences of underreporting positive COVID-19 cases with growing utilization of at-home LFIA tests. AI can address issues that arise from relying solely on the visual interpretation of LFIA tests to make accurate diagnoses. One problem is that LFIA test results may be subjective or ambiguous, especially when the test line of the LFIA displays faint color, indicating a low analyte abundance. Therefore, reaching a decisive conclusion regarding the patient's diagnosis becomes challenging. Additionally, the inclusion of a secondary source for verifying the test results could potentially increase the test's cost, as it may require the purchase of complementary electronic gadgets. To address these issues, our innovation would be accurately calibrated with appropriate sensitivity markers, ensuring increased accuracy of the diagnostic test and rapid acquisition of test results from the simultaneous classification of millions of LFIA tests as either positive or negative. Furthermore, the designed network architecture can be utilized to detect other LFIA-based tests, such as early pregnancy detection, HIV LFIA detection, and LFIA-based detection of other viruses. Such minute advances in machine learning and artificial intelligence can be leveraged on many different scales and at various levels to revolutionize the health sector. The motivating purpose of Task 2 is to design a highly accurate instance segmentation network architecture not only for the analysis of SARS-CoV-2 infected cells but also one that yields the highest possible segmentation accuracy for all applications in biomedical sciences. For example, the designed network architecture can be utilized to analyze macrophages, stem cells, and other types of cells. Task 3 focuses on conducting studies that were previously considered computationally impossible. The invention will assist medical researchers and dentists in automatically calculating alveolar crest height (ACH) in teeth using over 500 dental Xrays. This will help determine if patients diagnosed with COVID-19 by a positive PCR test exhibited more alveolar bone loss and had greater bone loss in the two years preceding their COVID-positive test when compared to a control group without a positive COVID-19 test. The contraction of periodontal disease results in higher levels of transmembrane serine protease 2 (TMPRSS2) within the buccal cavity, which is instrumental in enabling the entry of SARS-CoV-2. Gum inflammation, a symptom of periodontal disease, can lead to alterations in the ACH of teeth within the oral mucosa. Through this innovation, we can calculate ACHs of various teeth and, therefore, determine the correlation between ACH and the risk of contracting SARS-CoV-2 infection. Without the invention, extensive manpower and time would be required to make such calculations and gather data for further research into the effects of SARS-CoV-2 infection, as well as other related biological phenomena within the human body. Furthermore, the novel network framework can be modified and used to calculate dental caries and other periodontal diseases of interest.

Indiana University-Purdue University Indianapolis (IUPUI) / Approximately 2.8% of the world population is currently infected with hepatitis C virus (HCV). Neutralizing antibodies (nAbs) are often generated in chronic hepatitis C patients yet fail to control the infection. In the first two chapters of this study, we focused on two alternative routes of HCV transmission, which may contribute to HCV’s immune evasion and establishment of chronic infection. HCV was transmitted via a cell-cell contact-mediated (CCCM) route and in the form of exosomes. Formation of HCV infection foci resulted from CCCM HCV transfer and was cell density-dependent. Moreover, CCCM HCV transfer occurred rapidly, involved all four known HCV receptors and intact actin cytoskeleton, and led to productive HCV infection. Furthermore, live cell imaging revealed the temporal and spatial details of the transfer process. Lastly, HCV from HCV-infected hepatocytes and patient plasma occurred in both exosome-free and exosome-associated forms and the exosome-associated HCV remained infectious, even though HCV infection did not significantly alter exosome secretion. In the third chapter, we characterized HCV interaction with astrocytes, one of the putative HCV target cells in the brain. HCV infection causes the central nervous system (CNS) abnormalities in more than 50% of chronically infected subjects but the underlying mechanisms are largely unknown. We showed that primary human astrocytes (PHA) were very inefficiently infected by HCV, either in the free virus form or through cell-cell contact. PHA expressed all known HCV receptors but failed to support HCV entry. HCV IRES-mediated translation was functional in PHA and further enhanced by miR122 expression. Nevertheless, PHA did not support HCV replication regardless of miR122 expression. To our great surprise, HCV exposure induced robust IL-18 expression in PHA and exhibited direct neurotoxicity. In summary, we showed that CCCM HCV transfer and exosome-mediated HCV infection constituted important routes for HCV infection and dissemination and that astrocytes did not support productive HCV infection and replication, but HCV interactions with astrocytes and neurons alone might be sufficient to cause CNS dysfunction. These findings provide new insights into HCV infection of hepatocytes and astrocytes and shall aid in the development of new and effective strategies for preventing and treating HCV infection.