Health is a fundamental human right. To increase global health, research in the medical sector is of great importance. Decreasing time consumption of biomedical testing could accelerate the research and development of new drugs and vaccines. This could be achieved by automation of biomedical analysis, using computerized methods. In order to perform analysis on pictures of biomedical tests, it is important to identify the area of interest (AOI) of the test. For example, cells and bacteria are commonly grown in petri dishes, in this case the AOI is the bottom area of the dish, since this is where the object of analysis is located.This study was performed with the aim to compare a few computerized methods for identifying the AOI in pictures of biomedical tests. In the study, biomedical images from a testing method called ELISpot have been used. ELISpot uses plates with up to 96 circular wells, where pictures of the separate wells were used in order to find the AOI corresponding to the bottom area of each well. The focus has been on comparing the performance of three edge detection methods. More specifically, their ability to accurately detect the edges of the well. Furthermore, a method for identifying a circle based on the detected edges was used to specify the AOI.The study shows that methods using second order derivatives for edge detection, gives the best results regarding to robustness.

<p>  </p> <p>Cardiovascular disease has been the leading cause of death in the United States for over 70 years. To evaluate the extent and progression of cardiovascular disease, non-invasive imaging techniques are frequently used clinically and pre-clinically. Current echocardiographic and cine magnetic resonance approaches rely on measurements that are typically obtained from two-dimensional images, which assumes uniformity of the structure being evaluated. To explore methods to potentially address these shortcomings, our group has developed and validated high frequency four-dimensional ultrasound techniques as well as created a software toolbox that allows for measurement of myocardial kinematics. In this thesis, I assisted in the application of these methods to two murine models of disease states: myocardial infarction and aortic aneurysm. Another study I aided in focused on cardiac magnetic resonance imaging data from patients with Duchenne muscular dystrophy. From our software, we are able to obtain various strain and strain rate estimates that reveal significant functional changes in infarction and Duchenne muscular dystrophy earlier than standard measurement techniques. Furthermore, we are able to identify vascular expansion, transmural thickening, and changes in hemodynamics prior to aneurysm development. Earlier detection and localization allows for more targeted surveillance and interventions, which ultimately may result in improved clinical outcomes. Ideally, these findings can be used to expand the capabilities of cardiac research and the development of clinically applicable imaging techniques and treatments to better address underlying cardiovascular pathophysiology. </p>

<p>This dissertation details the creation, validation, and characterization of a porcine ex-vivo culture model to study subcuteneous tissue. The viability of the model was assessed over seven days of culture by digestion and the proliferation and death of cells was monitored by immunohistochmeical labelling and image analysis. The model was then used in a timecourse proteomics experiment to characterize the effect of culture on subcutaneous proteome. The model was then compared to a commercially available human ex-vivo model with respect to viability and changes to the subcutaneous proteome. </p>

No description available.

<p dir="ltr">Progress in deep learning methodologies has transformed the landscape of medical image analysis, opening fresh pathways for precise and effective diagnostics. Currently, multi-phase liver CT scans follow a four-stage process, commencing with an initial scan carried out before the administration of <a href="" target="_blank">intravenous (IV) contrast-enhancing material</a>. Subsequently, three additional scans are performed following the contrast injection. The primary objective of this research is to automate the analysis and prediction of 50% of liver CT scans. It concentrates on discerning patterns of intensity change during the second, third, and fourth phases concerning the initial phase. The thesis comprises two key sections. The first section employs the non-contrast phase (first scan), late hepatic arterial phase (second scan), and portal venous phase (third scan) to predict the delayed phase (fourth scan). In the second section, the non-contrast phase and late hepatic arterial phase are utilized to predict both the portal venous and delayed phases. The study evaluates the performance of two deep learning models, U-Net and U²-Net. The process involves preprocessing steps like subtraction and normalization to compute contrast difference images, followed by post-processing techniques to generate the predicted 2D CT scans. Post-processing steps have similar techniques as in preprocessing but are performed in reverse order. Four fundamental evaluation metrics, including <a href="" target="_blank">Mean Absolute Error (MAE), Signal-to-Reconstruction Error Ratio (SRE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM), </a>are employed for assessment. Based on these evaluation metrics, U²-Net performed better than U-Net for the prediction of both portal venous (third) and delayed (fourth) phases. Specifically, U²-Net exhibited superior MAE and PSNR results for the predicted third and fourth scans. However, U-Net did show slightly better SRE and SSIM performance in the predicted scans. On the other hand, for the exclusive prediction of the fourth scan, U-Net outperforms U²-Net in all four evaluation metrics. This implementation shows promising results which will eliminate the need for additional CT scans and reduce patients’ exposure to harmful radiation. Predicting 50% of liver CT volumes will reduce exposure to harmful radiation by half. The proposed method is not limited to liver CT scans and can be applied to various other multi-phase medical imaging techniques, including multi-phase CT angiography, multi-phase renal CT, contrast-enhanced breast MRI, and more.</p>

<p>Biotherapeutics, such as monoclonal antibodies (mAbs), represent a primary mechanism for treatment of human disease, and there has been a steady increase in Food and Drug Administration approvals since the first one in 1982. Subcutaneous (SC) injection of protein-based therapeutics is a convenient and clinically established drug delivery method that increases the convenience and reduces cost compared to other delivery methods. However, progress is needed to optimize bioavailability via this route. This dissertation describes the methods for evaluation of mass transport of high molecular weight proteins, such as mAbs, following SC injection using <em>in vitro</em> and <em>ex vivo</em> modeling developed to describe the factors relevant for optimal distribution prior to uptake into systemic circulation. The first chapter describes a novel collagen and hyaluronic acid (HA) based hydrogel for investigation of macromolecule transport based on the physiochemical properties of the diffusing molecule and the tissue matrix. This initial study demonstrated that, in collagen alone, collagen combined with HA, and HA alone, the molecules demonstrated different transport paradigms dependent primarily on molecule size, matrix viscosity, and electrostatic charge, respectively. This showed that the local tissue heterogeneity and therapeutic properties could be determining factors for molecule transport and bioavailability. The second, third, and fourth chapters describe two novel platforms for the investigation of injection plume formation in SC tissue utilizing three-dimensional X-ray tomography. Injection plume analysis has been studied comprehensively in the context of insulin transport using co-injection of radiopaque dyes to track the protein distribution. However high molecular weight therapeutics have vastly different physiochemical properties than insulin and are injected under different rates, concentrations, volumes, and viscosities due to dosing considerations. To address the gap mAb distribution, we first developed a novel protein conjugated to an x-ray contrast agent to directly track injection plume formation and investigated the effects of injection rate and tissue location through injections into ex vivo porcine tissue, described in chapters three and four. Ex vivo tissue analysis showed that the rate did not influence the distribution, however, plume volume was lower in porcine belly compared to neck tissue. Whereas porcine tissue is an excellent model to represent the structural features of human injection, the large heterogeneity between animal subjects and collected samples is a disadvantage. Therefore, the fourth chapter describes the fabrication of a gelatin hydrogel-based injection platform representing the dermal and subcutaneous tissue layers for controlled injection plume analysis. In summary, all three models represent useful platforms for the assessment of macromolecular mass transport, pharmaceutical autoinjector performance, as well as the potential impact of tissue properties and intersubject heterogeneity on plume formation. Overall, the findings in these studies might better inform drug designers and clinicians on how to most optimally engineer an injection to deliver the most efficient patient outcomes through better dosing and increased cost savings. </p>

<p dir="ltr">This thesis describes advancements in both chemical imaging and optical manipulation methodologies for their application in tandem monitoring and control of biochemical processes. We developed a fast acquisition multimodal nonlinear imaging platform based on pulse-picking to minimize photoperturbation to the sample during imaging. By frequency doubling the imaging source, through acousto-optic modulation and simple comparator circuitry, we developed a comprehensive platform that uses chemical specific signals generated during imaging to control the pixel location for laser activation for reaction control. This feedback loop allows for advanced decision logic on a pixel by pixel basis.</p>

<p dir="ltr">In this dissertation, efforts and innovations are made to advance subtle pattern mining, edge computing, and system efficiency optimization for biomedical applications, thereby advancing precision medicine big data.</p><p dir="ltr">Brain visual dynamics encode rich functional and biological patterns of the neural system, promising for applications like intention decoding, cognitive load quantization and neural disorder measurement. We here focus on the understanding of the brain visual dynamics for the Amyotrophic lateral sclerosis (ALS) population. We leverage a deep learning framework for automatic feature learning and classification, which can translate the eye Electrooculography (EOG) signal to meaningful words. We then build an edge computing platform on the smart phone, for learning, visualization, and decoded word demonstration, all in real-time. In a further study, we have leveraged deep transfer learning to boost EOG decoding effectiveness. More specifically, the model trained on basic eye movements is leveraged and treated as an additional feature extractor when classifying the signal to the meaningful word, resulting in higher accuracy.</p><p dir="ltr">Efforts are further made to decoding functional Near-Infrared Spectroscopy (fNIRS) signal, which encodes rich brain dynamics like the cognitive load. We have proposed a novel Multi-view Multi-channel Graph Neural Network (mmGNN). More specifically, we propose to mine the multi-channel fNIRS dynamics with a multi-stage GNN that can effectively extract the channel- specific patterns, propagate patterns among channels, and fuse patterns for high-level abstraction. Further, we boost the learning capability with multi-view learning to mine pertinent patterns in temporal, spectral, time-frequency, and statistical domains.</p><p dir="ltr">Massive-device systems, like wearable massive-sensor computers and Internet of Things (IoTs), are promising in the era of big data. The crucial challenge is about how to maximize the efficiency under coupling constraints like energy budget, computing, and communication. We propose a deep reinforcement learning framework, with a pattern booster and a learning adaptor. This framework has demonstrated optimally maximizes the energy utilization and computing efficiency on the local massive devices under a one-center fifteen-device circumstance.</p><p dir="ltr">Our research and findings are expected to greatly advance the intelligent, real-time, and efficient big data harnessing, leveraging deep learning, edge computing, and efficiency optimization.</p>

Magnetresonanztomographie (MRT) ist eine nichtinvasive Bildgebungsmethode, die in der Medizin sowie in der Forschung eingesetzt wird und auf der magnetischen Kernresonanz beruht. Die Erforschung der Ultrahochfeld (UHF) MRT ab Magnetfeldstärken von 7.0 Tesla und darüber ist durch einen intrinsischen Signalgewinn hin zu hohen Magnetfeldstärken motiviert und beschäftigt sich mit den dabei auftretenden physikalischen Effekten ebenso wie mit den dazu notwendigen neuartigen Technologien. Die vorliegende Arbeit untersucht Mehrkanalantennen zur Anregung der magnetischen Kernresonanz sowie zum Empfang des resultierenden Signals bei 7.0 T. Für die magnetische Kernresonanz von Protonen ergibt sich eine Resonanzfrequenz von 300 MHz. Die zugehörige Wellenlänge in menschlichem Gewebe verlässt in diesem Frequenzbereich im Verhältnis zu den Körperabmessungen den quasistatischen Bereich. Die sich ergebende Wellenausbreitung hat Interferenzmuster in den erzeugten Bildern zur Folge, die zu klinisch nicht verwertbaren Bildinformationen führen können. Vor diesem Hintergrund wurden in dieser Arbeit Mehrkanalantennen mit 4, 8 und 16 unabhängigen Elementen zur Signalanregung und zum Empfang konzipiert, aufgebaut und untersucht. Die Erkenntnisse mündeten in der erfolgreichen Implementierung der weltweit ersten 32-Kanal Antenne zur kardiovaskulären Bildgebung bei 7.0 T. Darüber hinaus wurde eine Antenne entwickelt, welche die ersten auf der Natriumkonzentration beruhenden bewegten MRT Bilder des menschlichen Herzens bei 7.0 T ermöglichte. Der Zusammenhang zwischen Natriumkonzentration und Zellintegrität ermöglicht direkte und ortsaufgelöste Einblicke in physiologische Prozesse. Die Ergebnisse dieser Arbeit belegen die breite Anwendbarkeit von Mehrkanalantennen in der UHF MRT zur Protonen-und Natriumbildgebung und bilden eine solide technologische Basis für breitere klinische Studien, um die Ultrahochfeld MRT reif für den routinemäßigen Einsatz im Gesundheitswesen zu machen. / Magnetic resonance imaging (MRI) is a non-invasive imaging method based on the effect of nuclear magnetic resonance. It is used in healthcare as well as in research. MRI at magnetic field strengths of 1.5 Tesla and 3 Tesla is well established. The gain in signal-to-noise ratio (SNR) intrinsic to higher magnetic field strength fuels the vigorous research field of Ultrahigh field (UHF) MRI at 7.0 T and above. Nevertheless for MRI based upon proton imaging the wavelength of the transmitted electro-magnetic fields slowly departs from the semi-static regime and reaches the dimension of the transection of the human body at 7.0 T. This gives rise to constructive and destructive interferences that potentially render image quality non-diagnostic for clinical use. Therefore is work proposes the worlds’ first 32 channel antenna array for cardiovascular MRI at 7.0 T. Electro-magnetic field simulations are utilized to study the capabilities of multi-channel RF antenna arrays to mitigate destructive interferences and provided the basis for a workflow towards homogenization of the electromagnetic radio-frequency field. Pre-clinical studies showed the capabilities and limits of translating the SNR gain of UHF MRI into clinical beneficial numbers, namely increased spatial or temporal resolution or scan time shortening. To make further use of the benefits of UHR MRI and to make a step towards first-hand spatial resolved information of biological processes in human tissue sodium imaging of the human heart was enabled with the design of a tailored antenna array. The results were reconstructed into the first movies of the human heart at 7.0 T based on sodium signal. This profound technological basis for radio frequency excitation and reception in UHF MRI can be expected to pave the way for broader clinical studies at 7.0 T with the ultimate goal to improve the quality and the earliness of treatment decisions in future clinical practice.

La tomographie de cohérence optique plein champ est une technique de microscopie permettant d’imager un plan d’intérêt en profondeur dans un milieu diffusant. Cette technique a été utilisée pour l’examen de pièces opératoires dans un but de diagnostic en cancérologie. L’utilisation de cette technique permettrait en effet de fournir un outil de diagnostic peropératoire rapide et fiable, évitant ainsi de nombreuses procédures de réopération. Ces réopérations peuvent survenir lorsque – lors du diagnostic final par analyse de coupes histologiques – le pathologiste décèle la présence de tissus cancéreux restant, non retirés au cours de l’opération.L’OCT plein champ a montré de bons résultats pour cette application. Néanmoins, cette technique ne fournit qu’un contraste morphologique des tissus, ne permettant pas d’utiliser des critères de qualification des pièces opératoires basées – par exemple – sur la morphologie ou la densité cellulaire.Nous avons développé une nouvelle modalité d’imagerie basée sur l’OCT plein champ permettant de révéler un contraste métabolique dans le tissu à une échelle subcellulaire. Ce contraste permet de révéler les cellules précédemment non distinguées en OCT plein champ. Nous avons également utilisé la mesure quantitative de cette modalité pour réaliser des outils d’aide au diagnostic utilisant des approches d’apprentissage par ordinateur. / Full filed optical coherence tomography is a microscopy imaging technique allowing to image a specific slice in a scattering medium, in depth. This technique has been used for the diagnosis of biopsy in cancerology. This technique could be an efficient and fast way to diagnose excised tissues during surgery. This would avoid numerous reoperations procedures. These reoperations are necessary when a pathologist suspects cancerous tissue to still be present in the patient, based on histological slide examination.FFOCT has shown promising results for that purpose. Nevertheless, this technique only gives a morphological contrast of tissues, which is not enough for applying some diagnostic criteria such as cell morphology or cell density.We developed a new imaging modality based on FFOCT allowing to reveal metabolic contrast in tissues at the subcellular scale. This contrast reveals cells previously indistinguishable with FFOCT. We also used this quantitative metric to propose tools to facilitate diagnosis, using machine learning approaches.