Glucocorticoids (GCs) are a class of steroid hormone that play essential roles in development, glucose homeostasis, and reducing inflammation. Clinically, GCs are potent anti-inflammatory and immunosuppressive agents used to treat a variety of diseases. However, the therapeutic benefit of GCs is negatively impacted by the induction of severe side effects. In this thesis, I present two studies that have contributed to the understanding of the non-genomic actions of GCs. GCs inhibit cell migration by a non-transcriptional pathway involving HDAC6: A negative side effect of GC therapy is impaired wound healing which is ascribed to inhibited cell migration. Using live-cell microscopy, I show that GCs inhibit cell migration within 30 minutes of administration. GCs alter the dynamics of the microtubule network through rapid induction of tubulin acetylation (by inhibition of HDAC6) which increases microtubule stability and slows cell movement. The inhibitory effect of GCs on cell migration is reversed by overexpressing HDAC6. Using quantitative imaging, I identified a rapid ligand-dependent association of the GR and HDAC6 within the cytoplasm that is absent in the nucleus. However, a very small proportion of HDAC6 enters the nucleus post-GC treatment, suggesting that HDAC6 accompanies the GR during nuclear translocation. This study demonstrates that GCs rapidly inhibit cell migration by a non-transcriptional mechanism involving HDAC6. Investigating the rapid effects of GCs on the phosphoproteome: Non-steroidal GCs are useful tool compounds to dissect glucocorticoid receptor (GR) activity. Here, I investigated the early, rapid effect of GCs on the phosphoproteome of A549 cells using SILAC-based phosphoproteomics. A consistent spectrum of phosphoproteins was differentially regulated by GC within 10 minutes of administration, notably including regulators of RNA polymerase II, chromatin remodifying proteins, transcription factors, cytoskeletal modifiers, regulators of intracellular calcium signalling and endocytosis. These phosphoproteins were validated by western blotting. This study shows a clear early effect of GCs on the phosphoproteome with implications for non-specific, non-transcriptional activity of GCs.

This thesis presents two quantitative holographic reconstruction techniques for the imaging of dielectric targets. The first method is a quasi-real-time holographic reconstruction technique, which is capable of imposing physically based constraints on the real and imaginary parts of the permittivity. The other method is a real-time holographic reconstruction technique that is faster than the constrained method but cannot accommodate constraints on the reconstructed permittivity in its current form. The goal of this thesis is to introduce both methods and recommend which is best. Microwave holography has been used by our research group to reconstruct images of a target’s shape and location from microwave scattering parameters. This thesis will demonstrate that holography can be extended to quantify the permittivity distribution in a region of interest. The problems presented in this thesis are generic and are meant to show that near-field quantitative holography is a valid approach for applications such as tissue imaging, baggage inspection, concealed weapon detection, etc. The holographic inversion is carried out in the spectral domain (Fourier space), which allows for the use of Fourier transform properties to expedite the algorithm. This differs from sensitivity-based imaging (another inversion method developed by Tu et al. (2015)) where the inversion is performed in real space and is unable to take advantage of the techniques proposed in this thesis to improve the speed of reconstruction. Mutual coupling is not taken into consideration in the forward model of scattering used here; however, this technique is meant to be viewed as a foundation for a more sophisticated reconstruction algorithm, like the iterative Born method, which can overcome such limitations. Iterative reconstruction methods require an accurate initial guess, which can be provided by the quantitative technique presented in this thesis. Moreover, this technique, implementing fast and efficient linearized inversion, can serve as a module, which is called repetitively by the iterative algorithm. Such a module will take the current estimate of the total field distribution inside the imaged volume as an input and will return an estimate of complex permittivity distribution. / Thesis / Master of Applied Science (MASc)

The use of positron emission tomography (PET) has so far mainly been limited to a few nuclides with short half-lives such as 11C and 18F. Certain applications require nuclides with longer half-lives, such as 76Br and 52Fe. In radionuclide therapy positron emitting analogues of therapeutic nuclides, such as 110mIn, or daughter nuclides, such as 134La, can enable improved dosimetry with the use of PET. A challenge associated with the use of these positron emitters is that they emit gamma radiation in cascade with positrons, which complicates quantitative PET imaging. Other possible problems are the high energies of the emitted positrons, and the decay of 52Fe to the short-lived positron emitter 52mMn. Performance measurements were made to investigate the effects of these decay characteristics on the quantitative accuracy, spatial resolution, and other parameters of PET. The distribution of gamma radiation coincidences in PET data was studied and correction methods were implemented and evaluated. PET resolution degrades with 1-2 mm for the studied nuclides in comparison with 18F. The implemented sinogram tail fit and delayed coincidence based gamma radiation coincidence correction methods lead to a quantitative accuracy similar as for 18F and to improved image contrast. Standard dead time corrections are not adequate for gamma-emitting nuclides. Noise equivalent count rates are considerably lower for 76Br than for 18F at clinically relevant radioactivity concentrations. A method to correct 52Fe patient data for the contribution of 52mMn is discussed. The use of 110mIn is evaluated in a patient study and compared to SPECT imaging with 111In. A dosimetric and PET evaluation of the use of 134Ce/134La for radionuclide therapy and dosimetry is presented. Dosimetry of 76Br-labelled antibodies is studied in a pig model. Finally, the possibility to use PET for dosimetry during radionuclide therapy is studied and a nonuniform dose calculation program is presented.

Magnetic Resonance Imaging can now detect cells that are labeled with contrast agents such as superparamagnetic iron oxide (SPIO). Quantitative monitoring, which is desirable for evaluating cellular therapies, remains challenging. In this work, an MRI technique called TurboSPI is implemented for quantitative cellular imaging. TurboSPI acquires maps of the relaxation rate R2', which is directly related to SPIO concentration. Quantification of R2' is demonstrated using micron-sized iron oxide particles and SPIO-labeled cells. To explain experimental results that deviated from predicted behavior, an extended analytical description of MRI signal relaxation near SPIO was developed. This model compares well to Monte Carlo simulations and experimental data, and may allow improved quantification. The slow imaging speed of TurboSPI is overcome using a signal processing technique called compressed sensing to reconstruct undersampled data, enabling in vivo animal imaging with TurboSPI. Such images demonstrate detection of iron with improved specificity and good potential for quantification.

<p>The cross-sectional images of computed tomography (CT) are calculated from a series of projections using reconstruction methods. Recently introduced on clinical CT scanners, iterative reconstruction (IR) method enables potential patient dose reduction with significantly reduced image noise, but is limited by its "waxy" texture and nonlinear nature. To balance the advantages and disadvantages of IR, evaluations are needed with diagnostic accuracy as the endpoint. Moreover, evaluations need to take into consideration the type of the imaging task (detection and quantification), the properties of the task (lesion size, contrast, edge profile, etc.), and other acquisition and reconstruction parameters. </p><p>To evaluate detection tasks, the more acceptable method is observer studies, which involve image preparation, graphical user interface setup, manual detection and scoring, and statistical analyses. Because such evaluation can be time consuming, mathematical models have been proposed to efficiently predict observer performance in terms of a detectability index (d'). However, certain assumptions such as system linearity may need to be made, thus limiting the application of the models to potentially nonlinear IR. For evaluating quantification tasks, conventional method can also be time consuming as it usually involves experiments with anthropomorphic phantoms. A mathematical model similar to d' was therefore proposed for the prediction of volume quantification performance, named the estimability index (e'). However, this prior model was limited in its modeling of the task, modeling of the volume segmentation process, and assumption of system linearity.</p><p>To expand prior d' and e' models to the evaluations of IR performance, the first part of this dissertation developed an experimental methodology to characterize image noise and resolution in a manner that was relevant to nonlinear IR. Results showed that this method was efficient and meaningful in characterizing the system performance accounting for the non-linearity of IR at multiple contrast and noise levels. It was also shown that when certain criteria were met, the measurement error could be controlled to be less than 10% to allow challenging measuring conditions with low object contrast and high image noise.</p><p>The second part of this dissertation incorporated the noise and resolution characterizations developed in the first part into the d' calculations, and evaluated the performance of IR and conventional filtered backprojection (FBP) for detection tasks. Results showed that compared to FBP, IR required less dose to achieve a threshold performance accuracy level, therefore potentially reducing the required dose. The dose saving potential of IR was not constant, but dependent on the task properties, with subtle tasks (small size and low contrast) enabling more dose saving than conspicuous tasks. Results also showed that at a fixed dose level, IR allowed more subtle tasks to exceed a threshold performance level, demonstrating the overall superior performance of IR for detection tasks.</p><p>The third part of this dissertation evaluated IR performance in volume quantification tasks with conventional experimental method. The volume quantification performance of IR was measured using an anthropomorphic chest phantom and compared to FBP in terms of accuracy and precision. Results showed that across a wide range of dose and slice thickness, IR led to accuracy significantly different from that of FBP, highlighting the importance of calibrating or expanding current segmentation software to incorporate the image characteristics of IR. Results also showed that despite IR's great noise reduction in uniform regions, IR in general had quantification precision similar to that of FBP, possibly due to IR's diminished noise reduction at edges (such as nodule boundaries) and IR's loss of resolution at low dose levels. </p><p>The last part of this dissertation mathematically predicted IR performance in volume quantification tasks with an e' model that was extended in three respects, including the task modeling, the segmentation software modeling, and the characterizations of noise and resolution properties. Results showed that the extended e' model correlated with experimental precision across a range of image acquisition protocols, nodule sizes, and segmentation software. In addition, compared to experimental assessments of quantification performance, e' was significantly reduced in computational time, such that it can be easily employed in clinical studies to verify quantitative compliance and to optimize clinical protocols for CT volumetry.</p><p>The research in this dissertation has two important clinical implications. First, because d' values reflect the percent of detection accuracy and e' values reflect the quantification precision, this work provides a framework for evaluating IR with diagnostic accuracy as the endpoint. Second, because the calculations of d' and e' models are much more efficient compared to conventional observer studies, the clinical protocols with IR can be optimized in a timely fashion, and the compliance of clinical performance can be examined routinely.</p> / Dissertation

Mammographic breast density (MBD) is a strong risk factor for developing breast cancer. MBD is typically estimated by manually selecting the area occupied by the dense tissue on a mammogram. There is interest in measuring the volume of dense tissue, or volumetric breast density (VBD), as it could potentially be a stronger risk factor. This dissertation presents and validates an algorithm to measure the VBD from digital mammograms. The algorithm is based on an empirical calibration of the mammography system, supplemented by physical modeling of x-ray imaging that includes the effects of beam polychromaticity, scattered radation, anti-scatter grid and detector glare. It also includes a method to estimate the compressed breast thickness as a function of the compression force, and a method to estimate the thickness of the breast outside of the compressed region. The algorithm was tested on 26 simulated mammograms obtained from computed tomography images, themselves deformed to mimic the effects of compression. This allowed the determination of the baseline accuracy of the algorithm. The algorithm was also used on 55 087 clinical digital mammograms, which allowed for the determination of the general characteristics of VBD and breast volume, as well as their variation as a function of age and time. The algorithm was also validated against a set of 80 magnetic resonance images, and compared against the area method on 2688 images. A preliminary study comparing association of breast cancer risk with VBD and MBD was also performed, indicating that VBD is a stronger risk factor. The algorithm was found to be accurate, generating quantitative density measurements rapidly and automatically. It can be extended to any digital mammography system, provided that the compression thickness of the breast can be determined accurately.

Background: Dynamic contrast-enhanced MRI (DCE-MRI) has the potential to produce images of physiological quantities such as blood flow, blood vessel volume fraction, and blood vessel permeability. Such information is highly valuable, e.g., in oncology. The focus of this work was to improve the quantitative aspects of DCE-MRI in terms of better understanding of error sources and their effect on estimated physiological quantities. Methods: Firstly, a novel parameter estimation algorithm was developed to overcome a problem with sensitivity to the initial guess in parameter estimation with a specific pharmacokinetic model. Secondly, the accuracy of the arterial input function (AIF), i.e., the estimated arterial blood contrast agent concentration, was evaluated in a phantom environment for a standard magnitude-based AIF method commonly used in vivo. The accuracy was also evaluated in vivo for a phase-based method that has previously shown very promising results in phantoms and in animal studies. Finally, a method was developed for estimation of uncertainties in the estimated physiological quantities. Results: The new parameter estimation algorithm enabled significantly faster parameter estimation, thus making it more feasible to obtain blood flow and permeability maps from a DCE-MRI study. The evaluation of the AIF measurements revealed that inflow effects and non-ideal radiofrequency spoiling seriously degrade magnitude-based AIFs and that proper slice placement and improved signal models can reduce this effect. It was also shown that phase-based AIFs can be a feasible alternative provided that the observed difficulties in quantifying low concentrations can be resolved. The uncertainty estimation method was able to accurately quantify how a variety of different errors propagate to uncertainty in the estimated physiological quantities. Conclusion: This work contributes to a better understanding of parameter estimation and AIF quantification in DCE-MRI. The proposed uncertainty estimation method can be used to efficiently calculate uncertainties in the parametric maps obtained in DCE-MRI.

abstract: Accurate quantitative information of tumor/lesion volume plays a critical role in diagnosis and treatment assessment. The current clinical practice emphasizes on efficiency, but sacrifices accuracy (bias and precision). In the other hand, many computational algorithms focus on improving the accuracy, but are often time consuming and cumbersome to use. Not to mention that most of them lack validation studies on real clinical data. All of these hinder the translation of these advanced methods from benchside to bedside. In this dissertation, I present a user interactive image application to rapidly extract accurate quantitative information of abnormalities (tumor/lesion) from multi-spectral medical images, such as measuring brain tumor volume from MRI. This is enabled by a GPU level set method, an intelligent algorithm to learn image features from user inputs, and a simple and intuitive graphical user interface with 2D/3D visualization. In addition, a comprehensive workflow is presented to validate image quantitative methods for clinical studies. This application has been evaluated and validated in multiple cases, including quantifying healthy brain white matter volume from MRI and brain lesion volume from CT or MRI. The evaluation studies show that this application has been able to achieve comparable results to the state-of-the-art computer algorithms. More importantly, the retrospective validation study on measuring intracerebral hemorrhage volume from CT scans demonstrates that not only the measurement attributes are superior to the current practice method in terms of bias and precision but also it is achieved without a significant delay in acquisition time. In other words, it could be useful to the clinical trials and clinical practice, especially when intervention and prognostication rely upon accurate baseline lesion volume or upon detecting change in serial lesion volumetric measurements. Obviously, this application is useful to biomedical research areas which desire an accurate quantitative information of anatomies from medical images. In addition, the morphological information is retained also. This is useful to researches which require an accurate delineation of anatomic structures, such as surgery simulation and planning. / Dissertation/Thesis / Doctoral Dissertation Biomedical Informatics 2016

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Les cellules vivantes sont capables de réagir aux signaux mécaniques tels que la rigidité de la surface sur laquelle elles adhèrent, les forces de tractions ou compressions auxquelles elles sont soumises, le flux de liquide à la surface de leur membrane ou encore la géométrie de leurs adhésions ou de leur forme globale. Ces signaux influent sur des processus cellulaires tels que la prolifération, la différenciation, la migration et la mort cellulaire. Ces processus sont finement régulés par des réactions biochimiques qui forment un réseau de signalisation. La mécanotransduction est la traduction du signal mécanique en signal biochimique.C’est dans le but d’étudier la mécanotransduction que nous avons étudié l’utilisation d’ultrasons pour stimuler mécaniquement les cellules à des fréquences temporelles et spatiales relativement élevées. De nombreux montages expérimentaux et de nombreuses voies ont été considérées dans cette partie très exploratoire. Nous en retenons finalement des pistes prometteuses pour la continuation future de ce projet.Nous avons développé ce que nous nommons des substrats actifs, qui nous permettent de contrôler à la fois spatialement et temporellement la stimulation mécanique appliquée à des cellules vivantes. Ces substrats actifs consistent en des micropiliers de fer incrustés dans un élastomère peu rigide (PDMS) et manipulés par deux électroaimants. Nous pouvons contrôler dynamiquement le déplacement des piliers qui vont déformer localement et de manière continue la surface. Cette déformation va ensuite déformer en traction ou en compression les cellules vivantes étalées sur la surface à proximité. En employant des marqueurs fluorescents nous pouvons réaliser de la Microscopie de Forces de Traction et surveiller la contrainte appliquée par les piliers aux cellules à travers la surface de PDMS, et nous pouvons étudier la réponse mécanique des cellules. De plus, ces substrats sont compatibles avec la microscopie de fluorescence en cellule vivante, ce qui rend possible l’observation de la réponse cellulaire au niveau morphologique (forme des adhésions focales, activité protrusive, …) et surtout biochimique.En effet, pour étudier la réponse biochimique des cellules après une stimulation mécanique, nous observons par microscopie de fluorescence des biosenseurs portant des paires de fluorophores donneur/accepteur. Ces biosenseurs nous permettent d’observer l’activité de protéines impliquées dans la signalisation cellulaire en calculant l’efficacité de Transfert d’Énergie Résonnant de Förster (FRET) de ces biosenseurs. Pour ce faire, les échantillons sont illuminés alternativement aux longueurs d’ondes d’excitation des fluorophores donneurs puis accepteurs. Le signal de fluorescence est collecté simultanément dans un canal d’émission du donneur et un canal d’émission de l’accepteur. Une grande partie de ma thèse a été consacrée à la mise au point d’une méthode quantitative pour analyser les images de fluorescence afin de mesurer une efficacité de FRET qui ne dépende pas de facteurs expérimentaux ni de la quantité de biosenseurs présents dans les cellules. Nous évaluons alors les différentes méthodes pour déterminer les facteurs de correction répandus corrigeant le débordement de spectre du donneur dans le canal accepteur et l’excitation directe de l’accepteur à la longueur d’onde d’excitation du donneur. Pour obtenir des mesures plus quantitatives, nous avons mis au point une nouvelles méthode pour déterminer 2 facteurs de correction supplémentaires. Nous comparons cette méthode à la seule préexistante et évaluons l’influence des paramètres de traitement des images sur les valeurs d’efficacité de FRET mesurées. / Living cells can react to mechanical signals such as the rigidity of the surface they adhere on, the traction or compression forces applied on them, the liquid flow at their membrane surface or the geometry of their adhesions or of their overall shape. Those signals influence cellular processes such as proliferation, differentiation, migration or cell death. Those processes are tightly regulated by biochemical reactions that constitute a signaling network. Mechanotransduction is the translation of the mechanical signal into the biochemical one.In order to study mechanotransduction, we have considered the use of ultrasounds to mechanically stimulate cells at relatively high temporal and spatial frequencies. Numerous setups and options have been considered in this very exploratory project. Finally, we will retain some promising leads for the continuation of this project.We have developed what we call active substrates that allows us to control both spatially and temporally the mechanical stimulation on living cells. Those active substrates consist of iron micropillars embedded in a soft elastomer and actuated by 2 electromagnets. We can control dynamically the displacement of the pillar that will deform locally and continuously the surface. This deformation will then deform in traction or in compression the living cells spread on the surface nearby. Thanks to fluorescent trackers we can perform Traction Force Microscopy and monitor the stress applied by the pillars to the cells through the PDMS surface, and we can look at the mechanical response of the cells. Moreover, those substrates are compatible with live cell fluorescence microscopy, which makes possible the observation of the cellular response at the morphological level (focal adhesions, protrusive activity, …) and most importantly at the biochemical level.Indeed, in order to study the cellular biochemical response after a mechanical stimulation, we use fluorescence microscopy to observe biosensors containing pairs of donor/acceptor fluorophores. Those biosensors allow us to monitor the activity of proteins implied in cellular signaling by computing the Förster Resonance Energy Transfer (FRET) efficiency of those biosensors. To do so, samples are alternatively excited at donor and acceptor excitation wavelengths. The fluorescence signal is then simultaneously measured in donor and acceptor emission channels. A substantial part of my thesis has been dedicated to the development of a quantitative method to analyze fluorescence images in order to measure FRET efficiencies that do not depend on experimental factors or biosensors concentration in cells. We assess different methods to compute standard correction factors that account for spectral bleed-through and direct excitation of acceptors at donor excitation wavelength. To obtain more quantitative measurements, we have developed a new method to compute 2 additional correction factors. We compare this method with the only one preexisting, and we assess the influence of image processing parameters on FRET efficiency values.