503 research outputs found
Visual Quality Enhancement in Optoacoustic Tomography using Active Contour Segmentation Priors
Segmentation of biomedical images is essential for studying and
characterizing anatomical structures, detection and evaluation of pathological
tissues. Segmentation has been further shown to enhance the reconstruction
performance in many tomographic imaging modalities by accounting for
heterogeneities of the excitation field and tissue properties in the imaged
region. This is particularly relevant in optoacoustic tomography, where
discontinuities in the optical and acoustic tissue properties, if not properly
accounted for, may result in deterioration of the imaging performance.
Efficient segmentation of optoacoustic images is often hampered by the
relatively low intrinsic contrast of large anatomical structures, which is
further impaired by the limited angular coverage of some commonly employed
tomographic imaging configurations. Herein, we analyze the performance of
active contour models for boundary segmentation in cross-sectional optoacoustic
tomography. The segmented mask is employed to construct a two compartment model
for the acoustic and optical parameters of the imaged tissues, which is
subsequently used to improve accuracy of the image reconstruction routines. The
performance of the suggested segmentation and modeling approach are showcased
in tissue-mimicking phantoms and small animal imaging experiments.Comment: Accepted for publication in IEEE Transactions on Medical Imagin
Measuring cellular traction forces on non-planar substrates
Animal cells use traction forces to sense the mechanics and geometry of their
environment. Measuring these traction forces requires a workflow combining cell
experiments, image processing and force reconstruction based on elasticity
theory. Such procedures have been established before mainly for planar
substrates, in which case one can use the Green's function formalism. Here we
introduce a worksflow to measure traction forces of cardiac myofibroblasts on
non-planar elastic substrates. Soft elastic substrates with a wave-like
topology were micromolded from polydimethylsiloxane (PDMS) and fluorescent
marker beads were distributed homogeneously in the substrate. Using feature
vector based tracking of these marker beads, we first constructed a hexahedral
mesh for the substrate. We then solved the direct elastic boundary volume
problem on this mesh using the finite element method (FEM). Using data
simulations, we show that the traction forces can be reconstructed from the
substrate deformations by solving the corresponding inverse problem with a
L1-norm for the residue and a L2-norm for 0th order Tikhonov regularization.
Applying this procedure to the experimental data, we find that cardiac
myofibroblast cells tend to align both their shapes and their forces with the
long axis of the deformable wavy substrate.Comment: 34 pages, 9 figure
Pigment Melanin: Pattern for Iris Recognition
Recognition of iris based on Visible Light (VL) imaging is a difficult
problem because of the light reflection from the cornea. Nonetheless, pigment
melanin provides a rich feature source in VL, unavailable in Near-Infrared
(NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical
not stimulated in NIR. In this case, a plausible solution to observe such
patterns may be provided by an adaptive procedure using a variational technique
on the image histogram. To describe the patterns, a shape analysis method is
used to derive feature-code for each subject. An important question is how much
the melanin patterns, extracted from VL, are independent of iris texture in
NIR. With this question in mind, the present investigation proposes fusion of
features extracted from NIR and VL to boost the recognition performance. We
have collected our own database (UTIRIS) consisting of both NIR and VL images
of 158 eyes of 79 individuals. This investigation demonstrates that the
proposed algorithm is highly sensitive to the patterns of cromophores and
improves the iris recognition rate.Comment: To be Published on Special Issue on Biometrics, IEEE Transaction on
Instruments and Measurements, Volume 59, Issue number 4, April 201
Magnetic Doppler imaging of alpha^2 Canum Venaticorum in all four Stokes parameters. Unveiling the hidden complexity of stellar magnetic fields
Strong organized magnetic fields have been studied in the upper main sequence
chemically peculiar stars for more than half a century. However, only recently
have observational methods and numerical techniques become sufficiently mature
to allow us to record and interpret high-resolution four Stokes parameter
spectra, leading to the first assumption-free magnetic field models of these
stars. Here we present a detailed magnetic Doppler imaging analysis of the
spectropolarimetric observations of the prototypical magnetic Ap star alpha^2
CVn. The surface abundance distributions of Fe and Cr and a full vector map of
the stellar magnetic field are reconstructed in a self-consistent inversion
using our state-of-the-art magnetic Doppler imaging code Invers10. We succeeded
in reproducing most of the details of the available spectropolarimetric
observations of alpha^2 CVn with a magnetic map which combines a global
dipolar-like field topology with localized spots of higher field intensity. We
demonstrate that these small-scale magnetic structures are inevitably required
to fit the linear polarization spectra; however, their presence cannot be
inferred from the Stokes I and V observations alone. Our magnetic Doppler
imaging analysis of alpha^2 CVn and previous results for 53 Cam support the
view that the upper main sequence stars can harbour fairly complex surface
magnetic fields which resemble oblique dipoles only at the largest spatial
scales. Spectra in all four Stokes parameters are absolutely essential to
unveil and meaningfully characterize this field complexity in Ap stars. We
therefore suggest that understanding magnetism of stars in other parts of the
H-R diagram is similarly incomplete without investigation of their linear
polarization spectra.Comment: 16 pages, 12 figures; Accepted for publication by Astronomy &
Astrophysic
Characterization of vertical cracks using lock-in vibrothermography
214 p.Esta tesis se centra en la aplicación de la vibrotermografÃa lock-in para la detección y caracterización dedefectos verticales sumergidos. En esta técnica, la pieza se excita mediante ultrasonidos, que generancalor en los defectos por fricción o deformación plástica. Este calor se difunde por el material y susefectos se pueden detectar midiendo la temperatura superficial mediante una cámara infrarroja. Con el finde caracterizar defectos es necesario resolver el problema inverso, que consiste en recuperar la geometrÃade las fuentes de calor a partir de la distribución de temperatura superficial medida. Éste es un problemamal puesto, ya que su solución es fuertemente dependiente de pequeños errores en los datos y la inversiónes inestable. Se ha implementado un algoritmo de inversión robusto, basado en minimización pormÃnimos cuadrados estabilizados mediante términos de penalización basados en los funcionales deTikhonov, Total Variation y L1, capaz de reconstruir distribuciones de fuentes de calor partiendo de datosde vibrotermografÃa. El algoritmo se ha analizado con datos sintéticos y se ha optimizado con el fin deextender su aplicación a la caracterización del mayor rango de geometrÃas de fuentes de calor posible.Los resultados obtenidos se han verificado con datos experimentales obtenidos en ensayos devibrotermografÃa lock-in, utilizando muestras con fuentes de calor verticales calibradas. Finalmente, se hahecho uso del algoritmo de inversión para caracterizar grietas reales en una muestra soldada de Inconel718 y los resultados están en buena correlación cualitativa con los resultados del ensayo de lÃquidospenetrantes realizado posteriormente
Doctor of Philosophy
dissertationInverse Electrocardiography (ECG) aims to noninvasively estimate the electrophysiological activity of the heart from the voltages measured at the body surface, with promising clinical applications in diagnosis and therapy. The main challenge of this emerging technique lies in its mathematical foundation: an inverse source problem governed by partial differential equations (PDEs) which is severely ill-conditioned. Essential to the success of inverse ECG are computational methods that reliably achieve accurate inverse solutions while harnessing the ever-growing complexity and realism of the bioelectric simulation. This dissertation focuses on the formulation, optimization, and solution of the inverse ECG problem based on finite element methods, consisting of two research thrusts. The first thrust explores the optimal finite element discretization specifically oriented towards the inverse ECG problem. In contrast, most existing discretization strategies are designed for forward problems and may become inappropriate for the corresponding inverse problems. Based on a Fourier analysis of how discretization relates to ill-conditioning, this work proposes refinement strategies that optimize approximation accuracy o f the inverse ECG problem while mitigating its ill-conditioning. To fulfill these strategies, two refinement techniques are developed: one uses hybrid-shaped finite elements whereas the other adapts high-order finite elements. The second research thrust involves a new methodology for inverse ECG solutions called PDE-constrained optimization, an optimization framework that flexibly allows convex objectives and various physically-based constraints. This work features three contributions: (1) fulfilling optimization in the continuous space, (2) formulating rigorous finite element solutions, and (3) fulfilling subsequent numerical optimization by a primal-dual interiorpoint method tailored to the given optimization problem's specific algebraic structure. The efficacy o f this new method is shown by its application to localization o f cardiac ischemic disease, in which the method, under realistic settings, achieves promising solutions to a previously intractable inverse ECG problem involving the bidomain heart model. In summary, this dissertation advances the computational research of inverse ECG, making it evolve toward an image-based, patient-specific modality for biomedical research
Reconstruction and Simulation of Cellular Traction Forces
Biological cells are able to sense the stiffness, geometry and topography of their environment
and sensitively respond to it. For this purpose, they actively apply contractile forces to the
extracellular space, which can be determined by traction force microscopy. Thereby cells
are cultured on elastically deformable substrates and cellular traction patterns are quanti-
tatively reconstructed from measured substrate deformations, by solving the inverse elastic
problem. In this thesis we investigate the influence of environmental topography to cellular
force generation and the distribution of intracellular tension. For this purpose, we reconstruct
traction forces on wavy elastic substrates, using a novel technique based on finite element
methods. In order to relate forces to single cell-matrix contacts and different structures of
the cytoskeleton, we then introduce another novel variant of traction force microscopy, which
introduces cell contraction modeling into the process of cellular traction reconstruction. This
approach is robust against experimental noise and does not need regularisation. We apply
this method to experimental data to demonstrate that different types of actin fibers in the
cell statistically show different contractilities. We complete our investigation by simulation
studies considering cell colonies and single cells as thermoelastically contracting continuum
coupled to an elastic substrate. In particular we examined the effect of geometry on cellular
behavior in collective cell migration and tissue invasion during tumor metastasis
Uncertainty Quantification and Reduction in Cardiac Electrophysiological Imaging
Cardiac electrophysiological (EP) imaging involves solving an inverse problem that infers cardiac electrical activity from body-surface electrocardiography data on a physical domain defined by the body torso. To avoid unreasonable solutions that may fit the data, this inference is often guided by data-independent prior assumptions about different properties of cardiac electrical sources as well as the physical domain. However, these prior assumptions may involve errors and uncertainties that could affect the inference accuracy. For example, common prior assumptions on the source properties, such as fixed spatial and/or temporal smoothness or sparseness assumptions, may not necessarily match the true source property at different conditions, leading to uncertainties in the inference. Furthermore, prior assumptions on the physical domain, such as the anatomy and tissue conductivity of different organs in the thorax model, represent an approximation of the physical domain, introducing errors to the inference. To determine the robustness of the EP imaging systems for future clinical practice, it is important to identify these errors/uncertainties and assess their impact on the solution. This dissertation focuses on the quantification and reduction of the impact of uncertainties caused by prior assumptions/models on cardiac source properties as well as anatomical modeling uncertainties on the EP imaging solution.
To assess the effect of fixed prior assumptions/models about cardiac source properties on the solution of EP imaging, we propose a novel yet simple Lp-norm regularization method for volumetric cardiac EP imaging. This study reports the necessity of an adaptive prior model (rather than fixed model) for constraining the complex spatiotemporally changing properties of the cardiac sources. We then propose a multiple-model Bayesian approach to cardiac EP imaging that employs a continuous combination of prior models, each re-effecting a specific spatial property for volumetric sources. The 3D source estimation is then obtained as a weighted combination of solutions across all models. Including a continuous combination of prior models, our proposed method reduces the chance of mismatch between prior models and true source properties, which in turn enhances the robustness of the EP imaging solution.
To quantify the impact of anatomical modeling uncertainties on the EP imaging solution, we propose a systematic statistical framework. Founded based on statistical shape modeling and unscented transform, our method quantifies anatomical modeling uncertainties and establish their relation to the EP imaging solution. Applied on anatomical models generated from different image resolutions and different segmentations, it reports the robustness of EP imaging solution to these anatomical shape-detail variations. We then propose a simplified anatomical model for the heart that only incorporates certain subject-specific anatomical parameters, while discarding local shape details. Exploiting less resources and processing for successful EP imaging, this simplified model provides a simple clinically-compatible anatomical modeling experience for EP imaging systems.
Different components of our proposed methods are validated through a comprehensive set of synthetic and real-data experiments, including various typical pathological conditions and/or diagnostic procedures, such as myocardial infarction and pacing.
Overall, the methods presented in this dissertation for the quantification and reduction of uncertainties in cardiac EP imaging enhance the robustness of EP imaging, helping to close the gap between EP imaging in research and its clinical application
A proximal iteration for deconvolving Poisson noisy images using sparse representations
We propose an image deconvolution algorithm when the data is contaminated by
Poisson noise. The image to restore is assumed to be sparsely represented in a
dictionary of waveforms such as the wavelet or curvelet transforms. Our key
contributions are: First, we handle the Poisson noise properly by using the
Anscombe variance stabilizing transform leading to a {\it non-linear}
degradation equation with additive Gaussian noise. Second, the deconvolution
problem is formulated as the minimization of a convex functional with a
data-fidelity term reflecting the noise properties, and a non-smooth
sparsity-promoting penalties over the image representation coefficients (e.g.
-norm). Third, a fast iterative backward-forward splitting algorithm is
proposed to solve the minimization problem. We derive existence and uniqueness
conditions of the solution, and establish convergence of the iterative
algorithm. Finally, a GCV-based model selection procedure is proposed to
objectively select the regularization parameter. Experimental results are
carried out to show the striking benefits gained from taking into account the
Poisson statistics of the noise. These results also suggest that using
sparse-domain regularization may be tractable in many deconvolution
applications with Poisson noise such as astronomy and microscopy
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