Nuevo Biomarcador en la Enfermedad de Parkinson Mediante el Análisis y Cuantificación de Lesiones Cerebrales en Secuencias Flair Obtenidas por Resonancia Magnética (ACL-Tool)

Las Lesiones en Sustancia Blanca cerebral (LSB) aparecen tanto en sujetos sanos como en ciertas patologías, siendo visibles con imágenes FLAIR de Resonancia Magnética, las cuales se presentan como áreas más brillantes respecto al resto del tejido. El uso clínico de imágenes FLAIR se basan en la inspección visual, sin embargo, este enfoque no permite la evaluación y cuantificación de lesiones que pueden ser muy importantes para diferenciar y evaluar el comportamiento de alguna patología. Actualmente existen pocas herramientas para computar y analizar este tipo de lesiones de forma automática, y más aún, que permitan determinar una serie de características importantes como volumen, cantidad, intensidad, forma, ubicación, útiles para realizar análisis y comparaciones entre grupos que permitan determinar el comportamiento de diferentes patologías que afectan la sustancia blanca cerebral. En este trabajo se presenta el desarrollo de una herramienta utilizando MATLAB y librerías de SPM donde se requiere una imagen FLAIR y una T1 por sujeto para segmentar y cuantificar estas lesiones. Se inicia con un preproceso que consiste en corregistrar la imagen T1 y FLAIR. Para realizar comparaciones intersujeto, todas las imágenes son normalizadas y deformadas hacia un espacio estándar MNI usando deformación difeomórfica. Posteriormente se segmentan las LSB y se extraen sus características. El algoritmo permite también realizar comparaciones de grupo a través de Anovas. Se reclutaron 18 sujetos sanos y 18 pacientes de Parkinson pareados por edad para evaluar la utilidad de la herramienta. Los resultados mostraron que los pacientes de Parkinson presentaron mayor cantidad de lesiones que los controles sanos y distribuidas mayormente en regiones del hemisferio derecho y Lóbulo parietal (p < 0.05). Adicionalmente, las lesiones en pacientes de Parkinson presentaron significativamente márgenes mas irregulares que las lesiones de los controles sanos. Los pacientes de Parkinson no sólo presentan volúmenes significativamente incrementados de LSB, como es lógico de suponer, dada su condición de enfermedad neurodegenerativa. Sin embargo, los pacientes manifiestan aspectos muy importantes como la lateralidad e irregularidad del contorno de las lesiones que pueden ser características únicas de la enfermedad y que podrían considerarse un biomarcador importante en la evaluación general de la enfermedad de Parkinson

A non-rigid registration approach for quantifying myocardial contraction in tagged MRI using generalized information measures.

International audienceWe address the problem of quantitatively assessing myocardial function from tagged MRI sequences. We develop a two-step method comprising (i) a motion estimation step using a novel variational non-rigid registration technique based on generalized information measures, and (ii) a measurement step, yielding local and segmental deformation parameters over the whole myocardium. Experiments on healthy and pathological data demonstrate that this method delivers, within a reasonable computation time and in a fully unsupervised way, reliable measurements for normal subjects and quantitative pathology-specific information. Beyond cardiac MRI, this work redefines the foundations of variational non-rigid registration for information-theoretic similarity criteria with potential interest in multimodal medical imaging

Dental x-ray image stitching algorithm

Suunsisäisessä röntgenkuvauksessa käytettävän fosforikuvalevyn lukulaitteen kokorajoitus esti hampaiden okluusiokuvauksen. Ratkaisu oli käyttää kahta kuvalevyä limittäin, jolloin niille tallentui osittain sama näkymä hampaista. Kuvat voitaisiin ohjelmallisesti yhdistää limittäisten osien avulla yhdeksi, suuremmaksi kuvaksi. Tässä diplomityössä on esitetty kuvat yhdistävän algoritmin ratkaisu. Se perustuu keskinäisinformaatioon sekä laitteiston tuottamien kuvien muokkaamiseen tarvittavaan muotoon ennen niiden yhdistämistä. Ohjelmisto testattiin testikuvapareilla. Johtuen kuvalevyjen päällekkäisyydestä ja röntgensäteilyn ominaisuuksista, toisella kuvista on heikompi kontrasti ja signaali-kohina-suhde. Hampaiden kohdalla kuvien kohdistus onnistui hyvin. Kitalaen alueella selkeästi erottuvaa informaatiota on vähemmän ja kohdistus oli hieman epätarkempi, joskin riittävä kyseiseen sovellukseen. Työssä on myös lyhyt katsaus röntgenkuvaukseen ja kuvien kohdistamiseen.Size restriction of the reading device of the phosphor imaging plate used in intraoral radiography prevented occlusion imaging. The solution was to use two overlapping plates to gain partially same imaging into both images. Images could be stitched into one, larger image by software. The solution for the stitching algorithm has been presented in this thesis. It is based on the mutual information method and the adjustment of the images acquired by the system for suitable form prior to the stitching. unctionality of the software was tested by a set of image pairs. Due to the overlapping phosphor plates and the properties of x-radiation, one of the images acquired has lesser contrast and weaker signal-to-noise ratio. Around the teeth the image registration was successful. Information on the palate area is less distinguishable and the registration was less accurate, but nonetheless, decent for the application. In the beginning of the thesis, there is a short review on x-radiography and image registration

Hidden Markov Models for Analysis of Multimodal Biomedical Images

Modern advances in imaging technology have enabled the collection of huge amounts of multimodal imagery of complex biological systems. The extraction of information from this data and subsequent analysis are essential in understanding the architecture and dynamics of these systems. Due to the sheer volume of the data, manual annotation and analysis is usually infeasible, and robust automated techniques are the need of the hour. In this dissertation, we present three hidden Markov model (HMM)-based methods for automated analysis of multimodal biomedical images. First, we outline a novel approach to simultaneously classify and segment multiple cells of different classes in multi-biomarker images. A 2D HMM is set up on the superpixel lattice obtained from the input image. Parameters ensuring spatial consistency of labels and high confidence in local class selection are embedded in the HMM framework, and learnt with the objective of maximizing discrimination between classes. Optimal labels are inferred using the HMM, and are aggregated to obtain global multiple object segmentation. We then address the problem of automated spatial alignment of images from different modalities. We propose a probabilistic framework, constructed using a 2D HMM, for deformable registration of multimodal images. The HMM is tailored to capture deformation via state transitions, and modality-specific representation via class-conditional emission probabilities. The latter aspect is premised on the realization that different modalities may provide very different representation for a given class of objects. Parameters of the HMM are learned from data, and hence the method is applicable to a wide array of datasets. In the final part of the dissertation, we describe a method for automated segmentation and subsequent tracking of cells in a challenging target image modality, wherein useful information from a complementary (source) modality is effectively utilized to assist segmentation. Labels are estimated in the source domain, and then transferred to generate preliminary segmentations in the target domain. A 1D HMM-based algorithm is used to refine segmentation boundaries in the target image, and subsequently track cells through a 3D image stack. This dissertation details techniques for classification, segmentation and registration, that together form a comprehensive system for automated analysis of multimodal biomedical datasets

MEDICAL SIGNALS ALIGNMENT AND PRIVACY PROTECTION USING BELIEF PROPAGATION AND COMPRESSED SENSING

The advance in human genome sequencing technology has significantly reduced the cost of data generation and overwhelms the computing capability of sequence analysis. Efficiency, efficacy and scalability remain challenging in sequence alignment, which is an important and foundational operation for genome data analysis. In this dissemination, I propose a two stage approach to tackle this problem. In the preprocessing step, I match blocks of reference and target genome sequences based on the similarities between their empirical transition probability distributions using belief propagation. I then conduct a refined match using our recently published SCoBeP technique. I extract features from neighbors of an input nucleotide (a genome sequence of neighboring nucleotides that the input nucleotide is its middle nucleotide) and leverage sparse coding to find a set of candidate nucleotides, followed by using Belief Propagation (BP) to rank these candidates. Our experimental results demonstrated robustness in nucleotide sequence alignment and our results are competitive to those of the SOAP aligner and the BWA algorithm . In addition, Most genomic datasets are not publicly accessible, due to privacy concerns. Patients genomic data contains identifiable markers and can be used to determine the presence of an individual in a dataset. Prior research shows that the re-identification can be possible when a very small set of genomic data is released. To protect patients, the data owners impose an application and evaluation procedure which often takes months to complete and limits the researchers. One solution to the problem is to let each data owner publish a set of pilot data to help data users choose the right datasets based on their needs. The data owners release these pilot data with the noise parameters and the mechanism that they used. A data user can run any kind of association tests and compare the outcomes with the other datasets outputs to get an idea which datasets can be useful. I present a privacy preserving genomic data dissemination algorithm based on the compressed sensing. In my proposed method, I am adding the noise into the sparse representation of the input vector to make it differentially private. It means I find the sparse representation using using the SubSpace Pursuit and then disturb it with sufficient Laplasian noise. I compare my method with state-of-the-art compressed sensing privacy protection method

Animal experiment (rabbit) to demonstrate changes in trabecular bone mechanical properties over time using finite element analysis.

Decreased strength of trabecular bone is a direct effect of osteoporosis, which can be evaluated by finite element analysis. However, computational limitations have restricted previous trabecular bone analyses primarily to the linear domain. In addition, previous work was largely invasive and the corresponding finite element models were typically homogeneous. Nonlinear heterogeneous finite element analysis was used to calculate trabecular bone apparent strength directly from in vivo micro computational tomography (micro-CT) scans. Through a series of validation experiments, it was shown that this nonlinear modeling is more accurate in evaluating trabecular bone mechanics in osteoporosis than previous work. A parameter driven set of material properties was employed in the finite element models using gray levels in the form of Hounsfield units as the independent variable. This enabled the finite element models to capture the variations of material properties of trabecular bone. The methods and techniques for converting the micro-CT scans into finite element models, defining the finite element models (element type, material properties characteristics) and solving the models are discussed in detail. In addition, the techniques developed for image preprocessing, such as image registration and image degradation, are also provided in this dissertation. The scanning, image processing and modeling methods and techniques were applied to two groups of rabbits, an ovariectomy group and a control group, to evaluate the time-course of trabecular bone osteoporosis. Our experiment showed that ovariectomy significantly slows the normal bone strength increase over time observed in the control group. The strength increase over time was due to a combination of increased bone architecture indices such as volume fraction and trabecular thickness as well as increased material properties due to greater bone tissue density. Compared to heterogeneous models, the homogeneous models reflected less strength increase over time because they lack the capability to capture tissue level material property variations. Volume fraction analysis alone resulted in even lower predicted increases in bone strength because it could only monitor the bone apparent level density variation. Thus the nonlinear heterogeneous models, with parameter driven material definitions, are more accurate than other types of models or methods