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Computational Anatomy As a Driver of Understanding Structural and Functional Cardiac Remodeling

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Computational Anatomy As a Driver of Understanding Structural and Functional Cardiac Remodeling Computational anatomy as a driver of understanding structural and functional cardiac remodeling. Gabriel Bernardino TESI DOCTORAL UPF / 2019 THESIS SUPERVISORS Bart Bijnens, Miguel Angel González Ballester and Mathieu De Craene Department Engineering and Information and Communication Technologies Academic Coordination Unit The cover artwork is a part of a photographic reproduction, available in Wikimedia, of a public domain domain painting: Anatomy of the heart by Enrique Simonet (1890), currently deposited in Museo de Málaga, Málaga, Spain. III Acknowledgements This has been a long wandering, which I couldn’t have endured alone. All of this has been possible thanks to my supervisors: Bart, Miguel Angel and Mathieu. They helped me by putting order in chaos (and sometimes also chaos into order). Their constant guidance, advice and help have shaped this thesis. The quality of this work reflects their quality as supervisors. A very special thanks to my colleagues in UPF, partners in coffees, merendolas, beers, aperols and wine. They have made the hard moments easier to cope with, and our scientific discussions provided a lot of ideas and insights. I have to thank the people of Philips for their hospitality and the pauses gourmandes. I would like to thank the clinical collaborators, at the CHU Caen, Hospi- tal Clínic and Maternitat. Without them this thesis would be useless. Their patience explaining the most basic of the concepts of medicine and clinical cardiology has been crucial for this thesis. I also want to thank them for their time they took from their busy schedules to understand and discuss my results. A very special thanks to Eric and Amir of the CHU Caen who hosted me during a month. Finally, I want to thank my friend and family for all the support and understanding in the difficult times. V VI Abstract Cardiac structural and functional remodelling, induced by altered or adverse working conditions, has been extensively reported in the litera- ture. The quantification and interpretation of such remodelling is still an ongoing research topic, and the mechanisms and lasting effects are not completely understood. A difficulty in understanding remodelling is that, even if, at cellular level, each cell acts independently, it is the organ-level aggregation that will ultimately determine the cardiac efficiency. Given that remodelling is driven by regional stimuli, it will not be homogeneous but will express as complex segmental patterns. The assessment through tra- ditional methods is challenging due to the focus on global quantification of the current clinical measurements, as well as the large heterogeneity between the responses of individuals. We present a statistical shape analysis (SSA) framework to identify the expected appearance of regional shape remodelling. We use a patho- logical and control population to build a model that identifies populational differences in shape. This model consists of a dimensionality reduction step (PCA and PLS) and a classification step (logistic regression). Given the large natural shape variability present in the ventricles, that can cre- ate a confounding effect if there is an imbalance of the demographics, we applied and tested methods to account for that variability: adding the con- founders as covariates in the classification model (adjustment) and build- ing a model that predicts shape from the confounders, and analysing the regression residuals (confounder deflation). We show that these methods are able to correct for demographic imbalances. The previous methodology was applied to two distinct cardiac magnetic resonance imaging (MRI) datasets: one with triathletes and another one with small-for-gestational age (SGA) individuals, which were compared to a control population to obtain the remodelling. SGA is a syndrome oc- curring at foetal stage where the individual has an impaired growth during foetal development. It is hypothesized that it translates to a higher car- diovascular risk during seniority. Using our framework, we were able to identify that SGA presented a more curved base in the right ventricle (RV) compared with controls, especially in smokers and overweight SGA indi- viduals. Athletes were also compared to controls in order to characterize endurance-activities-related remodelling. We identified a pattern that was comprised of the already known athlete’s remodelling: an increase of the left ventricular (LV) volume size and mass, but also an increase of RV volume localized in the outflow. The found quantification of the athletic remodelling was associated to a better cardiovascular response during a VII maximal stress test. Finally, we explore alternatives to analyse regional shape of the RV that did rely on the point-to-point registration when the imaging modality is noisy and with low contrast, such as 3D echocardiography. We imple- mented a mesh independent volumetric parcellation of the RV in three parts: inlet, outflow and apex. The parcellation was defined on the surface using the geodesic distances to the apex, tricuspid and pulmonary valve, and was propagated to the cavity using the Laplace equation. We tested the reproducibility of the parcellation and found an acceptable mean error ( 8%) in the intraobserver test, and a higher (>14%) for the interobserver. We validated the method in a synthetic-remodelling generated dataset and found that our method was accurate for capturing circumferential dilation but less suited for longitudinal elongations. VIII IX Resumen El remodelado cardiaco ha sido descrito extensivamente en la litera- tura. Este remodelado, que afecta tanto la estructura como la función, es inducido por una alteración de las condiciones de trabajo del corazón. Su cuantificación e interpretación es todavía un tema en investigación y sus mecanismos y efectos no han sido todavía completamente entendidos. Una dificultad para entender dicho remodelado es que, aunque a nivel celular cada célula actúa de manera independiente, es la agregación a ni- vel de órgano la que en ultima instancia determina la eficiencia cardíaca. Dado que el remodelado esta dirigido por estímulos regionales, éste no resultará en una reacción homogénea sino en complejos patrones seg- mentales. La evaluación con los métodos tradicionales es complicada ya que las medidas clínicas se centran en cuantificación de variables globa- les. Presentamos un sistema de análisis estadístico de forma (SSA) que permite identificar patrones esperados de remodelado estructural y regio- nal. Usamos una población patológica y otra de control para construir un modelo que identifica diferencias de forma. Este modelo consiste en una reducción de dimensionalidad (PCA y PLS) seguida de una clasificación (regresión logística). Debido a la gran variabilidad estructural presente en los ventrículos, un desequilibrio en los demográficos de las poblaciones puede crear un factor de confusión, hemos aplicado y testeado dos méto- dos para ajustar por esa variabilidad: el primero añade los posibles facto- res de confusión como covariables en el modelo de clasificación (ajuste) y el segundo construye un modelo que predice la forma a partir de estos factores de confusión y analiza los residuos de regresión de dicho modelo (deflación de los factores de confusión). Mostramos que estos métodos son capaces de corregir desequilibrios en los demográficos. Aplicamos la metodología anterior a dos bases de datos de imagen por resonancia magnética: uno de triatletas y otro de individuos pequeños- para-su-edad-gestacional (SGA), a los que comparamos con una pobla- ción de controles para obtener el patrón de remodelado. SGA es un sín- drome que consiste en una restricción al crecimiento durante la etapa fetal. Se ha hipotetizado que los individuos con SGA tienen una mayor propensión a problemas cardiovascular durante la tercera edad. Usando nuestro sistema, encontramos que los SGA tienen una base del ventrícu- lo derecho (RV) más curvada, especialmente en SGA fumadores o con sobrepeso. Los atletas fueron también comparados con los controles pa- ra caracterizar el remodelado relacionado con la práctica de deportes de resistencia. Identificamos un remodelado concorde con la literatura: un in- X cremento del ventrículo izquierdo (LV) tanto en tamaño como en masa, pero también un incremento del volumen del RV focalizado en el infun- díbulo. Asociamos la cuantificación de dicho remodelado con una mejor respuesta cardiovascular durante una prueba de ejercicio máxima. Finalmente, exploramos alternativas al análisis regional de forma del RV que no usasen el registro punto a punto para cuando la modalidad de imagen fuera ruidosa y con poco contraste, como es el caso de la ecocardiografía 3D. Implementamos un método de parcelación volumétri- ca independiente del mallado que divide el RV en 3 partes: apical, inlet y outflow. La parcelación fue definida usando las distancias geodésicas al apex, tricúspide y válvula pulmonar, y se propago a la cavidad usando la ecuación de Laplace. Probamos la reproducibilidad de la parcelación y encontramos un error medio intraobservador aceptable (8 %), pero mas error (>14 %) en el caso del interobservador. También validamos el méto- do usando una base de datos de remodelado generado sintéticamente y encontramos que nuestro método era exacto para analizar crecimientos circunferenciales, pero no para elongaciones longitudinales. XI The only attitude worthy of a superior man is to persist in an activity he recognizes is useless, to observe a discipline he knows is sterile, and to apply certain norms of philosophical and metaphysical thought that he considers utterly inconsequential. Fernando Pessoa, Livro do Desassossego. XIII Contents List of figures XX List of tables XXII 1. INTRODUCTION 1 1.1. Motivation . .1 1.2. Cardiac remodelling . .1 1.2.1. Morphological remodelling . .2 1.2.2. Functional remodelling . .3 1.3. Medical imaging . .5 1.4. Computational anatomy . .6 1.5. Thesis structure . .9 2. HANDLING CONFOUNDING VARIABLES IN STATISTICAL SHAPE ANALYSIS - APPLICATION TO CARDIAC REMODELLING 11 2.1. Introduction . 11 2.2. Methodology . 14 2.2.1. Atlas construction . 14 2.2.2. Confounding deflation . 15 2.2.3. Classification . 18 2.3. Experimental setup . 19 2.3.1. Clinical dataset . 19 2.3.2. Automatic measurements . 20 2.3.3.
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