Automated detection and classification of brain injury lesions on structural magnetic resonance imaging

El daño cerebral adquirido (DCA) es un problema social y sanitario grave, de magnitud creciente y de una gran complejidad diagnóstica y terapéutica. Su elevada incidencia, junto con el aumento de la supervivencia de los pacientes, una vez superada la fase aguda, lo convierten también en un problema de alta prevalencia. En concreto, según la Organización Mundial de la Salud (OMS) el DCA estará entre las 10 causas más comunes de discapacidad en el año 2020. La neurorrehabilitación permite mejorar el déficit tanto cognitivo como funcional y aumentar la autonomía de las personas con DCA. Con la incorporación de nuevas soluciones tecnológicas al proceso de neurorrehabilitación se pretende alcanzar un nuevo paradigma donde se puedan diseñar tratamientos que sean intensivos, personalizados, monitorizados y basados en la evidencia. Ya que son estas cuatro características las que aseguran que los tratamientos son eficaces. A diferencia de la mayor parte de las disciplinas médicas, no existen asociaciones de síntomas y signos de la alteración cognitiva que faciliten la orientación terapéutica. Actualmente, los tratamientos de neurorrehabilitación se diseñan en base a los resultados obtenidos en una batería de evaluación neuropsicológica que evalúa el nivel de afectación de cada una de las funciones cognitivas (memoria, atención, funciones ejecutivas, etc.). La línea de investigación en la que se enmarca este trabajo de investigación pretende diseñar y desarrollar un perfil cognitivo basado no sólo en el resultado obtenido en esa batería de test, sino también en información teórica que engloba tanto estructuras anatómicas como relaciones funcionales e información anatómica obtenida de los estudios de imagen. De esta forma, el perfil cognitivo utilizado para diseñar los tratamientos integra información personalizada y basada en la evidencia. Las técnicas de neuroimagen representan una herramienta fundamental en la identificación de lesiones para la generación de estos perfiles cognitivos. La aproximación clásica utilizada en la identificación de lesiones consiste en delinear manualmente regiones anatómicas cerebrales. Esta aproximación presenta diversos problemas relacionados con inconsistencias de criterio entre distintos clínicos, reproducibilidad y tiempo. Por tanto, la automatización de este procedimiento es fundamental para asegurar una extracción objetiva de información. La delineación automática de regiones anatómicas se realiza mediante el registro tanto contra atlas como contra otros estudios de imagen de distintos sujetos. Sin embargo, los cambios patológicos asociados al DCA están siempre asociados a anormalidades de intensidad y/o cambios en la localización de las estructuras. Este hecho provoca que los algoritmos de registro tradicionales basados en intensidad no funcionen correctamente y requieran la intervención del clínico para seleccionar ciertos puntos (que en esta tesis hemos denominado puntos singulares). Además estos algoritmos tampoco permiten que se produzcan deformaciones grandes deslocalizadas. Hecho que también puede ocurrir ante la presencia de lesiones provocadas por un accidente cerebrovascular (ACV) o un traumatismo craneoencefálico (TCE). Esta tesis se centra en el diseño, desarrollo e implementación de una metodología para la detección automática de estructuras lesionadas que integra algoritmos cuyo objetivo principal es generar resultados que puedan ser reproducibles y objetivos. Esta metodología se divide en cuatro etapas: pre-procesado, identificación de puntos singulares, registro y detección de lesiones. Los trabajos y resultados alcanzados en esta tesis son los siguientes: Pre-procesado. En esta primera etapa el objetivo es homogeneizar todos los datos de entrada con el objetivo de poder extraer conclusiones válidas de los resultados obtenidos. Esta etapa, por tanto, tiene un gran impacto en los resultados finales. Se compone de tres operaciones: eliminación del cráneo, normalización en intensidad y normalización espacial. Identificación de puntos singulares. El objetivo de esta etapa es automatizar la identificación de puntos anatómicos (puntos singulares). Esta etapa equivale a la identificación manual de puntos anatómicos por parte del clínico, permitiendo: identificar un mayor número de puntos lo que se traduce en mayor información; eliminar el factor asociado a la variabilidad inter-sujeto, por tanto, los resultados son reproducibles y objetivos; y elimina el tiempo invertido en el marcado manual de puntos. Este trabajo de investigación propone un algoritmo de identificación de puntos singulares (descriptor) basado en una solución multi-detector y que contiene información multi-paramétrica: espacial y asociada a la intensidad. Este algoritmo ha sido contrastado con otros algoritmos similares encontrados en el estado del arte. Registro. En esta etapa se pretenden poner en concordancia espacial dos estudios de imagen de sujetos/pacientes distintos. El algoritmo propuesto en este trabajo de investigación está basado en descriptores y su principal objetivo es el cálculo de un campo vectorial que permita introducir deformaciones deslocalizadas en la imagen (en distintas regiones de la imagen) y tan grandes como indique el vector de deformación asociado. El algoritmo propuesto ha sido comparado con otros algoritmos de registro utilizados en aplicaciones de neuroimagen que se utilizan con estudios de sujetos control. Los resultados obtenidos son prometedores y representan un nuevo contexto para la identificación automática de estructuras. Identificación de lesiones. En esta última etapa se identifican aquellas estructuras cuyas características asociadas a la localización espacial y al área o volumen han sido modificadas con respecto a una situación de normalidad. Para ello se realiza un estudio estadístico del atlas que se vaya a utilizar y se establecen los parámetros estadísticos de normalidad asociados a la localización y al área. En función de las estructuras delineadas en el atlas, se podrán identificar más o menos estructuras anatómicas, siendo nuestra metodología independiente del atlas seleccionado. En general, esta tesis doctoral corrobora las hipótesis de investigación postuladas relativas a la identificación automática de lesiones utilizando estudios de imagen médica estructural, concretamente estudios de resonancia magnética. Basándose en estos cimientos, se han abrir nuevos campos de investigación que contribuyan a la mejora en la detección de lesiones. ABSTRACT Brain injury constitutes a serious social and health problem of increasing magnitude and of great diagnostic and therapeutic complexity. Its high incidence and survival rate, after the initial critical phases, makes it a prevalent problem that needs to be addressed. In particular, according to the World Health Organization (WHO), brain injury will be among the 10 most common causes of disability by 2020. Neurorehabilitation improves both cognitive and functional deficits and increases the autonomy of brain injury patients. The incorporation of new technologies to the neurorehabilitation tries to reach a new paradigm focused on designing intensive, personalized, monitored and evidence-based treatments. Since these four characteristics ensure the effectivity of treatments. Contrary to most medical disciplines, it is not possible to link symptoms and cognitive disorder syndromes, to assist the therapist. Currently, neurorehabilitation treatments are planned considering the results obtained from a neuropsychological assessment battery, which evaluates the functional impairment of each cognitive function (memory, attention, executive functions, etc.). The research line, on which this PhD falls under, aims to design and develop a cognitive profile based not only on the results obtained in the assessment battery, but also on theoretical information that includes both anatomical structures and functional relationships and anatomical information obtained from medical imaging studies, such as magnetic resonance. Therefore, the cognitive profile used to design these treatments integrates information personalized and evidence-based. Neuroimaging techniques represent an essential tool to identify lesions and generate this type of cognitive dysfunctional profiles. Manual delineation of brain anatomical regions is the classical approach to identify brain anatomical regions. Manual approaches present several problems related to inconsistencies across different clinicians, time and repeatability. Automated delineation is done by registering brains to one another or to a template. However, when imaging studies contain lesions, there are several intensity abnormalities and location alterations that reduce the performance of most of the registration algorithms based on intensity parameters. Thus, specialists may have to manually interact with imaging studies to select landmarks (called singular points in this PhD) or identify regions of interest. These two solutions have the same inconvenient than manual approaches, mentioned before. Moreover, these registration algorithms do not allow large and distributed deformations. This type of deformations may also appear when a stroke or a traumatic brain injury (TBI) occur. This PhD is focused on the design, development and implementation of a new methodology to automatically identify lesions in anatomical structures. This methodology integrates algorithms whose main objective is to generate objective and reproducible results. It is divided into four stages: pre-processing, singular points identification, registration and lesion detection. Pre-processing stage. In this first stage, the aim is to standardize all input data in order to be able to draw valid conclusions from the results. Therefore, this stage has a direct impact on the final results. It consists of three steps: skull-stripping, spatial and intensity normalization. Singular points identification. This stage aims to automatize the identification of anatomical points (singular points). It involves the manual identification of anatomical points by the clinician. This automatic identification allows to identify a greater number of points which results in more information; to remove the factor associated to inter-subject variability and thus, the results are reproducible and objective; and to eliminate the time spent on manual marking. This PhD proposed an algorithm to automatically identify singular points (descriptor) based on a multi-detector approach. This algorithm contains multi-parametric (spatial and intensity) information. This algorithm has been compared with other similar algorithms found on the state of the art. Registration. The goal of this stage is to put in spatial correspondence two imaging studies of different subjects/patients. The algorithm proposed in this PhD is based on descriptors. Its main objective is to compute a vector field to introduce distributed deformations (changes in different imaging regions), as large as the deformation vector indicates. The proposed algorithm has been compared with other registration algorithms used on different neuroimaging applications which are used with control subjects. The obtained results are promising and they represent a new context for the automatic identification of anatomical structures. Lesion identification. This final stage aims to identify those anatomical structures whose characteristics associated to spatial location and area or volume has been modified with respect to a normal state. A statistical study of the atlas to be used is performed to establish which are the statistical parameters associated to the normal state. The anatomical structures that may be identified depend on the selected anatomical structures identified on the atlas. The proposed methodology is independent from the selected atlas. Overall, this PhD corroborates the investigated research hypotheses regarding the automatic identification of lesions based on structural medical imaging studies (resonance magnetic studies). Based on these foundations, new research fields to improve the automatic identification of lesions in brain injury can be proposed.

Assessment of oral bioaccessibility of arsenic in playground soil in Madrid (Spain): A three-method comparison and implications for risk assessment

Three methodologies to assess As bioaccessibility were evaluated using playgroundsoil collected from 16 playgrounds in Madrid, Spain: two (Simplified Bioaccessibility Extraction Test: SBET, and hydrochloric acid-extraction: HCl) assess gastric-only bioaccessibility and the third (Physiologically Based Extraction Test: PBET) evaluates mouth–gastric–intestinal bioaccessibility. Aqua regia-extractable (pseudo total) As contents, which are routinely employed in riskassessments, were used as the reference to establish the following percentages of bioaccessibility: SBET – 63.1; HCl – 51.8; PBET – 41.6, the highest values associated with the gastric-only extractions. For Madridplaygroundsoils – characterised by a very uniform, weakly alkaline pH, and low Fe oxide and organic matter contents – the statistical analysis of the results indicates that, in contrast with other studies, the highest percentage of As in the samples was bound to carbonates and/or present as calcium arsenate. As opposed to the As bound to Fe oxides, this As is readily released in the gastric environment as the carbonate matrix is decomposed and calcium arsenate is dissolved, but some of it is subsequently sequestered in unavailable forms as the pH is raised to 5.5 to mimic intestinal conditions. The HCl extraction can be used as a simple and reliable (i.e. low residual standard error) proxy for the more expensive, time consuming, and error-prone PBET methodology. The HCl method would essentially halve the estimate of carcinogenic risk for children playing in Madridplaygroundsoils, providing a more representative value of associated risk than the pseudo-total concentrations used at present

Mealiness assessment in fruits using MRI techniques.

Mealiness is a sensory attribute that cannot be defined by a single parameter but through a combination of variables (multidimensional structure). Previous studies propose the definition of mealiness as the lack of crispiness, of hardness and of juiciness. Current aims are focused on establishing non destructive tests for mealiness assessment. MultiSliceMultiEcho Magnetic resonance images (MRI, 64*64pixels) have been taken corresponding to a 3ms of Echo time. Small samples of Top Red apples stored 6 months at controlled atmosphere (expected to be non mealy) and 2°C (expected to be mealy) have been used for MRI imaging. Three out of four apples corresponding to the sample maintained at controlled atmosphere did not develop mealiness while three out of four fruits corresponding to the sample stored at 2°C became mealy after 6 month of storage. The minimum T2 values/image obtained for the mealy apples shows to be significantly lower when compared with non mealy apples pointing that a more dis-aggregated structure leads to a quicker loss of signal Also, there is a significant linear correlation (r=-0.76) between the number of pixels with a T2 value below 35ms within a fruit image and the deformation parameter registered during the Magness-Taylor firmness test. Finally, all the T2 images of the mealy apples show a regional variation of contrast which is not shown for non mealy apples. This variation of contrast is similar to the MRI images of water-cored apples indicating that in these cases there is a differential water movement that may precede the internal browning.

In-field assessment of batteries and PV modules in a large photovoltaic rural electrification programme

Assuring the sustainability of quality in photovoltaic rural electrification programmes involves enhancing the reliability of the components of solar home systems as well as the characterization of the overall programme cost structure. Batteries and photovoltaic modules have a great impact on both the reliability and the cost assessment, the battery being the weakest component of the solar home system and consequently the most expensive element of the programme. The photovoltaic module, despite being the most reliable component, has a significant impact cost-wise on the initial investment, even at current market prices. This paper focuses on the in-field testing of both batteries and photovoltaic modules working under real operating conditions within a sample of 41 solar home systems belonging to a large photovoltaic rural electrification programme with more than 13,000 installed photovoltaic systems. Different reliability parameters such as lifetime have been evaluated, taking into account different factors, for example energy consumption rates, or the manufacturing quality of batteries. A degradation model has been proposed relating both loss of capacity and time of operation. The user e solar home system binomial is also analysed in order to understand the meaning of battery lifetime in rural electrification.

Bioaccessibility of metals and human health risk assessment in community urban gardens

Pseudo-total (i.e. aqua regia extractable) and gastric-bioaccessible (i.e. glycine + HCl extractable) concentrations of Ca, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn were determined in a total of 48 samples collected from six community urban gardens of different characteristics in the city of Madrid (Spain). Calcium carbonate appears to be the soil property that determines the bioaccessibility of a majority of those elements, and the lack of influence of organic matter, pH and texture can be explained by their low levels in the samples (organic matter) or their narrow range of variation (pH and texture). A conservative risk assessment with bioaccessible concentrations in two scenarios, i.e. adult urban farmers and children playing in urban gardens, revealed acceptable levels of risk, but with large differences between urban gardens depending on their history of land use and their proximity to busy areas in the city center. Only in a worst-case scenario in which children who use urban gardens as recreational areas also eat the produce grown in them would the risk exceed the limits of acceptability

Quantitative assessment based on kinematic measures of functional impairments during upper extremity movements: a review

Quantitative measures of human movement quality are important for discriminating healthy and pathological conditions and for expressing the outcomes and clinically important changes in subjects' functional state. However the most frequently used instruments for the upper extremity functional assessment are clinical scales, that previously have been standardized and validated, but have a high subjective component depending on the observer who scores the test. But they are not enough to assess motor strategies used during movements, and their use in combination with other more objective measures is necessary. The objective of the present review is to provide an overview on objective metrics found in literature with the aim of quantifying the upper extremity performance during functional tasks, regardless of the equipment or system used for registering kinematic data.