Magnetic Resonance Imaging of the Pituitary Gland of Horses With Pituitary Pars Intermedia Dysfunction

Magnetic Resonance Imaging of the Pituitary Gland of Horses With Pituitary Pars Intermedia Dysfunction

Magnetic Resonance Imaging Finding in Small Ruminants with Brain

Brain disease is an important cause of neurologic deficits in small ruminants, however few MRI features have been described. The aim of this retrospective, case series study was to describe MRI characteristics in a group of small ruminants with confirmed brain disease. A total of nine small ruminants (six sheep and three goats) met inclusion criteria. All had neurologic disorders localized to the brain and histopathologic confirmation. In animals with toxic-metabolic diseases, there were bilaterally symmetric MRI lesions affecting either the gray matter (one animal with polioencephalomalacia) or the white matter (two animals with enterotoxemia). In animals with suppurative inflammation, asymmetric focal brainstem lesions were present (two animals with listeric encephalitis), or lesions typical of an intra-axial (one animal) or dural abscess (one animal), respectively. No MRI lesions were detected in one animal with suspected viral cerebellitis and one animal with parasitic migration tracts. No neoplastic or vascular lesions were identified in this case series. Findings from the current study supported the use of MRI for diagnosing brain diseases in small ruminants.

Improved Techniques for Acquisition and Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Detecting Vascular Permeability in the Central Nervous System

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a noninvasive technique for quantitative assessment of the integrity of blood-brain barrier and blood-spinal cord barrier (BSCB) in the presence of central nervous system pathologies. However, the results of DCE-MRI show substantial variability. The high variability can be caused by a number of factors including inaccurate T1 estimation, insufficient temporal resolution and poor contrast-to-noise ratio. My thesis work is to develop improved methods to reduce the variability of DCE-MRI results. To obtain fast and accurate T1 map, the Look-Locker acquisition technique was implemented with a novel and truly centric k-space segmentation scheme. In addition, an original multi-step curve fitting procedure was developed to increase the accuracy of T1 estimation. A view sharing acquisition method was implemented to increase temporal resolution, and a novel normalization method was introduced to reduce image artifacts. Finally, a new clustering algorithm was developed to reduce apparent noise in the DCE-MRI data. The performance of these proposed methods was verified by simulations and phantom studies. As part of this work, the proposed techniques were applied to an in vivo DCE-MRI study of experimental spinal cord injury (SCI). These methods have shown robust results and allow quantitative assessment of regions with very low vascular permeability. In conclusion, applications of the improved DCE-MRI acquisition and analysis methods developed in this thesis work can improve the accuracy of the DCE-MRI results.

Dynamic contrast agent -enhanced magnetic resonance imaging assessment of tumor microvasculature

Dynamic contrast agent-enhanced magnetic resonance imaging (DCE MRI) data, when analyzed with the appropriate pharmacokinetic models, have been shown to provide quantitative estimates of microvascular parameters important in characterizing the angiogenic activity of malignant tissue. These parameters consist of the whole blood volume per unit volume of tissue, v b, transport constant from the plasma to the extravascular, extracellular space (EES), k1 and the transport constant from the EES to the plasma, k2. Parameters vb and k1 are expected to correlate with microvascular density (MVD) and vascular permeability, respectively, which have been suggested to serve as surrogate markers for angiogenesis. In addition to being a marker for angiogenesis, vascular permeability is also useful in estimating tumor penetration potential of chemotherapeutic agents. ^ Histological measurements of the intratumoral microvascular environment are limited by their invasiveness and susceptibility to sampling errors. Also, MVD and vascular permeability, while useful for characterizing tumors at a single time point, have shown less utility in longitudinal studies, particularly when used to monitor the efficacy of antiangiogenic and traditional chemotherapeutic agents. These limitations led to a search for a non-invasive means of characterizing the microvascular environment of an entire tumor. ^ The overall goal of this project was to determine the utility of DCE MRI for monitoring the effect of antiangiogenic agents. Further applications of a validated DCE MRI technique include in vivo measurements of tumor microvascular characteristics to aid in determining prognosis at presentation and in estimating drug penetration. DCE MRI data were generated using single- and dual-tracer pharmacokinetic models with different molecular-weight contrast agents. The resulting pharmacokinetic parameters were compared to immunohistochemical measurements. The model and contrast agent combination yielding the best correlation between the pharmacokinetic parameters and histological measures was further evaluated in a longitudinal study to evaluate the efficacy of DCE MRI in monitoring the intratumoral microvascular environment following antiangiogenic treatment. ^

Objective Assessment of Olfactory Function Using Functional Magnetic Resonance Imaging=Estudio objetivo del olfato mediante resonancia magnética funcional

Objective: To show the results of a device that generates automated olfactory stimuli suitable for functional magnetic resonance imaging (fMRI) experiments. Material and methods: Te n normal volunteers, 5 women and 5 men, were studied. The system allows the programming of several sequences, providing the capability to synchronise the onset of odour presentation with acquisition by a trigger signal of the MRI scanner. The olfactometer is a device that allows selection of the odour, the event paradigm, the time of stimuli and the odour concentration. The paradigm used during fMRI scanning consisted of 15-s blocks. The odorant event took 2 s with butanol, mint and coffee. Results: We observed olfactory activity in the olfactory bulb, entorhinal cortex (4%), amygdala (2.5%) and temporo-parietal cortex, especially in the areas related to emotional integration. Conclusions: The device has demonstrated its effectiveness in stimulating olfactory areas and its capacity to adapt to fMRI equipment.RESUMEN Objetivo: Mostrar los resultados del olfatómetro capaz de generar tareas olfativas en un equipo de resonancia magnética funcional (fMRI). Material y métodos: Estudiamos 10 sujetos normales: 5 varones y 5 mujeres. El olfatómetro está dise ̃ nado para que el estímulo que produce se sincronice con el equipo de fMRI mediante la se ̃ nal desencadenante que suministra el propio equipo. El olfatómetro es capaz de: selec- cionar el olor, secuenciar los distintos olores, programar la frecuencia y duración de los olores y controlar la intensidad del olor. El paradigma utilizado responde a un dise ̃ no de activación asociada a eventos, en el que la duración del bloque de activación y de reposo es de 15 s. La duración del estímulo olfativo (butanol, menta o café) es de 2 segundos, durante toda la serie que consta de 9 ciclos. Resultados: Se ha observado reactividad (contraste BOLD) en las diferentes áreas cerebrales involucradas en las tareas olfativas: bulbo olfatorio, córtex entorrinal (4%), amigdala (2,5%) y córtex temporoparietal. Las áreas relacionadas con integración de las emociones tienen una reactividad mayor. Conclusiones: El dispositivo propuesto nos permite controlar de forma automática y sincronizada los olores necesarios para estudiar la actividad de las áreas olfatorias cerebrales mediante fMRI.

Magnetic resonance imaging based signal intensity mapping predicts appropriate therapies after prophylactic ventricular tachycardia substrate ablation in patients with previous myocardial infarction and secondary prevention implantable cardioverter-defibrillator implantation

The aim of this study was to determine the capability of ceMRI based signal intensity (SI) mapping to predict appropriate ICD therapies after PVTSA.

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.

Signal changes in the spinal cord of the rat after injection of formalin into the hindpaw: Characterization using functional magnetic resonance imaging

Changes in metabolism and local circulation occur in the spinal cord during peripheral noxious stimulation. Evidence is presented that this stimulation also causes signal intensity alterations in functional magnetic resonance images of the spinal cord during formalin-induced pain. These results indicate the potential of functional magnetic resonance imaging in assessing noninvasively the extent and intensity of spinal cord excitation in this well characterized pain model. Therefore, the aim of this study was to establish functional magnetic resonance imaging as a noninvasive method to characterize temporal changes in the spinal cord after a single injection of 50 μl of formalin subcutaneously into the hindpaw of the anesthetized rat. This challenge produced a biphasic licking activity in the freely moving conscious animal. Images of the spinal cord were acquired within 2 min, enabling monitoring of the site and the temporal evolution of the signal changes during the development of formalin-induced hyperalgesia without the need of any surgical procedure. The time course of changes in the spinal cord functional image in the isoflurane-anesthetized animal was similar to that obtained from behavioral experiments. Also, comparable physiological data, control experiments, and the inhibition of a response through application of the local anesthetic agent lidocaine indicate that the signal changes observed after formalin injection were specifically related to excitability changes in the relevant segments of the lumbar spinal cord. This approach could be useful to characterize different models of pain and hyperalgesia and, more importantly, to evaluate effects of analgesic drugs.

Detection of cortical activation during averaged single trials of a cognitive task using functional magnetic resonance imaging

Functional neuroimaging studies in human subjects using positron emission tomography or functional magnetic resonance imaging (fMRI) are typically conducted by collecting data over extended time periods that contain many similar trials of a task. Here methods for acquiring fMRI data from single trials of a cognitive task are reported. In experiment one, whole brain fMRI was used to reliably detect single-trial responses in a prefrontal region within single subjects. In experiment two, higher temporal sampling of a more limited spatial field was used to measure temporal offsets between regions. Activation maps produced solely from the single-trial data were comparable to those produced from blocked runs. These findings suggest that single-trial paradigms will be able to exploit the high temporal resolution of fMRI. Such paradigms will provide experimental flexibility and time-resolved data for individual brain regions on a trial-by-trial basis.

Retinotopic mapping of lateral geniculate nucleus in humans using functional magnetic resonance imaging

Subcortical nuclei in the thalamus, which play an important role in many functions of the human brain, provide challenging targets for functional mapping with neuroimaging techniques because of their small sizes and deep locations. In this study, we explore the capability of high-resolution functional magnetic resonance imaging at 4 Tesla for mapping the retinotopic organization in the lateral geniculate nucleus (LGN). Our results show that the hemifield visual stimulation only activates LGN in the contralateral hemisphere, and the lower-field and upper-field visual stimulations activate the superior and inferior portion of LGN, respectively. These results reveal a similar retinotopic organization between the human and nonhuman primate LGN and between LGN and the primary visual cortex. We conclude that high-resolution functional magnetic resonance imaging is capable of functional mapping of suborganizations in small nuclei together with cortical activation. This will have an impact for studying the thalamocortical networks in the human brain.

Dissociation of response conflict, attentional selection, and expectancy with functional magnetic resonance imaging

Two different attentional networks have been associated with visuospatial attention and conflict resolution. In most situations either one of the two networks is active or both are increased in activity together. By using functional magnetic resonance imaging and a flanker task, we show conditions in which one network (anterior attention system) is increased in activity whereas the other (visuospatial attention system) is reduced, showing that attentional conflict and selection are separate aspects of attention. Further, we distinguish between neural systems involved in different forms of conflict. Specifically, we dissociate patterns of activity in the basal ganglia and insula cortex during simple violations in expectancies (i.e., sudden changes in the frequency of an event) from patterns of activity in the anterior attention system specifically correlated with response conflict as evidenced by longer response latencies and more errors. These data provide a systems-level approach in understanding integrated attentional networks.