Imaging of a fluid injection process using geophysical data - A didactic example

In many subsurface industrial applications, fluids are injected into or withdrawn from a geologic formation. It is of practical interest to quantify precisely where, when, and by how much the injected fluid alters the state of the subsurface. Routine geophysical monitoring of such processes attempts to image the way that geophysical properties, such as seismic velocities or electrical conductivity, change through time and space and to then make qualitative inferences as to where the injected fluid has migrated. The more rigorous formulation of the time-lapse geophysical inverse problem forecasts how the subsurface evolves during the course of a fluid-injection application. Using time-lapse geophysical signals as the data to be matched, the model unknowns to be estimated are the multiphysics forward-modeling parameters controlling the fluid-injection process. Properly reproducing the geophysical signature of the flow process, subsequent simulations can predict the fluid migration and alteration in the subsurface. The dynamic nature of fluid-injection processes renders imaging problems more complex than conventional geophysical imaging for static targets. This work intents to clarify the related hydrogeophysical parameter estimation concepts

Brain Functional and Structural Networks Underpinning Musical Creativity

Musical improvisation is one of the most complex forms of creative behavior, which offers a realistic task paradigm for the investigation of real-time creativity. Despite previous studies on the topics of musical improvisation, brain activations, and creativity, the main questions about the neural mechanisms for musical improvisation in efforts to unlocking the mystery of human creativity remain unanswered. What are the brain regions that are activated during the improvised performances of music? How do these brain areas coordinate activity among themselves and others during such performances? Whether and how does the brain connectivity structure encapsulate such creative skills? In attempts to contribute to answering these questions, this dissertation examines the brain activity dynamics during musical improvisation, explores white matter fiber architecture in advanced jazz improvisers using functional and structural magnetic resonance imaging (MRI) techniques. A group of advanced jazz musicians underwent functional and structural magnetic resonance brain imaging. While the functional MRI (fMRI) of their brains were collected, these expert improvisers performed vocalization and imagery improvisation and pre-learned melody tasks. The activation and connectivity analysis of the fMRI data showed that musical improvisation is characterized by higher brain activity with less functional connectivity compared to pre-learned melody in the brain network consisting of the dorsolateral prefrontal cortex (dlPFC), supplementary motor area (SMA), lateral premotor cortex (lPMC), Cerebellum (Cb) and Broca’s Area (BCA). SMA received a dominant causal information flow from dlPFC during improvisation and prelearned melody tasks. The deterministic fiber tractography analysis also revealed that the underlying white matter structure and fiber pathways in advanced jazz improvisers were enhanced in advanced jazz improvisers compared to the control group of nonmusicians, specifically the dlPFC - SMA network. These results point to the notion that an expert\u27s performance under real-time constraints is an internally directed behavior controlled primarily by a specific brain network, that has enhanced task-supportive structural connectivity. Overall, these findings suggest that a creative act of an expert is functionally controlled by a specific cortical network as in any internally directed attention and is encapsulated by the long-timescale brain structural network changes in support of the related cognitive underpinnings

Correction of spatial distortion in magnetic resonance imaging

Dissertation to Obtain the Degree of Master in Biomedical EngineeringMagnetic Resonance Imaging (MRI) has been a major investigation and research focus among scientific and medical communities. So, new hardware with superior magnetic fields and faster sequences has been developed. However, these improvements result in intensity and spatial distortions, particularly in fast sequences, as Echo Plana Imaging (EPI), used in functional and diffusion-weighed MRI (fMRI and DW-MRI). Therefore, correction of spatial distortion is useful to obtain a higher quality in this kind of images. This project contains two major parts. The first part consists in simulating MRI data required for assessing the performance of Registration methods and optimizing parameters. To assess the methods five evaluation metrics were calculated between the corrected data and an undistorted EPI, namely: Root Mean Square (RMS); Normalized Mutual Information (NMI), Squared Correlation Coefficient(SCC); Euclidean Distance of Centres of Mass (CM) and Dice Coefficient of segmented images. In brief, this part validates the applied Registration correction method. The project’s second part includes correction of real images, obtained at a Clinical Partner. Real images are diffusion weighted MRI data with different b-values (gradient strength coefficient), allowing performance assessment of different methods on images with increasing b-values and decreasing SNR. The methods tested on real data were Registration, Field Map correction and a new proposed pipeline, which consists in performing a Field Map correction after a registration process. To assess the accuracy of these methods on real data, we used the same evaluation metrics, as for simulated data, except RMS and Dice Coefficient. At the end, it was concluded that Registration-based methods are better than Field Map, and that the new proposed pipeline produces some improvements in the registration. Regarding the influence of b-value on the correction, it is important to say that the methods performed using images with higher b’s showed more improvements in regarding metric values, but the behaviour is similar for all b-values

On motion in dynamic magnetic resonance imaging: Applications in cardiac function and abdominal diffusion

La imagen por resonancia magnética (MRI), hoy en día, representa una potente herramienta para el diagnóstico clínico debido a su flexibilidad y sensibilidad a un amplio rango de propiedades del tejido. Sus principales ventajas son su sobresaliente versatilidad y su capacidad para proporcionar alto contraste entre tejidos blandos. Gracias a esa versatilidad, la MRI se puede emplear para observar diferentes fenómenos físicos dentro del cuerpo humano combinando distintos tipos de pulsos dentro de la secuencia. Esto ha permitido crear distintas modalidades con múltiples aplicaciones tanto biológicas como clínicas. La adquisición de MR es, sin embargo, un proceso lento, lo que conlleva una solución de compromiso entre resolución y tiempo de adquisición (Lima da Cruz, 2016; Royuela-del Val, 2017). Debido a esto, la presencia de movimiento fisiológico durante la adquisición puede conllevar una grave degradación de la calidad de imagen, así como un incremento del tiempo de adquisición, aumentando así tambien la incomodidad del paciente. Esta limitación práctica representa un gran obstáculo para la viabilidad clínica de la MRI. En esta Tesis Doctoral se abordan dos problemas de interés en el campo de la MRI en los que el movimiento fisiológico tiene un papel protagonista. Éstos son, por un lado, la estimación robusta de parámetros de rotación y esfuerzo miocárdico a partir de imágenes de MR-Tagging dinámica para el diagnóstico y clasificación de cardiomiopatías y, por otro, la reconstrucción de mapas del coeficiente de difusión aparente (ADC) a alta resolución y con alta relación señal a ruido (SNR) a partir de adquisiciones de imagen ponderada en difusión (DWI) multiparamétrica en el hígado.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione