Integrated Visualization of Human Brain Connectome Data

Visualization plays a vital role in the analysis of multi-modal neuroimaging data. A major challenge in neuroimaging visualization is how to integrate structural, functional and connectivity data to form a comprehensive visual context for data exploration, quality control, and hypothesis discovery. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomic structure. New surface texture techniques are developed to map non-spatial attributes onto the brain surfaces from MRI scans. Two types of non-spatial information are represented: (1) time-series data from resting-state functional MRI measuring brain activation; (2) network properties derived from structural connectivity data for different groups of subjects, which may help guide the detection of differentiation features. Through visual exploration, this integrated solution can help identify brain regions with highly correlated functional activations as well as their activation patterns. Visual detection of differentiation features can also potentially discover image based phenotypic biomarkers for brain diseases

Tractografia em tempo real através de unidades de processamento gráfico

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2009.A tractografia por propagação de linhas é um método que, com base na ressonância magnética de tensores de difusão (DT-MRI), revela in vivo a disposição dos tratos neurais no cérebro humano. O alto custo computacional da tractografia, porém, implica um tempo de espera significativamente longo para o cálculo e exibição dos resultados. Esse tempo pode ser reduzido executando-se a tractografia de forma paralela, já que as trajetórias encontradas pelo método de propagação de linhas são independentes umas das outras. Uma plataforma atrativa para a execução paralela de programas é oferecida pelas unidades de processamento gráfico (GPUs) das placas de vídeo atuais. Desse modo, este trabalho propõe a execução paralela da tractografia por propagação de linhas em GPUs através da tecnologia CUDA. Experimentos foram conduzidos para avaliar o desempenho da tractografia em um processador central (CPU) e quatro GPUs distintas. Os resultados mostram que as GPUs são, no melhor caso, até 38 vezes mais velozes que a CPU na execução da tractografia, e até 8 vezes mais rápidas no pior caso. A velocidade obtida através das GPUs permite que os resultados tractográficos sejam calculados e exibidos em tempo real para um número de trajetórias superior a 3.000

Visualisation en imagerie par résonance magnétique de diffusion : tractographie en temps réel des fibres de la matière blanche du cerveau

L'imagerie par résonance magnétique de diffusion est une technique non-invasive permettant de mesurer la diffusion des molécules d'eau selon plusieurs directions. Le résultat d'une telle acquisition contient de l'information implicite sur les structures microbiologiques qui composent le cerveau humain. La tractographie consiste à déterminer et visualiser, en trois dimensions, l'ensemble des connections neuronales de la matière blanche du cerveau en suivant la diffusion préférentielle de l'eau présente en chaque voxel. Les fibres de la matière blanche sont responsables de connecter les différentes aires fonctionnelles du cerveau entre-elles. En présence d'une tumeur, elles peuvent se réorganiser de multiples façons et refaire des connections pour assurer le suivi des fonctions importantes. L'imagerie du câblage cérébral est utilisée lors d'interventions neurochirurgicales afin d'aider le neurochirurgien à planifier son angle d'attaque pour réséquer le maximum de la tumeur sans léser la fonction du patient. La tractographie prend donc tout son sens pour le neurochirurgien avant et pendant l'opération. Dans ce mémoire, il sera question de tractographie en temps réel. La plupart des algorithmes de tractographie utilisent des paramètres fixes et prédéfinis pour l'ensemble du cerveau. Nous croyons que ces paramètres devraient être accessibles et modifiables afin de voir l'impact que ceux-ci ont sur la reconstruction des connections cérébrales. Nous proposons une méthode de visualisation de fibres en temps réel, permettant de calculer et d'afficher instantanément le résultat d'un nouvel algorithme de tractographie qui sera confronté aux méthodes existantes. Le nouveau module permet d'effectuer la tractographie des fibres de la matière blanche de manière interactive en offrant la possibilité d'ajuster les paramètres impliqués dans le processus de tractographie. Il a notamment été introduit plus d'une vingtaine de fois lors d'interventions neurochirurgicales au Centre Hospitalier Universitaire de Sherbrooke, grâce à la supervision du Dr. David Fortin. La tractographie en temps réel a changé la manière dont les données sont préparées en vue d'une intervention en bloc opératoire. Dans un contexte où le temps entre le traitement des données et l'intervention chirurgicale est une contrainte majeure, l'élimination de l'étape de tractographie du processus de prétraitement est un avantage non-négligeable

A Programmable Streaming Framework for Extreme-Scale Scientific Visualizations

Emerging computational and acquisition technologies are empowering scientists to conduct simulations and experiments on an unprecedented scale. These advancements can push the frontiers of science and technology with groundbreaking discoveries. However, they also pose significant challenges to traditional scientific visualization workflows. Firstly, the data generated by modern scientific studies using these technologies tends to be extremely large and complex, often resulting in slow processing and rendering times. This demands the development of visualization algorithms that can effectively scale with the size of the data. Secondly, state-of-the-art simulations and experiments produce data at extraordinary rates, complicating the task of generating valuable visualization results for scientists. Therefore, there's a pressing need for more adaptive and intelligent visualization workflows. Lastly, although new computer hardware and architecture can speed up the visualization process, significant performance variations still exist among visualization algorithms due to differing design choices. As a result, optimizing algorithms to better leverage emerging hardware features for enhanced efficiency remains an ongoing necessity.This dissertation addresses the aforementioned challenges by introducing a programmable streaming framework enhanced with implicit neural representation, designed for visualizing extreme-scale scientific data. Specifically, it unfolds three innovative methodologies:Firstly, the framework offers a reactive and declarative programming language for streamlining image generation, layout and interaction creation, and I/O processes, eliminating the need for users to manually control all visualization parameters and procedures. This language enables scientists to define highly adaptive visualization workflows through high-level, rule-based grammars. The system then automatically optimizes the low-level implementation according to these specifications, facilitating the creation of more efficient visualization workflows with simpler coding.Secondly, the framework features a scalable, hardware-accelerated streaming visualization system that allows visualization processes to run concurrently with I/O operations. This system not only achieves state-of-the-art scalability but can also effectively manages complex, multi-resolution data structures. It delivers accurate rendering outcomes, reduces memory usage, and leverages emerging hardware capabilities more efficiently.Finally, the framework integrates implicit neural representation (INR) techniques for data compression and interactive visualization. The use of INRs significantly reduces data size while preserving high-frequency details. Additionally, it enables direct access to spatial locations at any desired resolution, obviating the need for decompression or interpolation.In summary, this dissertation research addresses long-standing challenges inherent in extreme-scale scientific visualization by introducing novel designs and methodologies. The presented framework not only enables more efficient and adaptive visualization workflows but also leverages the latest hardware acceleration and data compression techniques. The implications of these advancements extend beyond mere technical improvements; they pave the way for deeper insights and discoveries across a broad spectrum of scientific studies. This research, therefore, represents a significant leap forward, with the potential to transform the landscape of scientific visualization