Biallelic SQSTM1 mutations in early-onset, variably progressive neurodegeneration.

OBJECTIVE: To characterize clinically and molecularly an early-onset, variably progressive neurodegenerative disorder characterized by a cerebellar syndrome with severe ataxia, gaze palsy, dyskinesia, dystonia, and cognitive decline affecting 11 individuals from 3 consanguineous families. METHODS: We used whole-exome sequencing (WES) (families 1 and 2) and a combined approach based on homozygosity mapping and WES (family 3). We performed in vitro studies to explore the effect of the nontruncating SQSTM1 mutation on protein function and the effect of impaired SQSTM1 function on autophagy. We analyzed the consequences of sqstm1 down-modulation on the structural integrity of the cerebellum in vivo using zebrafish as a model. RESULTS: We identified 3 homozygous inactivating variants, including a splice site substitution (c.301+2T>A) causing aberrant transcript processing and accelerated degradation of a resulting protein lacking exon 2, as well as 2 truncating changes (c.875_876insT and c.934_936delinsTGA). We show that loss of SQSTM1 causes impaired production of ubiquitin-positive protein aggregates in response to misfolded protein stress and decelerated autophagic flux. The consequences of sqstm1 down-modulation on the structural integrity of the cerebellum in zebrafish documented a variable but reproducible phenotype characterized by cerebellum anomalies ranging from depletion of axonal connections to complete atrophy. We provide a detailed clinical characterization of the disorder; the natural history is reported for 2 siblings who have been followed up for >20 years. CONCLUSIONS: This study offers an accurate clinical characterization of this recently recognized neurodegenerative disorder caused by biallelic inactivating mutations in SQSTM1 and links this phenotype to defective selective autophagy

Foveated Path Tracing with Fast Reconstruction and Efficient Sample Distribution

Polunseuranta on tietokonegrafiikan piirtotekniikka, jota on käytetty pääasiassa ei-reaaliaikaisen realistisen piirron tekemiseen. Polunseuranta tukee luonnostaan monia muilla tekniikoilla vaikeasti saavutettavia todellisen valon ilmiöitä kuten heijastuksia ja taittumista. Reaaliaikainen polunseuranta on hankalaa polunseurannan suuren laskentavaatimuksen takia. Siksi nykyiset reaaliaikaiset polunseurantasysteemi tuottavat erittäin kohinaisia kuvia, jotka tyypillisesti suodatetaan jälkikäsittelykohinanpoisto-suodattimilla. Erittäin immersiivisiä käyttäjäkokemuksia voitaisiin luoda polunseurannalla, joka täyttäisi laajennetun todellisuuden vaatimukset suuresta resoluutiosta riittävän matalassa vasteajassa. Yksi mahdollinen ratkaisu näiden vaatimusten täyttämiseen voisi olla katsekeskeinen polunseuranta, jossa piirron resoluutiota vähennetään katseen reunoilla. Tämän johdosta piirron laatu on katseen reunoilla sekä harvaa että kohinaista, mikä asettaa suuren roolin lopullisen kuvan koostavalle suodattimelle. Tässä työssä esitellään ensimmäinen reaaliajassa toimiva regressionsuodatin. Suodatin on suunniteltu kohinaisille kuville, joissa on yksi polunseurantanäyte pikseliä kohden. Nopea suoritus saavutetaan tiileissä käsittelemällä ja nopealla sovituksen toteutuksella. Lisäksi työssä esitellään Visual-Polar koordinaattiavaruus, joka jakaa polunseurantanäytteet siten, että niiden jakauma seuraa silmän herkkyysmallia. Visual-Polar-avaruuden etu muihin tekniikoiden nähden on että se vähentää työmäärää sekä polunseurannassa että suotimessa. Nämä tekniikat esittelevät toimivan prototyypin katsekeskeisestä polunseurannasta, ja saattavat toimia tienraivaajina laajamittaiselle realistisen reaaliaikaisen polunseurannan käyttöönotolle.Photo-realistic offline rendering is currently done with path tracing, because it naturally produces many real-life light effects such as reflections, refractions and caustics. These effects are hard to achieve with other rendering techniques. However, path tracing in real time is complicated due to its high computational demand. Therefore, current real-time path tracing systems can only generate very noisy estimate of the final frame, which is then denoised with a post-processing reconstruction filter. A path tracing-based rendering system capable of filling the high resolution in the low latency requirements of mixed reality devices would generate a very immersive user experience. One possible solution for fulfilling these requirements could be foveated path tracing, wherein the rendering resolution is reduced in the periphery of the human visual system. The key challenge is that the foveated path tracing in the periphery is both sparse and noisy, placing high demands on the reconstruction filter. This thesis proposes the first regression-based reconstruction filter for path tracing that runs in real time. The filter is designed for highly noisy one sample per pixel inputs. The fast execution is accomplished with blockwise processing and fast implementation of the regression. In addition, a novel Visual-Polar coordinate space which distributes the samples according to the contrast sensitivity model of the human visual system is proposed. The specialty of Visual-Polar space is that it reduces both path tracing and reconstruction work because both of them can be done with smaller resolution. These techniques enable a working prototype of a foveated path tracing system and may work as a stepping stone towards wider commercial adoption of photo-realistic real-time path tracing

Real-time quality visualization of medical models on commodity and mobile devices

This thesis concerns the specific field of visualization of medical models using commodity and mobile devices. Mechanisms for medical imaging acquisition such as MRI, CT, and micro-CT scanners are continuously evolving, up to the point of obtaining volume datasets of large resolutions (> 512^3). As these datasets grow in resolution, its treatment and visualization become more and more expensive due to their computational requirements. For this reason, special techniques such as data pre-processing (filtering, construction of multi-resolution structures, etc.) and sophisticated algorithms have to be introduced in different points of the visualization pipeline to achieve the best visual quality without compromising performance times. The problem of managing big datasets comes from the fact that we have limited computational resources. Not long ago, the only physicians that were rendering volumes were radiologists. Nowadays, the outcome of diagnosis is the data itself, and medical doctors need to render them in commodity PCs (even patients may want to render the data, and the DVDs are commonly accompanied with a DICOM viewer software). Furthermore, with the increasing use of technology in daily clinical tasks, small devices such as mobile phones and tablets can fit the needs of medical doctors in some specific areas. Visualizing diagnosis images of patients becomes more challenging when it comes to using these devices instead of desktop computers, as they generally have more restrictive hardware specifications. The goal of this Ph.D. thesis is the real-time, quality visualization of medium to large medical volume datasets (resolutions >= 512^3 voxels) on mobile phones and commodity devices. To address this problem, we use multiresolution techniques that apply downsampling techniques on the full resolution datasets to produce coarser representations which are easier to handle. We have focused our efforts on the application of Volume Visualization in the clinical practice, so we have a particular interest in creating solutions that require short pre-processing times that quickly provide the specialists with the data outcome, maximize the preservation of features and the visual quality of the final images, achieve high frame rates that allow interactive visualizations, and make efficient use of the computational resources. The contributions achieved during this thesis comprise improvements in several stages of the visualization pipeline. The techniques we propose are located in the stages of multi-resolution generation, transfer function design and the GPU ray casting algorithm itself.Esta tesis se centra en la visualización de modelos médicos de volumen en dispositivos móviles y de bajas prestaciones. Los sistemas médicos de captación tales como escáners MRI, CT y micro-CT, están en constante evolución, hasta el punto de obtener modelos de volumen de gran resolución (> 512^3). A medida que estos datos crecen en resolución, su manejo y visualización se vuelve más y más costoso debido a sus requisitos computacionales. Por este motivo, técnicas especiales como el pre-proceso de datos (filtrado, construcción de estructuras multiresolución, etc.) y algoritmos específicos se tienen que introducir en diferentes puntos de la pipeline de visualización para conseguir la mejor calidad visual posible sin comprometer el rendimiento. El problema que supone manejar grandes volumenes de datos es debido a que tenemos recursos computacionales limitados. Hace no mucho, las únicas personas en el ámbito médico que visualizaban datos de volumen eran los radiólogos. Hoy en día, el resultado de la diagnosis son los datos en sí, y los médicos necesitan renderizar estos datos en PCs de características modestas (incluso los pacientes pueden querer visualizar estos datos, pues los DVDs con los resultados suelen venir acompañados de un visor de imágenes DICOM). Además, con el reciente aumento del uso de las tecnologías en la clínica práctica habitual, dispositivos pequeños como teléfonos móviles o tablets son los más convenientes en algunos casos. La visualización de volumen es más difícil en este tipo de dispositivos que en equipos de sobremesa, pues las limitaciones de su hardware son superiores. El objetivo de esta tesis doctoral es la visualización de calidad en tiempo real de modelos grandes de volumen (resoluciones >= 512^3 voxels) en teléfonos móviles y dispositivos de bajas prestaciones. Para enfrentarnos a este problema, utilizamos técnicas multiresolución que aplican técnicas de reducción de datos a los modelos en resolución original, para así obtener modelos de menor resolución. Hemos centrado nuestros esfuerzos en la aplicación de la visualización de volumen en la práctica clínica, así que tenemos especial interés en diseñar soluciones que requieran cortos tiempos de pre-proceso para que los especialistas tengan rápidamente los resultados a su disposición. También, queremos maximizar la conservación de detalles de interés y la calidad de las imágenes finales, conseguir frame rates altos que faciliten visualizaciones interactivas y que hagan un uso eficiente de los recursos computacionales. Las contribuciones aportadas por esta tesis són mejoras en varias etapas de la pipeline de visualización. Las técnicas que proponemos se situan en las etapas de generación de la estructura multiresolución, el diseño de la función de transferencia y el algoritmo de ray casting en la GPU

Real-time quality visualization of medical models on commodity and mobile devices

This thesis concerns the specific field of visualization of medical models using commodity and mobile devices. Mechanisms for medical imaging acquisition such as MRI, CT, and micro-CT scanners are continuously evolving, up to the point of obtaining volume datasets of large resolutions (> 512^3). As these datasets grow in resolution, its treatment and visualization become more and more expensive due to their computational requirements. For this reason, special techniques such as data pre-processing (filtering, construction of multi-resolution structures, etc.) and sophisticated algorithms have to be introduced in different points of the visualization pipeline to achieve the best visual quality without compromising performance times. The problem of managing big datasets comes from the fact that we have limited computational resources. Not long ago, the only physicians that were rendering volumes were radiologists. Nowadays, the outcome of diagnosis is the data itself, and medical doctors need to render them in commodity PCs (even patients may want to render the data, and the DVDs are commonly accompanied with a DICOM viewer software). Furthermore, with the increasing use of technology in daily clinical tasks, small devices such as mobile phones and tablets can fit the needs of medical doctors in some specific areas. Visualizing diagnosis images of patients becomes more challenging when it comes to using these devices instead of desktop computers, as they generally have more restrictive hardware specifications. The goal of this Ph.D. thesis is the real-time, quality visualization of medium to large medical volume datasets (resolutions >= 512^3 voxels) on mobile phones and commodity devices. To address this problem, we use multiresolution techniques that apply downsampling techniques on the full resolution datasets to produce coarser representations which are easier to handle. We have focused our efforts on the application of Volume Visualization in the clinical practice, so we have a particular interest in creating solutions that require short pre-processing times that quickly provide the specialists with the data outcome, maximize the preservation of features and the visual quality of the final images, achieve high frame rates that allow interactive visualizations, and make efficient use of the computational resources. The contributions achieved during this thesis comprise improvements in several stages of the visualization pipeline. The techniques we propose are located in the stages of multi-resolution generation, transfer function design and the GPU ray casting algorithm itself.Esta tesis se centra en la visualización de modelos médicos de volumen en dispositivos móviles y de bajas prestaciones. Los sistemas médicos de captación tales como escáners MRI, CT y micro-CT, están en constante evolución, hasta el punto de obtener modelos de volumen de gran resolución (> 512^3). A medida que estos datos crecen en resolución, su manejo y visualización se vuelve más y más costoso debido a sus requisitos computacionales. Por este motivo, técnicas especiales como el pre-proceso de datos (filtrado, construcción de estructuras multiresolución, etc.) y algoritmos específicos se tienen que introducir en diferentes puntos de la pipeline de visualización para conseguir la mejor calidad visual posible sin comprometer el rendimiento. El problema que supone manejar grandes volumenes de datos es debido a que tenemos recursos computacionales limitados. Hace no mucho, las únicas personas en el ámbito médico que visualizaban datos de volumen eran los radiólogos. Hoy en día, el resultado de la diagnosis son los datos en sí, y los médicos necesitan renderizar estos datos en PCs de características modestas (incluso los pacientes pueden querer visualizar estos datos, pues los DVDs con los resultados suelen venir acompañados de un visor de imágenes DICOM). Además, con el reciente aumento del uso de las tecnologías en la clínica práctica habitual, dispositivos pequeños como teléfonos móviles o tablets son los más convenientes en algunos casos. La visualización de volumen es más difícil en este tipo de dispositivos que en equipos de sobremesa, pues las limitaciones de su hardware son superiores. El objetivo de esta tesis doctoral es la visualización de calidad en tiempo real de modelos grandes de volumen (resoluciones >= 512^3 voxels) en teléfonos móviles y dispositivos de bajas prestaciones. Para enfrentarnos a este problema, utilizamos técnicas multiresolución que aplican técnicas de reducción de datos a los modelos en resolución original, para así obtener modelos de menor resolución. Hemos centrado nuestros esfuerzos en la aplicación de la visualización de volumen en la práctica clínica, así que tenemos especial interés en diseñar soluciones que requieran cortos tiempos de pre-proceso para que los especialistas tengan rápidamente los resultados a su disposición. También, queremos maximizar la conservación de detalles de interés y la calidad de las imágenes finales, conseguir frame rates altos que faciliten visualizaciones interactivas y que hagan un uso eficiente de los recursos computacionales. Las contribuciones aportadas por esta tesis són mejoras en varias etapas de la pipeline de visualización. Las técnicas que proponemos se situan en las etapas de generación de la estructura multiresolución, el diseño de la función de transferencia y el algoritmo de ray casting en la GPU.Postprint (published version

FVV Live: A real-time free-viewpoint video system with consumer electronics hardware

FVV Live is a novel end-to-end free-viewpoint video system, designed for low cost and real-time operation, based on off-the-shelf components. The system has been designed to yield high-quality free-viewpoint video using consumer-grade cameras and hardware, which enables low deployment costs and easy installation for immersive event-broadcasting or videoconferencing. The paper describes the architecture of the system, including acquisition and encoding of multiview plus depth data in several capture servers and virtual view synthesis on an edge server. All the blocks of the system have been designed to overcome the limitations imposed by hardware and network, which impact directly on the accuracy of depth data and thus on the quality of virtual view synthesis. The design of FVV Live allows for an arbitrary number of cameras and capture servers, and the results presented in this paper correspond to an implementation with nine stereo-based depth cameras. FVV Live presents low motion-to-photon and end-to-end delays, which enables seamless free-viewpoint navigation and bilateral immersive communications. Moreover, the visual quality of FVV Live has been assessed through subjective assessment with satisfactory results, and additional comparative tests show that it is preferred over state-of-the-art DIBR alternatives

Real-Time Noise Removal in Foveated Path Tracing

Path tracing is a method for rendering photorealistic two-dimensional images of three-dimensional scenes based on computing intersection between the scene geometry and light rays traveling through the scene. The rise in parallel computation resources in devices such as graphics processing units (GPUs) have made it more and more viable to do path tracing in real time. To achieve real-time performance, path tracing can be further optimized by using foveated rendering, where the properties of the human visual system are exploited to reduce the number of rays outside the central point of vision (fovea), where the human eye cannot discern fine detail. The reduction in the number of rays can, however, lead to several issues. Noise appears in the image as a result of an inadequate number of path tracing samples allocated to each pixel. Furthermore, the variation in the noise from one animation frame to the next appears as flicker. Finally, artifacts can appear when the spatially subsampled image is upsampled to a uniform resolution for display. In this thesis, solutions to the aforementioned issues are explored by implementing three noise removal methods into a foveated path tracing rendering system. The computational performance and the visual quality of the implemented methods is evaluated. Of the implemented methods, cross-bilateral filter provides the best quality, but its runtime doesn't scale well to large filter sizes. Large filter sizes are enabled by the À-Trous approximation of the cross-bilateral filter, at the cost of generating more artifacts in the result. Overall, while the implemented methods are able to provide visually pleasing results in some scenarios, improvements in the algorithms (e.g., local filter parameter selection) are needed to reach the quality seen in offline methods

Determination Of Correlations Induced By The Sense And Grappa Pmri Models With An Application To Mri Rf Coil Design

Functional connectivity MRI is fast becoming a widely used non-invasive means of observing the connectivity between regions of the brain. In order to more accurately observe fluctuations in the blood oxygenation level of hemoglobin, parallel MRI reconstruction models such as SENSE and GRAPPA can be used to reduce data acquisition time, effectively increasing spatial and temporal resolution. However, the statistical implications of these models are not generally known or considered in the final analysis of the reconstructed data. In this dissertation, the non-biological correlations artificially induced by the SENSE and GRAPPA models are precisely quantified through the development of a real-valued isomorphism that represents each model in terms of a series of linear matrix operators. Using both theoretical and experimentally acquired functional connectivity data, these artificial correlations are shown to corrupt functional connectivity conclusions by incurring false positives, where regions of the brain appear to be correlated when they are not, and false negatives, where regions of the brain appear to be uncorrelated when they actually are. With a precise quantification of the artificial correlations induced by SENSE, a new cost function for optimizing the design of RF coil arrays has also been developed and implemented to generate more favorable magnetic fields for functional connectivity studies in specific brain regions. Images reconstructed with such arrays have an improved signal-to-noise ratio and a minimal SENSE induced correlation within the regions of interest, effectively improving the accuracy and reliability of functional connectivity studies