Quantitative cerebral blood flow with optical coherence tomography

Absolute measurements of cerebral blood flow (CBF) are an important endpoint in studies of cerebral pathophysiology. Currently no accepted method exists for in vivo longitudinal monitoring of CBF with high resolution in rats and mice. Using three-dimensional Doppler Optical Coherence Tomography and cranial window preparations, we present methods and algorithms for regional CBF measurements in the rat cortex. Towards this end, we develop and validate a quantitative statistical model to describe the effect of static tissue on velocity sensitivity. This model is used to design scanning protocols and algorithms for sensitive 3D flow measurements and angiography of the cortex. We also introduce a method of absolute flow calculation that does not require explicit knowledge of vessel angles. We show that OCT estimates of absolute CBF values in rats agree with prior measures by autoradiography, suggesting that Doppler OCT can perform absolute flow measurements in animal models.National Institutes of Health (U.S.) (Grant number R01-NS057476)National Institutes of Health (U.S.) (Grant number P01NS055104)National Institutes of Health (U.S.) (Grant number P50NS010828)ational Institutes of Health (U.S.) (Grant number K99NS067050)National Institutes of Health (U.S.) (Grant number R01-CA075289-13)United States. Air Force Office of Scientific Research (FA9550-07-1-0014)United States. Dept. of Defense. Medical Free Electron Laser Program (FA9550-07-1-0101

Time-varying Learning and Content Analytics via Sparse Factor Analysis

We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA), that jointly (i) traces learner concept knowledge over time, (ii) analyzes learner concept knowledge state transitions (induced by interacting with learning resources, such as textbook sections, lecture videos, etc, or the forgetting effect), and (iii) estimates the content organization and intrinsic difficulty of the assessment questions. These quantities are estimated solely from binary-valued (correct/incorrect) graded learner response data and a summary of the specific actions each learner performs (e.g., answering a question or studying a learning resource) at each time instance. Experimental results on two online course datasets demonstrate that SPARFA-Trace is capable of tracing each learner's concept knowledge evolution over time, as well as analyzing the quality and content organization of learning resources, the question-concept associations, and the question intrinsic difficulties. Moreover, we show that SPARFA-Trace achieves comparable or better performance in predicting unobserved learner responses than existing collaborative filtering and knowledge tracing approaches for personalized education

Particle Image Velocimetry and Analysis Methods Using Cleanly Seeded Particles in Supersonic Flow

Particle Image Velocimetry (PIV) was successfully conducted in the Air Force Research Lab Mach 3/ Mach 6 Facility (M3M6F) for the first time. Particle response experiments evaluating the performance of dry ice particles across an oblique shock wave were conducted using a 15 degree half-wedge in nominal Mach 3 flow. Solid carbon dioxide particles are generated through rapid expansion of liquid carbon dioxide via a small nozzle within a simple shroud tube or a tube containing static mixing elements. Particles are injected directly into the settling chamber of the Mach 3 tunnel. The particle response of carbon dioxide particles is compared to that of water particles produced from ambient moisture content in the tunnel system. Carbon dioxide particles produced particle response curves indicating particle diameters of approximately 2 microns acclimating to the asymptotic change in velocity across the shock between 25 and 30 mm on a path normal to the shock wave. Ambient water particles had a somewhat faster response acclimating within 20 to 25 mm. Analysis methods in Dantec DynamicStudio 2015a, such as cross correlation in 32x32 pixel and 64x64 pixel Interrogation Regions (IR), were compared to other techniques, namely the Adaptive PIV method and the Least Squares Method. The effects of analysis method on both freestream velocity and particle response were investigated. Other augmentations such as peak validation, Gaussian window functions, and image pre-processing techniques were evaluated using the same performance metrics

Blind deconvolution of medical ultrasound images: parametric inverse filtering approach

©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2007.910179The problem of reconstruction of ultrasound images by means of blind deconvolution has long been recognized as one of the central problems in medical ultrasound imaging. In this paper, this problem is addressed via proposing a blind deconvolution method which is innovative in several ways. In particular, the method is based on parametric inverse filtering, whose parameters are optimized using two-stage processing. At the first stage, some partial information on the point spread function is recovered. Subsequently, this information is used to explicitly constrain the spectral shape of the inverse filter. From this perspective, the proposed methodology can be viewed as a ldquohybridizationrdquo of two standard strategies in blind deconvolution, which are based on either concurrent or successive estimation of the point spread function and the image of interest. Moreover, evidence is provided that the ldquohybridrdquo approach can outperform the standard ones in a number of important practical cases. Additionally, the present study introduces a different approach to parameterizing the inverse filter. Specifically, we propose to model the inverse transfer function as a member of a principal shift-invariant subspace. It is shown that such a parameterization results in considerably more stable reconstructions as compared to standard parameterization methods. Finally, it is shown how the inverse filters designed in this way can be used to deconvolve the images in a nonblind manner so as to further improve their quality. The usefulness and practicability of all the introduced innovations are proven in a series of both in silico and in vivo experiments. Finally, it is shown that the proposed deconvolution algorithms are capable of improving the resolution of ultrasound images by factors of 2.24 or 6.52 (as judged by the autocorrelation criterion) depending on the type of regularization method used

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

Using complex light modulation for holographic applications

Complex light modulation is the ability to control a light-wave’s phase and amplitude, thereby allowing complete control of the light-wave at any spatial location. The applied optics group at The University of Texas at Austin Electrical and Computer Engineering Department created a fully complex hologram by a combination of spatial light modulators. A digital micromirror device (DMD) was used to produce a precise amplitude profile, and a liquid crystal spatial light modulator (SLM) was used to produce the phase profile. A band-limited 4-f imaging system imaged the DMD onto the SLM to create a fully complex modulated wavefront, which reconstructed a holographic image at the desired location. With this capability, it is possible to create improved imaging methods for the consumer, medical, and defense industries as well as applications in holography. Our previous research has successfully created phase-only holograms (POH), amplitude-only beam-shaping patterns, and published simulation results on full-complex modulation. This thesis provides an in-depth experimental analysis of the full-complex hologram.Electrical and Computer Engineerin