Novel high performance techniques for high definition computer aided tomography

Mención Internacional en el título de doctorMedical image processing is an interdisciplinary field in which multiple research areas are involved: image acquisition, scanner design, image reconstruction algorithms, visualization, etc. X-Ray Computed Tomography (CT) is a medical imaging modality based on the attenuation suffered by the X-rays as they pass through the body. Intrinsic differences in attenuation properties of bone, air, and soft tissue result in high-contrast images of anatomical structures. The main objective of CT is to obtain tomographic images from radiographs acquired using X-Ray scanners. The process of building a 3D image or volume from the 2D radiographs is known as reconstruction. One of the latest trends in CT is the reduction of the radiation dose delivered to patients through the decrease of the amount of acquired data. This reduction results in artefacts in the final images if conventional reconstruction methods are used, making it advisable to employ iterative reconstruction algorithms. There are numerous reconstruction algorithms available, from which we can highlight two specific types: traditional algorithms, which are fast but do not enable the obtaining of high quality images in situations of limited data; and iterative algorithms, slower but more reliable when traditional methods do not reach the quality standard requirements. One of the priorities of reconstruction is the obtaining of the final images in near real time, in order to reduce the time spent in diagnosis. To accomplish this objective, new high performance techniques and methods for accelerating these types of algorithms are needed. This thesis addresses the challenges of both traditional and iterative reconstruction algorithms, regarding acceleration and image quality. One common approach for accelerating these algorithms is the usage of shared-memory and heterogeneous architectures. In this thesis, we propose a novel simulation/reconstruction framework, namely FUX-Sim. This framework follows the hypothesis that the development of new flexible X-ray systems can benefit from computer simulations, which may also enable performance to be checked before expensive real systems are implemented. Its modular design abstracts the complexities of programming for accelerated devices to facilitate the development and evaluation of the different configurations and geometries available. In order to obtain near real execution times, low-level optimizations for the main components of the framework are provided for Graphics Processing Unit (GPU) architectures. Other alternative tackled in this thesis is the acceleration of iterative reconstruction algorithms by using distributed memory architectures. We present a novel architecture that unifies the two most important computing paradigms for scientific computing nowadays: High Performance Computing (HPC). The proposed architecture combines Big Data frameworks with the advantages of accelerated computing. The proposed methods presented in this thesis provide more flexible scanner configurations as they offer an accelerated solution. Regarding performance, our approach is as competitive as the solutions found in the literature. Additionally, we demonstrate that our solution scales with the size of the problem, enabling the reconstruction of high resolution images.El procesamiento de imágenes médicas es un campo interdisciplinario en el que participan múltiples áreas de investigación como la adquisición de imágenes, diseño de escáneres, algoritmos de reconstrucción de imágenes, visualización, etc. La tomografía computarizada (TC) de rayos X es una modalidad de imágen médica basada en el cálculo de la atenuación sufrida por los rayos X a medida que pasan por el cuerpo a escanear. Las diferencias intrínsecas en la atenuación de hueso, aire y tejido blando dan como resultado imágenes de alto contraste de estas estructuras anatómicas. El objetivo principal de la TC es obtener imágenes tomográficas a partir estas radiografías obtenidas mediante escáneres de rayos X. El proceso de construir una imagen o volumen en 3D a partir de las radiografías 2D se conoce como reconstrucción. Una de las últimas tendencias en la tomografía computarizada es la reducción de la dosis de radiación administrada a los pacientes a través de la reducción de la cantidad de datos adquiridos. Esta reducción da como resultado artefactos en las imágenes finales si se utilizan métodos de reconstrucción convencionales, por lo que es aconsejable emplear algoritmos de reconstrucción iterativos. Existen numerosos algoritmos de reconstrucción disponibles a partir de los cuales podemos destacar dos categorías: algoritmos tradicionales, rápidos pero no permiten obtener imágenes de alta calidad en situaciones en las que los datos son limitados; y algoritmos iterativos, más lentos pero más estables en situaciones donde los métodos tradicionales no alcanzan los requisitos en cuanto a la calidad de la imagen. Una de las prioridades de la reconstrucción es la obtención de las imágenes finales en tiempo casi real, con el fin de reducir el tiempo de diagnóstico. Para lograr este objetivo, se necesitan nuevas técnicas y métodos de alto rendimiento para acelerar estos algoritmos. Esta tesis aborda los desafíos de los algoritmos de reconstrucción tradicionales e iterativos, con respecto a la aceleración y la calidad de imagen. Un enfoque común para acelerar estos algoritmos es el uso de arquitecturas de memoria compartida y heterogéneas. En esta tesis, proponemos un nuevo sistema de simulación/reconstrucción, llamado FUX-Sim. Este sistema se construye alrededor de la hipótesis de que el desarrollo de nuevos sistemas de rayos X flexibles puede beneficiarse de las simulaciones por computador, en los que también se puede realizar un control del rendimiento de los nuevos sistemas a desarrollar antes de su implementación física. Su diseño modular abstrae las complejidades de la programación para aceleradores con el objetivo de facilitar el desarrollo y la evaluación de las diferentes configuraciones y geometrías disponibles. Para obtener ejecuciones en casi tiempo real, se proporcionan optimizaciones de bajo nivel para los componentes principales del sistema en las arquitecturas GPU. Otra alternativa abordada en esta tesis es la aceleración de los algoritmos de reconstrucción iterativa mediante el uso de arquitecturas de memoria distribuidas. Presentamos una arquitectura novedosa que unifica los dos paradigmas informáticos más importantes en la actualidad: computación de alto rendimiento (HPC) y Big Data. La arquitectura propuesta combina sistemas Big Data con las ventajas de los dispositivos aceleradores. Los métodos propuestos presentados en esta tesis proporcionan configuraciones de escáner más flexibles y ofrecen una solución acelerada. En cuanto al rendimiento, nuestro enfoque es tan competitivo como las soluciones encontradas en la literatura. Además, demostramos que nuestra solución escala con el tamaño del problema, lo que permite la reconstrucción de imágenes de alta resolución.This work has been mainly funded thanks to a FPU fellowship (FPU14/03875) from the Spanish Ministry of Education. It has also been partially supported by other grants: • DPI2016-79075-R. “Nuevos escenarios de tomografía por rayos X”, from the Spanish Ministry of Economy and Competitiveness. • TIN2016-79637-P Towards unification of HPC and Big Data Paradigms from the Spanish Ministry of Economy and Competitiveness. • Short-term scientific missions (STSM) grant from NESUS COST Action IC1305. • TIN2013-41350-P, Scalable Data Management Techniques for High-End Computing Systems from the Spanish Ministry of Economy and Competitiveness. • RTC-2014-3028-1 NECRA Nuevos escenarios clinicos con radiología avanzada from the Spanish Ministry of Economy and Competitiveness.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: José Daniel García Sánchez.- Secretario: Katzlin Olcoz Herrero.- Vocal: Domenico Tali

Surfing the optimization space of a multiple-GPU parallel implementation of a X-ray tomography reconstruction algorithm

The increasing popularity of massively parallel architectures based on accelerators have opened up the possibility of significantly improving the performance of X-ray computed tomography (CT) applications towards achieving real-time imaging. However, achieving this goal is a challenging process, as most CT applications have not been designed for exploiting the amount of parallelism existing in these architectures. In this paper we present the massively parallel implementation and optimization of Mangoose(++), a CT application for reconstructing 3D volumes from 20 images collected by scanners based on cone-beam geometry. The main contribution of this paper are the following. First, we develop a modular application design that allows to exploit the functional parallelism inside the application and to facilitate the parallelization of individual application phases. Second, we identify a set of optimizations that can be applied individually and in combination for optimally deploying the application on a massively parallel multi-GPU system. Third, we present a study of surfing the optimization space of the modularized application and demonstrate that a significant benefit can be obtained from employing the adequate combination of application optimizations. (C) 2014 Elsevier Inc. All rights reserved.This work was partially funded by the Spanish Ministry of Science and Technology under the grant TIN2010-16497, the AMIT project (CEN-20101014) from the CDTI-CENIT program, RECAVA-RETIC Network (RD07/0014/2009), projects TEC2010-21619-C04-01, TEC2011-28972-C02-01, and PI11/00616 from the Spanish Ministerio de Ciencia e Innovacion, ARTEMIS program (S2009/DPI-1802), from the Comunidad de Madrid

High-performance geometric vascular modelling

Image-based high-performance geometric vascular modelling and reconstruction is an essential component of computer-assisted surgery on the diagnosis, analysis and treatment of cardiovascular diseases. However, it is an extremely challenging task to efficiently reconstruct the accurate geometric structures of blood vessels out of medical images. For one thing, the shape of an individual section of a blood vessel is highly irregular because of the squeeze of other tissues and the deformation caused by vascular diseases. For another, a vascular system is a very complicated network of blood vessels with different types of branching structures. Although some existing vascular modelling techniques can reconstruct the geometric structure of a vascular system, they are either time-consuming or lacking sufficient accuracy. What is more, these techniques rarely consider the interior tissue of the vascular wall, which consists of complicated layered structures. As a result, it is necessary to develop a better vascular geometric modelling technique, which is not only of high performance and high accuracy in the reconstruction of vascular surfaces, but can also be used to model the interior tissue structures of the vascular walls.This research aims to develop a state-of-the-art patient-specific medical image-based geometric vascular modelling technique to solve the above problems. The main contributions of this research are:- Developed and proposed the Skeleton Marching technique to reconstruct the geometric structures of blood vessels with high performance and high accuracy. With the proposed technique, the highly complicated vascular reconstruction task is reduced to a set of simple localised geometric reconstruction tasks, which can be carried out in a parallel manner. These locally reconstructed vascular geometric segments are then combined together using shape-preserving blending operations to faithfully represent the geometric shape of the whole vascular system.- Developed and proposed the Thin Implicit Patch method to realistically model the interior geometric structures of the vascular tissues. This method allows the multi-layer interior tissue structures to be embedded inside the vascular wall to illustrate the geometric details of the blood vessel in real world

Review : Deep learning in electron microscopy

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy