ACE - A Canadian Small Science Satellite Mission

The Canadian Space Agency (CSA) has initiated the Small Science Satellite (SCISAT) Program as a part of their ongoing space science program. The Atmospheric Chemistry Experiment (ACE) Mission from the University of Waterloo has been selected as the first SCISAT Mission, and will be launched in Q1, 2002 on board a Pegasus XL vehicle. The ACE spacecraft will be co-manifested on the Pegasus vehicle with another spacecraft which has not yet been selected. The ACE Mission will comprise instrumentation to measure atmospheric chemistry using the solar occultation method. The principal goal of the ACE Mission is to measure and to understand the chemical and dynamical processes that control the distribution of ozone in the upper troposphere and stratosphere. The spacecraft will be designed to operate in a 650 km, 65° inclination orbit for 2 years. The spacecraft will be developed by Canadian Industry, with Bristol Aerospace Limited of Winnipeg, Manitoba being the prime contractor for the bus, and Bomem, Inc. of Quebec City, Quebec the prime contractor for the instrument. This paper presents an overview of the CSA’s SCISAT Program and the ACE Mission. The paper describes the mission concept, the scientific instrument and the concept for the spacecraft bus, highlighting new technology that will be developed in Canada to support this mission

Comprehensive analysis of high-performance computing methods for filtered back-projection

This paper provides an extensive runtime, accuracy, and noise analysis of Computed To-mography (CT) reconstruction algorithms using various High-Performance Computing (HPC) frameworks such as: "conventional" multi-core, multi threaded CPUs, Compute Unified Device Architecture (CUDA), and DirectX or OpenGL graphics pipeline programming. The proposed algorithms exploit various built-in hardwired features of GPUs such as rasterization and texture filtering. We compare implementations of the Filtered Back-Projection (FBP) algorithm with fan-beam geometry for all frameworks. The accuracy of the reconstruction is validated using an ACR-accredited phantom, with the raw attenuation data acquired by a clinical CT scanner. Our analysis shows that a single GPU can run a FBP reconstruction 23 time faster than a 64-core multi-threaded CPU machine for an image of 1024 X 1024. Moreover, directly programming the graphics pipeline using DirectX or OpenGL can further increases the performance compared to a CUDA implementation

Algoritmos generales para simuladores de cirugía laparoscópica

Recent advances in fields such as modeling of deformable objects, haptic technologies, immersive technologies, computation capacity and virtual environments have created the conditions to offer novel and suitable training tools and learning methods in the medical area. One of these training tools is the virtual surgical simulator, which has no limitations of time or risk, unlike conventional methods of training. Moreover, these simulators allow for the quantitative evaluation of the surgeon performance, giving the possibility to create performance standards in order to define if the surgeon is well prepared to execute a determined surgical procedure on a real patient. This paper describes the development of a virtual simulator for laparoscopic surgery. The simulator allows the multimodal interaction between the surgeon and the surgical virtual environment using visual and haptic feedback devices. To make the experience of the surgeon closer to the real surgical environment a specific user interface was developed. Additionally in this paper we describe some implementations carried out to face typical challenges presented in surgical simulators related to the tradeoff between real-time performance and high realism; for instance, the deformation of soft tissues are simulated using a GPU (Graphics Processor Unit) -based implementation of the mass-spring model. In this case, we explain the algorithms developed taking into account the particular case of a cholecystectomy procedure in laparoscopic surgery.Recientes avances en áreas tales como modelación computacional de objetos deformables, tecnologías hápticas, tecnologías inmersivas, capacidad de procesamiento y ambiente virtuales han proporcionado las bases para el desarrollo de herramientas y métodos de aprendizaje confiables en el entrenamiento médico. Una de estas herramientas de entrenamiento son los simuladores quirúrgicos virtuales, los cuales no tienen limitaciones de tiempo o riesgos a diferencia de los métodos convencionales de entrenamiento. Además, dichos simuladores permiten una evaluación cuantitativa del desempeño del cirujano, dando la posibilidad de crear estándares de desempeño con el fin de definir en qué momento un cirujano está preparado para realizar un determinado procedimiento quirúrgico sobre un paciente. Este artículo describe el desarrollo de un simulador virtual para cirugía laparoscópica. Este simulador permite la interacción multimodal entre el cirujano y el ambiente virtual quirúrgico usando dispositivos de retroalimentación visual y háptica. Para hacer la experiencia del cirujano más cercana a la de una ambiente quirúrgico real se desarrolló una interfaz cirujano-simulador especial. Adicionalmente en este artículo se describen algunas implementaciones que solucionan los problemas típicos cuando se desarrolla un simulador quirúrgico, principalmente relacionados con lograr un desempeño en tiempo real mientras se sacrifica el nivel de realismo de la simulación: por ejemplo, la deformación de los tejidos blandos simulados usando una implementación del modelo masa-resorte en la unidad de procesamiento gráfico. En este caso se describen los algoritmos desarrollados tomando en cuenta la simulación de un procedimiento laparoscópico llamado colecistectomía

Simulating soft tissues using a GPU approach of the mass-spring model

The recent advances in the fields such as modeling bio-mechanics of living tissues, haptic technologies, computational capacity, and graphics realism have created conditions necessary in order to develop effective surgical training using virtual environments. However, virtual simulators need to meet two requirements, they need to be real-time and highly realistic. The most expensive computational task in a surgical simulator is that of the physical model. The physical model is the component responsible to simulate the deformation of the anatomical structures and the most important factor in order to obtain realism. In this paper we present a novel approach to virtual surgery. The novelty comes in two forms: specifically a highly realistic mass-spring model, and a GPU based technique, and analysis, that provides a nearly 80x speedup over serial execution and 20x speedup over CPU based parallel execution. ©2010 IEEE