Grid Analysis of Radiological Data

IGI-Global Medical Information Science Discoveries Research Award 2009International audienceGrid technologies and infrastructures can contribute to harnessing the full power of computer-aided image analysis into clinical research and practice. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. This chapter reports on the goals, achievements and lessons learned from the AGIR (Grid Analysis of Radiological Data) project. AGIR addresses this challenge through a combined approach. On one hand, leveraging the grid middleware through core grid medical services (data management, responsiveness, compression, and workflows) targets the requirements of medical data processing applications. On the other hand, grid-enabling a panel of applications ranging from algorithmic research to clinical use cases both exploits and drives the development of the services

A framework for evaluating the impact of compression on registration algorithms without gold standard

International audienceThe impact of lossy compression has often been discussed in the medical area. In this study, an evaluation of the impact of lossy compression on the performance of rigid registration algorithms for medical images is proposed. The robustness, repeatability and accuracy of these algorithms is estimated through a statistical procedure for each compression ratio. Results are obtained thanks to a grid technology handling the computation cost of the method. Experiments reveal that the impact of compression is quite negligible below a significant compression ratio if the registration algorithm has a good multi-scale handling. Beyond this threshold, feature-based methods are highly penalized

Exploring the Virtual Infrastructures as a Service concept with HIPerNET

With the expansion and convergence of communication and computing, dynamic provisioning of customized networking and processing infrastructures, as well as resource virtualization, are appealing concepts and technologies. Therefore, new models and tools are needed to allow users to create, trust and enjoy such on-demand virtual infrastructures within a wide area context. This research report presents the HIPerNET framework that we are designing and developing for creating, managing and controlling virtual infrastructures in the context of high-speed Internet. The key idea of this proposal is the combination of network- and system-virtualization associated with controlled resource reservation to provide fully isolated environments. HIPerNET's motivations and design principles are presented. We then examine specifically how this framework handles the virtual infrastructures, called Virtual Private eXecution Infrastructures (VPXI). To help specifying customized isolated infrastructures, HIPerNET relies on VXDL, a language for VPXI description and modeling which considers end-host resource as well as the virtual network topology interconnecting them, including virtual routers. We exemplify the VPXI specification, allocation and execution using a real large-scale distributed medical application. Experimental results obtained within the Grid'5000 testbed are presented and analyzed

Joint Elastic Cloud and Virtual Network Framework for Application Performance-cost Optimization

International audienceCloud computing infrastructures are providing resources on demand for tackling the needs of large-scale distributed applications. To adapt to the diversity of cloud infras- tructures and usage, new operation tools and models are needed. Estimating the amount of resources consumed by each application in particular is a difficult problem, both for end users who aim at minimizing their costs and infrastructure providers who aim at control- ling their resources allocation. Furthermore, network provision is generally not controlled on clouds. This paper describes a framework automating cloud resources allocation, deploy- ment and application execution control. It is based on a cost estimation model taking into account both virtual network and nodes managed by the cloud. The flexible provisioning of network resources permits the optimization of applications performance and infrastructure cost reduction. Four resource allocation strategies relying on the expertise that can be cap- tured in workflow-based applications are considered. Results of these strategies are confined virtual infrastructure descriptions that are interpreted by the HIPerNet engine responsible for allocating, reserving and configuring physical resources. The evaluation of this framework was carried out on the Aladdin/Grid'5000 testbed using a real application from the area of medical image analysis

Workflow-based data parallel applications on the EGEE production grid infrastructure

articleInternational audienceSetting up and deploying complex applications on a grid infrastructure is still challenging and the programming models are rapidly evolving. Efficiently exploiting grid parallelism is often not straight forward. In this paper, we report on the techniques used for deploying applications on the EGEE production grid through four experiments coming from completely different scientific areas: nuclear fusion, astrophysics and medical imaging. These applications have in common the need for manipulating huge amounts of data and all are computationally intensive. All the cases studied show that the deployment of data intensive applications require the development of more or less elaborated application-level workload management systems on top of the gLite middleware to efficiently exploit the EGEE grid resources. In particular, the adoption of high level workflow management systems eases the integration of large scale applications while exploiting grid parallelism transparently. Different approaches for scientific workflow management are discussed. The MOTEUR workflow manager strategy to efficiently deal with complex data flows is more particularly detailed. Without requiring specific application development, it leads to very significant speed-ups

A Service-Oriented Architecture enabling dynamic services grouping for optimizing distributed workflows execution

International audienceIn this paper, we describe a Service-Oriented Architecture allowing the optimization of the execution of service workflows. We discuss the advantages of the service-oriented approach with regard to the enactment of scientific applications on a grid infrastructure. Based on the development of a generic Web-Services wrapper, we show how the flexibility of our architecture enables dynamic service grouping for optimizing the application execution time. We demonstrate performance results on a real medical imaging application. On a production grid infrastructure, the optimization proposed introduces a significant speed-up (from 1.2 to 2.9) when compared to a traditional execution

An optimized workflow enactor for data-intensive grid applications

I3S laboratory Research Report (I3S/RR-2005-32-FR), Sophia Antipolis, FranceData-intensive applications benefit from an intrinsic data parallelism that should be exploited on parallel systems to lower execution time. In the last years, data grids have been developed to handle, process, and analyze the tremendous amount of data produced in many scientific areas. Although very large, these grid infrastructures are under heavy use and efficiency is of utmost importance. This paper deals with the optimization of workflow managers used for deploying complex data-driven applications on grids. In that kind of application, we show how to better exploit data parallelism than currently done in most existing workflow managers. We present the design of a prototype implementing our solution and we show that it provides a significant speed-up w.r.t existing solutions by exemplifying results on a realistic medical imaging application

Enabling technology for non-rigid registration during image-guided neurosurgery

In the context of image processing, non-rigid registration is an operation that attempts to align two or more images using spatially varying transformations. Non-rigid registration finds application in medical image processing to account for the deformations in the soft tissues of the imaged organs. During image-guided neurosurgery, non-rigid registration has the potential to assist in locating critical brain structures and improve identification of the tumor boundary. Robust non-rigid registration methods combine estimation of tissue displacement based on image intensities with the spatial regularization using biomechanical models of brain deformation. In practice, the use of such registration methods during neurosurgery is complicated by a number of issues: construction of the biomechanical model used in the registration from the image data, high computational demands of the application, and difficulties in assessing the registration results. In this dissertation we develop methods and tools that address some of these challenges, and provide components essential for the intra-operative application of a previously validated physics-based non-rigid registration method.;First, we study the problem of image-to-mesh conversion, which is required for constructing biomechanical model of the brain used during registration. We develop and analyze a number of methods suitable for solving this problem, and evaluate them using application-specific quantitative metrics. Second, we develop a high-performance implementation of the non-rigid registration algorithm and study the use of geographically distributed Grid resources for speculative registration computations. Using the high-performance implementation running on the remote computing resources we are able to deliver the results of registration within the time constraints of the neurosurgery. Finally, we present a method that estimates local alignment error between the two images of the same subject. We assess the utility of this method using multiple sources of ground truth to evaluate its potential to support speculative computations of non-rigid registration