EIC

The research project Aneurist funded by the European Commission has demon-strated similarly to the GEMSS project the benefits of using a complex Grid-based IT infrastructure on an even larger scale in the biomedical context. In particular, Aneurist and its developments are concerned with all processes linked to research, diagnosis and treatment of cerebral aneurysms. Although the focus of the project with respect to the medical context is on a one specific disease, the basic technologies are generic and transferable to other domains in healthcare [Arbona et al., 2006].

The Aneurist project is a four year project commenced in 2006. The Aneurist consortium consists of 27 partner organizations in Europe and five collaborators from the USA, Japan and New Zealand. The partner organizations are from industry and academia including five clinical pilot centers supplying real patient data to the consortium for medical research purposes.

In contrast to GEMSS, which highlights complex computing, the medical data and its integration is emphasized in Aneurist. All medical data available in the Aneurist project is exposed through the IT infrastructure and utilized by four different appli-cation suites. The following capabilities are addressed by an individual appliappli-cation suite: personalized aneurysm rupture risk assessment (@neuRisk), design of smart implants to treat ruptured aneurysms (@neuEndo), knowledge discovery for linking genetics to disease (@neuLink), as well as integration of modeling, simulation and visualization of multimodal data (@neuFuse).

Subsequently, the major objectives of the Aneurist project, its scope and context as well as the contributions of this work are discussed in further detail.

Objectives

The Aneurist project addresses the general problem in healthcare that the process of disease diagnosis, treatment planning and development is compromised by the lack and/or fragmentation of relevant medical data. Although Aneurist is mainly a biomedical project, one major aspect to encounter this situation is utilizing state-of-the-art information technology.

A main objective of Aneurist is the design and development of a comprehensive IT system, which supports and consequently improves all processes linked to research, diagnosis and treatment development for complex and multi-factorial diseases, such as cerebral aneurysms. But the system is generic enough to be adapted to support the treatment of other diseases as well.

The Aneurist IT infrastructure consolidates heterogeneous data, computing and complex processing services in a distributed environment to be used across scientific

and organisational boundaries. The infrastructure is created in line with evolving Grid and Web services standards and leverages existing developments from GEMSS and Fura⁴ [Arbona et al., 2007].

6.2.1 Scope and context

The main objective of the Aneurist project as concerned in the context of this work is the design and development of a generic IT infrastructure to support and improve the mentioned problems in healthcare. Therefore, four integrative application suites are selected to utilize and demonstrate the added value of the overall Aneurist system.

In the following these application suites are introduced with respect to their actual purpose and implementations as well as their targeted domain.

Aneurist application suites

The Aneurist application suites span from individual treatment and its planning to knowledge discovery based on population studies, but all share the common objective to considerably improve the understanding and management of cerebral aneurysms.

In a more technical context, these applications have in common that they use the Aneurist platforms, which provide support for computationally demanding tasks such as complex modeling and simulation as well as access to health data distributed geographically in public and/or protected databases.

The Aneurist application suites include @neuFuse, @neuLink, @neuRisk, and

@neuEndo, which rely on the Aneurist platforms comprising @neuCompute and

@neuInfo. In the following these application suites and platforms will be outlined briefly:

@neuFuse provides an interactive open application framework for medical pro-fessionals and/or bioengineers to work with multimodal patient-specific data. This includes fusing diagnostic and modeling data into a coherent representation, visual-izing different types of data using multiple display modalities and types, as well as simulating and processing based on the available data according to defined clinical processes. The underlying technology comprises compute-intensive image segmenta-tion methods and multimodal registrasegmenta-tion algorithms, access to distributed sources of patient-specific information and advanced visualization methods.

@neuLink comprises a framework for knowledge discovery in the context of link-ing genetics to a certain disease such as cerebral aneurysms. For this purpose the application suite supports the identification of candidate genes associated with the disease phenotype as well as an integrated analysis of genetic epidemiology and clin-ical data. Therefore, it relies on the integration of and access to structured and

4Grid Systems Fura, http://fura.sourceforge.net

unstructured data from heterogeneous distributed data sources. The gained infor-mation is subject to advanced data and text mining in order to eventually identify candidate genes that are relevant in the context of managing cerebral aneurysms [Friedrich et al., 2008].

@neuRisk enables personalized risk assessment in a decision support system which assists a clinician in assessing the rupture risk to determine if an aneurysm should be treated or not. This application relies on the collection of relevant data, including patient-specific data with @neuFuse and general data of similar patients and risk factors with @neuLink. Moreover, compute intensive methods are applied to calculate a personalized comprehensive risk associated to a specific aneurysm [Dunlop et al., 2008].

@neuEndo supports the intervention planning and the design process with ad-vanced computational tools towards the next generation of personalized smart flow-correcting implants to finally improve treatment of ruptured aneurysms. Within this application suite the planning and design of implantable devices is conducted by simulation of the structural, haemodynamic and biological response to interven-tion, considering patient specific characteristics. Hence according compute-intensive simulation applications as well as access to patient specific data is required.

The manifold application suites of Aneurist target different domains in the biomedical context, but as indicated in their individual outline, they depend on inten-sive computation and access to distributed, heterogeneous data. In order to address these requirements the Aneurist infrastructure comprises the platforms @neuCom-pute and @neuInfo to support com@neuCom-pute intensive applications as well as access and integration of different data sources.

Aneurist platforms

Subsequently, the Aneurist platforms which constitute the essential components of the basic Aneurist infrastructure are outlined briefly:

@neuCompute provides an environment to set up compute services exposing native applications based on existing developments from GEMSS and Fura. Both software systems rely on standard Web services technologies to allow compliance with other Web services-based systems and to realize a service oriented Grid architecture.

A major achievement of the GEMSS infrastructure as mentioned earlier is the Quality of Service support of complex HPC applications, while Fura comes with the benefit of improved support for parametric sweep-type applications relying on an agent-worker model.

@neuInfo supports accessing and integration of distributed heterogeneous data sources. The @neuInfo platform provides a generic framework that supports the pro-vision and deployment of data services exposing a variety of health data sources.

Internally, the platform is based on developments from OGSA data access and

inte-gration (OGSA-DAI)⁵and the Grid Data Mediation Service (GDMS) [W¨ohrer et al., 2005]. Furthermore, data services have the same interface as compute services in order to realize transparent access for the client to Aneurist platform services in general.

In the following both platforms and their provided services will be described in further detail in the context of the Aneurist architecture.

Architecture

A high-level layered view of the Aneurist architecture is shown in Figure 6.2. The architecture focuses on three layers - application suites, middleware and resources.

Services

and risk assessment processing and fusion

Compute

Job and data handling interface Computing and data transport services

@neuCompute

Job and data handling interface

@neuInfo Core Grid middleware: GEMSS and Fura

Figure 6.2. Aneurist architecture

The top layer consists of the Aneurist application suites as outlined earlier, com-prising multimodal data processing and image fusion with @neuFuse, linking genetics to diseases by @neuLink, integrative rupture and risk assessment with @neuRisk and virtual endovascular treatment planning by @neuEndo. Similar to other Grid ar-chitectures the middle and also middleware layer exposes various kinds of resources provided in the resource layer depicted at the bottom of Figure 6.2. The middleware basically enables clients (i.e. application suites) to transparently access resources without knowing the exact details of these resources (e.g. location). In the following the middleware and resource layers are discussed briefly.

The middleware layer comprises a service-oriented Grid middleware with data and compute services, exposing a variety of computation and information resources.

5OGSA-DAI, http://www.ogsadai.org

@neuInfo is basically concerned with providing abstractions to heterogeneous dis-tributed data and @neuCompute provides computational Grid facilities to enable advanced modeling and simulation tasks. Both @neuInfo and @neuCompute are also referred to as @neuPlatforms, which provide the provisioning of Aneurist Grid ser-vices based on standard Web serser-vices technologies, i.e. these service are defined by the Web Services Description Language (WSDL) and securely accessed using SOAP.

The resource layer encompasses mainly computational and storage resources.

Typically, computational resources embrace miscellaneous hardware (e.g. HPC fa-cilities, such as PC clusters) offered by service providers in order to execute com-putationally demanding tasks, such as simulation or modeling algorithms. Storage resources are usually utilized by databases comprising simulation or patient data of various kinds including private and public data sources within and outside of Aneurist.

Moreover, the clinical pilot centers offer patient data through a dedicated component of the Aneurist infrastructure named Biomedical Infostructure (BioIS). Internally, the BioIS will make use of clinical information systems (CIS) of the participating clinical pilot centers.

6.2.2 Contribution

The contributions to Aneurist gained from this work originated mainly from the GEMSS middleware. Even tough the developments that started in GEMSS have been extended and further improved, the main contributions of this work to GEMSS can be applied equally to Aneurist. Consequently, the well-proved service infrastructure from GEMSS with its key feature Quality of Service has been applied and demonstrated in Aneurist on an even larger scale as well.

The most considerable new capability of the Aneurist middleware in comparison to GEMSS is the incorporation of distributed heterogeneous data sources as data services relying on OGSA-DAI and GDMS. The contribution of this work in the context of data services was the adaptation of the existing application service infrastructure to accomplish the requirements and provision of data services as well.

Generally, the Aneurist system architecture comprises various innovative facilities besides the data and compute services and the most considerable issues have been published in [Arbona et al., 2007]. Furthermore, the capabilities with respect to data access, integration and semantic mediation are far beyond the scope of this thesis, but can be found in [Kumpf et al., 2007].

Chapter 7