ITR: Fine-Grain Data Management in Computational Grids and Applications in Network-Enabled Medical Imaging for Early Cancer Detection

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In the past few decades, high-performance computing has driven the development of practical medical applications that are now widely available, such as magnetic resonance imaging and computerized tomography. In recent years, information processing is undergoing rapid advances driven by the use of distributed computing systems connected by world-wide networks. Analogous to power grids, "computational grids" have the potential to provide seamless access to high-performance computers from ubiquitous, network-enabled devices. The unprecedent levels of computation enabled by this model may foster the development of new medical applications that can substantially improve healthcare. An example is found in a class of emerging medical applications that use Light-Scattering Spectroscopy (LSS) imaging to allow in-vivo detection of pre-cancerous changes in human epithelium. Effective deployments of LSS imaging will depend on the availability of high levels of performance and require access to remote resources. This project aims to improve the state-of-the-art in data management techniques for grids to allow seamless and high-performance integration of data generated by medical instrumentation devices with distributed computers. The long-term objective is to enable deployments of network-computing based medical applications for early cancer detection that require processing capabilities beyond those available in healthcare facilities. In particular, this project will develop computing techniques to enable accurate and fast analyses of LSS images.To this end, this project will focus on three specific aims. The first aim is the development ofhigh-performance, parallel implementations of LSS analysis algorithms. The second aim is the development of data management techniques that allow on-demand access to data generated by a network-enabled instrumentation device from off-site computing resources; these techniques are based on the notion of per-user virtual file system proxies that are controlled by grid middleware and allow latencyhiding performance enhancements to be decoupled from operating system and application implementations. The third aim consists of the integration of the proposed solutions with computational grid infrastructures to enable dissemination to the research community. The results of this project will be used and lead to the development and implementation of such LSS imaging system for clinical use in collaboration with the Northwestern University Medical School and Northwestern Memorial Hospital.