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Tensor analysis of diffusion weighted magnetic resonance images is increasingly used for non-invasive tracking of nerve fibers in the human brain. Diffusion-tensor imaging (DTI) enables in-vivo research on the internal structure of the central nervous system, encompassing interconnection of functional areas, correlation between fiber deformations and certain disease patterns, as well as brain tumor localization. But the modeling of the local diffusion parameters is a computationally expensive part of the processing pipeline, resulting to run times up to days on standard desktop computers. A grid implementation of the algorithm with slice based parallelization reduces the processing down to 10% compared to a local cluster and 20% compared to sequential processing on the grid. A workflow implementation enables fault-tolerant handling of temporary failures within the grid. Furthermore, pure web-based access to the grid application allows for collaborative utilization even from protected infrastructures, as they are typically found in clinical environments.

Sleep related breathing disorders (SRBD) represent a major disease in sleep medicine. For diagnosis and therapy control, extensive overnight investigations are required, encompassing long-term measurement of multiple biosignals in specialized sleep disorders centers. To date, evaluation of the examination is realized by comprehensive visual inspection of the data by an expert. Therefore, many approaches have been made to facilitate diagnosis, among them automated analysis of the ECG signal. In this article, we present a grid based infrastructure for computer aided diagnosis of SRBD, accessible for distributed users. As the analysis algorithms itself are still in a validation phase, and the Grid infrastructure is not approved for clinical applications, the application is currently used for research purposes only. But as important aspects of data-security, accessibility from protected environments, usability and fault-tolerance are already covered, the implementation is a solid base for further enhancement of the platform and paves the way for a sustainable service grid for sleep medicine.