Magnetic Resonance Imaging (MRI) is highly sensitive to motion; hence current practice is based on the prevention of motion during scan. In newborns, young children, and patients with limited cooperation, this commonly requires full sedation or general anesthesia, which is time consuming, costly, and is associated with significant risks. Despite progress in prospective motion correction in MRI, the use of motion compensation techniques is limited by the type and amount of motion that can be compensated for, the dependency on the scanner platform, the need for pulse sequence modifications, and/or difficult setup. In this paper we introduce a novel platform-independent motion-robust MRI technique based on prospective real-time motion tracking through a miniature magnetic field sensor and retrospective super-resolution volume reconstruction. The technique is based on fast 2D scans that maintain high-quality of slices in the presence of motion but are degraded in 3D due to inter-slice motion artifacts. The sensor, conveniently attached to the subject forehead, provides real-time estimation of the motion, which in turn gives the relative location of the slice acquisitions. These location parameters are used to compensate the inter-slice motion to reconstruct an isotropic high-resolution volumetric image from slices in a super-resolution reconstruction framework. The quantitative results obtained for phantom and volunteer subject experiments in this study show the efficacy of the developed technique, which is particularly useful for motion-robust high-resolution T2-weighted imaging of newborns and pediatric subjects.

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In the paper we present how g-Eclipse can be used for easy running computation on Grid resources. The g-Eclipse project is an EU-founded project that aims to build an integrated workbench framework to access the power of existing Grid infrastructures. The g-Eclipse framework provides a general, integrated workbench toolset for Grid users, operators and developers. It is very useful for inexperienced users to interact with Grid resources independently of the underlying Grid middleware. The Grid abstraction enables Grid users to access the Grid in a desktop-like manner with wizards specified for common use cases.

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