Parallel MRI reconstruction is formulated as a superresolution problem using coil sensitivities acquired with higher spatial resolution than the actual image. Array coils with a large number of small elements that present very localized and highly modulated sensitivity functions will form an encoding basis to estimate high k-space components from limited k-space acquisitions. The method is proposed for spatial resolution enhancement of intrinsic low-resolution modalities such as spectroscopic imaging and functional MRI where the coil sensitivities are varying withing the image voxel.

We study a method of optimal data-driven aggregation of classifiers in a convex combination and establish tight upper bounds on its excess risk with respect to a convex loss function under the assumption that the so- lution of optimal aggregation problem is sparse. We use a boosting type algorithm of optimal aggregation to develop aggregate classifiers of ac- tivation patterns in fMRI based on locally trained SVM classifiers. The aggregation coefficients are then used to design a "boosting map" of the brain needed to identify the regions with most significant impact on clas- sification.

In order to prepare for the upgrade of the German national scientific network to gigabit capacity in the year 2000, two testbeds have been set up. One of them, the 'Gigabit Testbed West', uses a 2.4 Gbit/s ATM link to connect the Research Centre Julich and the GMD-National Research Center for Information Technology in Sankt Augustin. The testbed is the basis for several application projects, ranging from metacomputing to multimedia. This paper gives an overview of the infrastructure of the testbed and its applications. As an example, the real-time analysis and visualization of functional MRI measurements of the human brain are described in detail.