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General purpose Problem Solving Environments (PSEs) like Matlab are widely used in the fields of science for development of new algorithms. If a lot of computing power is required to run these algorithms, today's PSEs lack support for accessing the distributed infrastructures of the organisation (i.e. grids), which limits the size of the problems that can be solved. This contribution shows a new approach to utilize the grid from within PSEs without major adjustments by the user. The primary tools are GridSolve and and the grid-middleware gLite. The applicability is illustrated by an exemplary algorithm (Mandelbrot calculations).

The paper is focused on ultrasonic transmission tomography as a potential medical imaging modality, namely for breast cancer diagnosis. Ultrasound attenuation coefficient is one of the tissue parameters which are related to the pathological tissue state. A technique to reconstruct images of attenuation distribution is presented. Furthermore, an alternative to the commonly used filtered backprojection or algebraic reconstruction techniques is proposed. It is based on regularization of the image reconstruction problem which imposes smoothness in the resulting images while preserving edges. The approach is analyzed on synthetic data sets. The results show that it stabilizes the image restoration by compensating for main sources of estimation errors in this imaging modality.