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Fast image registration in DirectX9 graphics hardware

Strzódka R., Droske M., Rumpf M.

Journal of Medical Informatics & Technologies

2003

Vol. 6

IT43--49

The analysis of image time series requires a correlation of the information between two images. The gradient flow registration is a method for correlating this information by successively minimizing an appropriate energy along its gradient A graphics hardware implementation of this approach to image registration is presented. The gradient flow formulation makes use of a robust multi-scale regularization, an efficient multi-grid solver and an effective time-step control. The locality of the involved operations implies a data-flow which is very well suited for an acceleration in the streaming architecture of the DX9 graphics hardware. Therefore, the implementation obtains registration results at very high performance, registering two 2562 in less than 2 seconds, such that it could be used as an interactive tool in medical image analysis.

Non-rigid registration using hardware-accelerated free-form deformation

Soza G., Bauer M., Hastreiter P., Nimsky C., Greiner G.

Journal of Medical Informatics & Technologies

2002

Vol. 3

MI181--188

In this paper we introduce a new method for non-rigid voxel-based registration of medical images. There exist many applications where an alignment between two image datasets has to be established. Often a registration of a time-shifted medical image sequence with appearing deformation of soft tissue (e.g. pre- and intraoperative data) has to be conducted. Soft tissue deformations are usually highly non-linear. In our approach, for the handling of this phenomenon and for obtaining an optimal non-linear alignment of respective datasets we transform one of them using 3D Bézier functions, which provides some inherent smoothness as well as elasticity. In order to find the optimal transformation, many evaluations of this Bézier function are necessary. In order to make the method more efficient, graphics hardware is extensively used. We applied our non-rigid algorithm successfully to MR brain images in several clinical cases and showed its value.