Modeling of a multimodal image aggregation process using discrete geometry

In order to help clinicians with the diagnosis of neurodegenerative diseases, we provide a synthetic functional information located in relation with anatomical structures. The final image is processed by multimodal data fusion between SPECT and MR images. We propose a new method for the management of such multiresolution data, in which a geometrical model allows an accurate correspondence of voxels from both images, while preserving at best both original pieces of information. We use this matching method to replace the interpolation step in the compulsory image registration of the data fusion process. The geometrical model is first built from registration parameters. Computational geometry algorithms, applied to this model, allow the computation of numerical values used to process the final information. The method has been applied to brain perfusion and neurotransmission SPECT images.