Probabilistic fusion of angiographic and echographic images for the 3D reconstruction of vessels

In order to provide a better quantitative and morphologic description of complex vascular lesions, we propose an approach of 3D reconstruction of the vessel internal wall, based on data fusion from two different imaging sources: two X-Ray Digital Angiography projections and a stack of Endovascular Echography slices. After extraction of echographic and angiographic information to be fused, a geometric model leads to the determination of the unknown parameters which allow the alignment of all data in a common reference frame. Both types of data are then directly included in a probabilistic reconstruction process based on Markov Random Fields. The markovian model consists of cost functions reflecting X-Ray and ultrasonic data consistency and regularization elements to control the anatomic reality of the reconstruction. The optimal solution according to the definition criteria is obtained by minimizing the model energy with an algorithm based on Simulated Annealing. Preliminary results have been obtained with data acquired on a dog aorta. The accuracy of reconstruction by data fusion is significantly improved compared with results obtained with separate reconstruction from angiographic or echographic data. Taking into account all information available about the problem, the method avoids uncertainties and ambiguities of a reconstruction based only on one modality, and the probabilistic fusion solves the possible contradictions between both acquisitions.