Physiological fusion of functional and structural data for cardiac deformation recovery

The advancement in meaningful constraining models has resulted in increasingly useful quantitative information recovered from cardiac images. Nevertheless, single-source data used by most of these algorithms have put certain limits on the clinical completeness and relevance of the analysis results, especially for pathological cases where data fusion of multiple complementary sources is essential. As traditional image fusion strategies are typically performed at pixel level by fusing commensurate information of registered images through various mathematical operators, such approaches are not necessarily based on meaningful biological bases, particularly when the data are dissimilar in physical nature and spatiotemporal quantity. In this work, we present a physiological fusion framework for integrating information from different yet complementary sources. Using a cardiac physiome model as the central link, structural and functional data are naturally fused together for a more complete subject-specific information recovery. Experiments were performed on synthetic and real data to show the benefits and potential clinical applicability of our framework.