Multimodality image fusion for diagnosing coronary artery disease

Abstract

Coronary artery disease (CAD) is one of the leading causes of death in the US and a substantial health-care burden in all industrialized societies. In recent years we have witnessed a constant strive towards the development and the clinical application of novel or improved detection methods as well as therapies. Particularly, noninvasive imaging is a decisive component in the cardiovascular field. Image fusion is the ability of combining into a single integrated display the anatomical as well as the physiological data retrieved by separated modalities. Clinical evidence suggests that it represents a promising strategy in CAD assessment and risk stratification by significantly improving the diagnostic power of each modality independently considered and of the traditional side-by-side interpretation. Numerous techniques and approaches taken from the image registration field have been implemented and validated in the context of CAD assessment and management. Although its diagnostic power is widely accepted, additional technical developments are still needed to become a routinely used clinical tool.