A dedicated guided-search displacement algorithm for cardiovascular strain imaging

Traditionally, an exhaustive search is performed for 2D strain imaging, often using a priori knowledge or an iterative, multi-level (ML) approach to improve strain quality. In this study, a dedicated guided-search algorithm (CGS), using a seeding procedure that was specifically designed for cardiovascular applications, is introduced and applied to simulation data, and data of aortas, both in vitro and in vitro. The method was compared to two existing methods, a multi-level algorithm and a conventional guided-search approach (GS). Results reveal an improvement of SNRe for the simulation data improvement. The (C)GS method showed good strain results, even when no filtering was applied to the displacements. The in vitro data revealed similar results, however, the in vivo data revealed significant improvement when using the CGS approach over the ML algorithm, whereas the GS method was not able to track the vessel wall over time. A next step will be to apply this algorithm to cardiac data and incorporate stretching.