Correlation-Based Reconstruction For Parallel Imaging

Yu Li¹, Charles L. Dumoulin¹

The presented work introduces a new reconstruction framework, "correlation-based reconstruction", to improve parallel imaging by taking advantage of all available data relationships in parallel acquisition. In this framework, correlation functions are used to mathematically describe a generic data relationship, and the reconstruction relies on the estimation of correlation functions from prior knowledge about imaging data. In a high-resolution brain imaging experiment, it is demonstrated that correlation-based reconstruction has the potential to overcome the limit posed by coil array in a conventional parallel imaging technique.