GRAPPA Operator Enhanced Initialization for Improved Multi-Channel Compressed Sensing

Feng Huang¹, Wei Lin¹, George Randy Duensing¹, Arne Reykowski¹

The combination of partially parallel imaging (PPI) and compressed sensing has shown great potential for fast imaging. Fourier transform of the partially acquired data is conventionally used as the initialization of the iterative reconstruction algorithm. A good initialization is crucial for the convergence speed and accuracy of iterative algorithms. In this work, it is proposed to use GRAPPA operator to efficiently generate initialization for multi-channel compressed sensing. Using self-feeding Sparse SENSE as a specific example of multi-channel compressed sensing algorithm, experimental results show the advantages of the proposed method over conventional scheme.