Improved Coil Sensitivity Estimation for SENSE Using Compressed Sensing

Bing Wu¹, Chunlei Liu¹

The conventional approach of deriving coil sensitivity profile for SENSE reconstruction using a small number of auto-calibration scan lines limits the fidelity of the coil sensitivity estimate, and hence the quality of SENSE reconstructions. However estimating coil sensitivity from under-sampled k-space data set is an under-determined problem, and previous attempts resort to additional regularizing terms that may affect the accuracy of the outcome. We present a new compress sensing based approach that allows the coil sensitivity profile to be estimated using all the acquired data measurements to achieve improved coil sensitivity estimate, which in turn leads to an improved SENSE reconstruction.