Proc. SPIE. 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics
KEYWORDS: Data modeling, Magnetic resonance imaging, Image restoration, Fourier transforms, Computer programming, Data acquisition, Temporal resolution, Picosecond phenomena, Data centers, Data analysis
This paper presents a new approach to highly accelerated dynamic parallel MRI using low rank matrix completion, partial separability (PS) model. In data acquisition, k-space data is moderately randomly undersampled at the center kspace navigator locations, but highly undersampled at the outer k-space for each temporal frame. In reconstruction, the navigator data is reconstructed from undersampled data using structured low-rank matrix completion. After all the unacquired navigator data is estimated, the partial separable model is used to obtain partial k-t data. Then the parallel imaging method is used to acquire the entire dynamic image series from highly undersampled data. The proposed method has shown to achieve high quality reconstructions with reduction factors up to 31, and temporal resolution of 29ms, when the conventional PS method fails.