MRI Reconstruction by Learning the Dictionary of Spatialfrequency-Bands Correlation: A novel algorithm integratable with PI and CS to further push acceleration

Parallel Imaging (PI) and Compressed Sensing (CS) enable MR acceleration by exploiting channel-correlation and sparsity. However, the acceleration capability is limited by channel-encoding, increased noise and blurred details. In this work, a novel algorithm is proposed to further improve the undersampled MRI reconstruction by exploiting the correlation between image details in different bands of spatial-frequencies. Dictionaries of image patches in different spatial-frequency bands were learned from database and undersampled MR images were reconstructed by solving as a sparse representation of the dictionary. The proposed algorithm demonstrated great advantages and were integrated with PI-CS to further push acceleration.

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