COMPRESSED SENSING MRI USING TOTAL VARIATION REGULARIZATION WITH K-SPACE DECOMPOSITION

Compressed sensing theory facilitates the fast magnetic resonance imaging by reducing the required number of measurements for reconstruction. Conventional compressed sensing magnetic resonance imaging(CSMRI) method utilize the partial k-space measurements as a whole without considering their intrinsic property. Some recent researches have shown the advantage of dealing the high and low frequency image content separately. Based on this, we propose a novel CSMRI algorithm based on total variation regularization with k-space decomposition. First we decompose k-space into high frequency band and low frequency band, then we reconstruct the corresponding high and low MR images which will be used for integration later. All the steps can be unified into a objective function. We will show that the proposed objective function can be split into several subproblems to solve iteratively using ADMM technique. The experimental results show that the proposed method outperforms the conventional CSMRI method. Besides, the proposed method can be extended to other image processing applications as well.