COMPRESSED SENSING MRI USING DOUBLE SPARSITY WITH ADDITIONAL TRAINING IMAGES

The compressed sensing using dictionary learning has led to state-of-the-art results for magnetic resonance imaging (MRI) reconstruction from highly under-sampled measurements. Dictionary learning had been considered time-consuming especially when the patch size or the number of training patches is large. Recently, double sparsity model and online dictionary learning algorithm were proposed to obtain dictionaries with much less computational time. In this paper, we propose an efficient MRI reconstruction method by adopting the double sparsity model with the online dictionary learning method. Besides, for better reconstruction, we use separately prepared fully-sampled MRI images to train dictionaries. We compare results of the proposed technique to traditional offline methods with and without double sparsity model. Our simulation results show that the proposed technique is approximately twice faster than the traditional methods while maintaining the same reconstruction quality. Furthermore, our technique performed even better for lower sampling rate.