A computationally efficient method for reconstructing sequences of MR images from undersampled k-space data

• A casual approach for real-time reconstruction of dynamic MRI images from highly undersampled k-space data is proposed.
• It extracts a priori information about the image of interest from the reconstructed image of the previous time instant.
• It incorporates the priori-knowledge into an iterative hard thresholding algorithm to aid the reconstruction.
• It achieves superior reconstruction quality, with less computational complexity, compared to other CS methods.

In this paper, we propose a Compressive Sensing based approach to the problem of real-time reconstruction of MR image sequences. Our proposed method is able to extract useful priori information and incorporate it into a modified iterative thresholding algorithm for fast casual reconstruction of MR images from highly undersampled k-space data. Through extensive experimental results we show that our proposed method achieves superior reconstruction quality, while having a lower computational complexity and memory requirements compared to the other state-of-the-art methods.

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