An Agile and Robust Sparse Recovery Method for MR Images Based on Selective k-space Acquisition and Artifacts Suppression

By Henry Macharia Kiragu, Elijah Mwangi, George Kamucha


Magnetic Resonance Imaging (MRI) has some attractive advantages over other medical imaging techniques. Its widespread application as a medical diagnostic tool is however hindered by its long acquisition time as well as reconstruction artifacts. A proposed Compressive Sampling (CS) based method that addresses the two limitations of conventional MRI is presented in this paper. The proposed method involves acquisition of an under-sampled k-space by employing a smaller number of phase encoding gradient steps than that dictated by the Nyquist sampling rate. The MR image reconstructed from the under-sampled k-space is then randomly sampled and reconstructed using a greedy sparse recovery method in the wavelet domain. To improve the robustness of the method, a proposed high-pass filter is used to suppress the reconstruction artifacts. The Peak Signal to Noise Ratio (PSNR) as well as the Structural SIMilarity (SSIM) measures are used to assess the performance of the proposed method. Computer simulation results demonstrate that the proposed method reduces the reconstruction concomitant artifacts by 1.75 dB for a given CS percentage measurement. For a given output image quality, the proposed method gives a scan time reduction of 20%.

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International Journal of Information Science and Technology (iJIST) – ISSN: 2550-5114