A Novel Compressive Sampling MRI Method Using Variable-Density k-Space Under-sampling and Substitution of Coefficients

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Abstract: A fast Magnetic Resonance Imaging (MRI) algorithm that also reduces reconstruction artifacts is proposed in this paper. The method employs a variable-density k-space under-sampling scheme that reduces the image acquisition time. The under-sampled k-space is converted to an MR image that is corrupted by artifacts. The image is fully sampled using a sub-Gaussian random sampling matrix prior to being reconstructed in the Discrete Wavelet Transform (DWT) domain using a Compressive Sampling (CS) greedy method. The k-space coefficients that are acquired during the under-sampling step are used to replace their corresponding coefficients in the k-space of the compressively reconstructed image. Computer simulation test results are used to compare the performance of the proposed algorithm to other reported CS methods based on the Peak-Signal-to-Noise Ratio (PSNR) and the Structured SIMilarity (SSIM) measures. The results show that the proposed method yields an average PSNR improvement of 1.76 dB compared to the Orthogonal Matching Pursuit method (OMP). This translates to a 13% reduction in scan time for a given quality of the reconstructed image.

WSEAS Transactions on Signal Processing, ISSN / E-ISSN: 1790-5052 / 2224-3488, Volume 15, 2019, Art. #14

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Henry Kiragu, Elijah Mwangi, George Kamucha, "A Novel Compressive Sampling MRI Method Using Variable-Density k-Space Under-sampling and Substitution of Coefficients," WSEAS Transactions on Signal Processing, vol. 15, pp. 114-120, 2019, DOI:
Henry Kiragu, Elijah Mwangi, George Kamucha. A Novel Compressive Sampling MRI Method Using Variable-Density k-Space Under-sampling and Substitution of Coefficients. WSEAS Transactions on Signal Processing. 2019;15:114-120.
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