A RESEARCH PAPER ON DENOISING MULTI-CHANNEL IMAGES IN PARALLEL MRI BY LOW RANK MATRIX DECOMPOSITION AND LOCAL PIXEL GROUPING WITH PRINCIPAL COMPONENT ANALYSIS

Parallel magnetic resonance imaging has emerged as an effective means for high-speed imaging in various applications. The reconstruction of parallel magnetic resonance imaging (pMRI) data can be a computationally demanding task. Signal-to-noise ratio is also a concern, especially in high-resolution imaging. We present a patchwise Denoising method for pMRI by exploiting the rank deficiency of multichannel images. For each processed patch and pixel, similar patches are searched with pixel in spatial domain and throughout all coil elements, and arranged in appropriate matrix forms. Then, noise and aliasing artifacts are removed from the structured matrix by applying sparse and low rank matrix decomposition method with Local Pixel Grouping using Principal Component Analysis (PCA). The proposed method has been validated using both phantom and in vivo brain data sets, producing encouraging results. Specifically, the method can effectively remove both noise and residual aliasing artifact from pMRI reconstructed noisy images, and produce higher peak signal noise rate (PSNR) and structural similarity index matrix (SSIM) than other state-of-the-art Denoising methods. The Denoising of pMRI is implemented using Image Processing Toolbox. This work has been tested and found suitable for its purpose. For the implementation of this proposed work we use the Matlab software.