Denoising of Rician noise in Magnitude MRI Images using wavelet shrinkage and fusion method

Improving the signal-to-noise-ratio (SNR) of magnetic resonance imaging (MRI) using denoising techniques could enhance their value, provided that signal statistics and image resolution are not compromised. Here, a new denoising method based on wavelet based bayes shrinkage method of the measured noise power from each signal acquisition is presented. Bayes shrink method denoising assumes no prior knowledge of the acquired signal and does not increase acquisition time. Whereas conventional denoising/filtering methods are compromised in parallel imaging by spatially dependent noise statistics, wavelet based method is performed on signals acquired from MRI. Using numerical simulations, we show that proposed method can improve SNR in MRI reconstructed images without compromising image resolution. Application of Wavelet to MRI knee and DWI which achieved SNR improvements compared to conventional reconstruction. Comparison of Wavelet with standard filtering shows comparable SNR enhancement at low and high-SNR level and shows improved accuracy and retention of structural detail at a reduced computational load. The proposed methodology can be applied on final MRI reconstructed images. We have compared the performance of Bayes shrink combined with fusion to the normal thresholding techniques in order to enhance the visual quality of the image for proper diagnosis of disease.