Application of Noise Invalidation Denoising in MRI

Magnetic Resonance Imaging (MRI) is a common medical imaging tool that have been used in clinical industry for diagnostic and research purposes. These images are subject to noises while capturing the data that can e ect the image quality and diagnostics.Therefore, improving the quality of the generated images from both resolution and signal to noise ratio (SNR) perspective is critical. Wavelet based denoising technique is one of the common tools to remove the noise in the MRI images. The noise is eliminated from the detailed coe cients of the signal in the wavelet domain. This can be done by applying thresholding methods. The main task here is to nd an optimal threshold and keep all the coe cients larger than this threshold as the noiseless ones. Noise Invalidation Denoising technique is a method in which the optimal threshold is found by comparing the noisy signal to a noise signature (function of noise statistics). The original NIDe approach is developed for one dimensional signals with additive Gaussian noise. In this work, the existing NIDe approach has been generalized for applications in MRI images with di erent noise distribution. The developed algorithm was tested on simulated data from the Brainweb database and compared with the well-known Non Local Mean ltering method for MRI. The results indicated better detailed structural preserving for the NIDe approach on the magnitude data while the signal to noise ratio is compatible. The algorithm shows an important advantageous which is less computational complexity than the NLM method. On the other hand, the Unbiased NLM technique is combined with the proposed technique, it can yield the same structural similarity while the signal to noise ratio is improved. KEYWORDs: Magnetic Resonance Imaging, Noise Invalidation Denoising, Unbiased Non Local Mean ltering, Wavelet Transform Function