Compressive sensing MRI reconstruction using empirical wavelet transform and grey wolf optimizer

Abstract

Magnetic resonance imaging (MRI) has exhibited an outstanding performance in the track of medical imaging compared to several imaging modalities, such as X-ray, positron emission tomography and computed tomography. MRI modality suffers from protracted scanning time, which affects the psychological status of patients. This scanning time also increases the blurring levels in MR image due to local motion actions, such as breathing as in the case of cardiac imaging. An acquisition technique called compressed sensing has contributed to solve the drawbacks of MRI and decreased the acquisition time by reducing the quantity of the measured data that is needed to reconstruct an image without significant degradation in image quality. All recent works have used different types of conventional wavelets for sparsifying the image, which employ constant filter banks that are independent of the characteristics of the input image. This paper proposes to use the empirical wavelet transform (EWT) which tunes its filter banks to the characteristics of the analyzed images. In other words, we use EWT to produce a sparse representation of the MRI images which yields a more accurate sparsification transform. In addition, the grey wolf optimizer is used to optimize the parameters of the proposed method. To validate the proposed method, we use three MRI datasets of different organs: brain, cardiac and shoulder. The experimental results show that the proposed method outperforms the state-of-the-art methods in terms of signal-to-noise ratio and structure similarity metrics.