MRI Phase Image Unwrapping using DCT Based Modified Weighted Least Square Algorithm

Abstract

Background: Phase images of magnetic resonance imaging (MRI) have applications in many fields, including the medical domain. It is often employed to identify biomarkers of neurodegenerative diseases such as Alzheimer's, Parkinson's, and others. However, directly extracted phase images from MRI exhibit the wrapped phase values within the ±π radian range. Methods: To circumvent these phase jumps or discontinuity, phase unwrapping is required. Path-following and minimum norms algorithms are unwrapping methods to retrieve the original unwrapped phase image. The path-following algorithm extracts the original phase value by considering the adjacent pixels along the integral path. In contrast, the minimum norms algorithm aims to minimize the difference between the partial derivatives of the wrapped and the unwrapped phase data. This paper presents the DCT-based modified minimum norm-based weighted least square (LS) phase unwrapping to improve the visibility and noise immunity of the phase images. The proposed algorithm eradicates residual noise by imposing spectral truncation of the low-frequency coefficient. Results and Conclusions: For the experimental validation of the proposed algorithm, the wrapped and unwrapped image phase profiles are demonstrated to show the effectiveness of the proposed phase unwrapping algorithm. In addition, the performance metrics, peak signal-to-noise ratio (PSNR), structural similarity index metric (SSIM), and root mean squared error (RMSE) are calculated to show the comparison of the proposed phase unwrapping algorithm with the state-of-the-art techniques.