Compressed Sensing-Based MRI Reconstruction Using Complex Double-Density Dual-Tree DWT

Abstract EN

Undersampling k-space data is an efficient way to speed up the magnetic resonance imaging (MRI) process.

As a newly developed mathematical framework of signal sampling and recovery, compressed sensing (CS) allows signal acquisition using fewer samples than what is specified by Nyquist-Shannon sampling theorem whenever the signal is sparse.

As a result, CS has great potential in reducing data acquisition time in MRI.

In traditional compressed sensing MRI methods, an image is reconstructed by enforcing its sparse representation with respect to a basis, usually wavelet transform or total variation.

In this paper, we propose an improved compressed sensing-based reconstruction method using the complex double-density dual-tree discrete wavelet transform.

Our experiments demonstrate that this method can reduce aliasing artifacts and achieve higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index.