Radial Undersampling-Based Interpolation Scheme for Multislice CSMRI Reconstruction Techniques.

Maria Murad,Ahmad Ali,Baber Khan,Muhammad Bilal,Shahid Ikram,Khizer Mehmood,Abdul Jalil,Marco Rengo

doi:10.1155/2021/6638588

Abstract

Magnetic Resonance Imaging (MRI) is an important yet slow medical imaging modality. Compressed sensing (CS) theory has enabled to accelerate the MRI acquisition process using some nonlinear reconstruction techniques from even 10% of the Nyquist samples. In recent years, interpolated compressed sensing (iCS) has further reduced the scan time, as compared to CS, by exploiting the strong interslice correlation of multislice MRI. In this paper, an improved efficient interpolated compressed sensing (EiCS) technique is proposed using radial undersampling schemes. The proposed efficient interpolation technique uses three consecutive slices to estimate the missing samples of the central target slice from its two neighboring slices. Seven different evaluation metrics are used to analyze the performance of the proposed technique such as structural similarity index measurement (SSIM), feature similarity index measurement (FSIM), mean square error (MSE), peak signal to noise ratio (PSNR), correlation (CORR), sharpness index (SI), and perceptual image quality evaluator (PIQE) and compared with the latest interpolation techniques. The simulation results show that the proposed EiCS technique has improved image quality and performance using both golden angle and uniform angle radial sampling patterns, with an even lower sampling ratio and maximum information content and using a more practical sampling scheme.

Highlights

The Shannon-Nyquist theorem [1] states that a signal can only be reconstructed from its k-space data if it has a sufficient number of samples, which are at minimum twice the maximum frequency in that signal
The radial sampling pattern used with efficient interpolated compressed sensing (EiCS) is more practical from the current hardware point of view compared to the 2D-VRDU sampling pattern adopted in FiCS
EiCS exploits the radial sampling pattern in its three-step interpolation process to get interpolated slices with the maximum number of samples from lower undersampling ratios, which ensures sharper reconstructed images compared to FiCS

Summary

Introduction

The Shannon-Nyquist theorem [1] states that a signal can only be reconstructed from its k-space data if it has a sufficient number of samples, which are at minimum twice the maximum frequency in that signal. The slow image acquisition process of MRI makes it inapplicable in emergency and accidental cases This time-consuming process causes a claustrophobic feeling, in pediatric patients, and it is difficult for them to be motionless and in the breath-held state for that long [2]. Unlike a normal MRI scan, where the acquired k-space data only require inverse Fourier transform, compressed sensing MRI (CSMRI) needs some nonlinear reconstruction techniques [10, 11] which are an additional computational overhead. This computational load is just a postacquisition process and does not bother a patient by compelling it to stay with the MRI machine. CS has been implemented using both Cartesian and non-Cartesian undersampling schemes [12, 17, 18]

Methods

Results

Conclusion