To Analyse the Effect of Relaxation Type on Magnetic Resonance Image Compression Using Compressive Sensing

Abstract

Medical Imaging and scanning technologies are used to provide better resolution of body and tissues. To achieve a better quality Magnetic Resonance (MR) image with a minimum duration of processing time is a tedious task. So our purpose in this paper is to find out a solution that can minimize the reconstruction time of an MRI signal. Compressive sensing can be used to accelerate Magnetic Resonance Image (MRI) acquisition by acquiring fewer data through the under-sampling of k-space, so it can be used to minimize the time. But according to the relaxation time, we can further classify the MRI signal into T1, T2, and Proton Density (PD) weighted images. These weighted images represent different signal intensities for different types of tissues and body parts. It also affects the reconstruction process conducted by using the Compressive Sensing Approach. This study is based on finding out the effect of T1, T2, and Proton Density (PD) weighted images on the reconstruction process as well as various image quality parameters like MSE, PSNR, & SSIM also calculated to analyze this effect. Meanwhile, we can analyze how many samples are enough to reconstruct the MR image so the problem associated with time and scanning speed can be reduced up to an extent. In this paper, we got the Structural Similarity Index Measure (SSIM) value up to 0.89 & PSNR value 37.83451 dB at an 85 % compression ratio for the T2 weighted image.