||Comparative Study of Compressive Sensing Approach in T1, T2, and Proton Density-based MRI Images
||(Vivek Upadhyaya) ; (Mohammad Salim)
|| Compressive sensing (CS); Magnetic resonance imaging (MRI); Structural similarity index (SSIM); Feature similarity index measure (FSIM)
||Magnetic resonance imaging (MRI) is one of the imaging techniques that is very useful for the imaging of soft body tissues. But a problem associated with MRI is the slow processing and time-consuming process. Therefore, in this work, we tried to find out a way to use a compressive sensing approach that can reduce the number of samples that are required to reconstruct an MR image by using k-space. In this work, we also discuss the three types of MR weighted images and try to find out how the types of MR images affect the image quality assurance matrices and the recovered MR images. These matrices also indicate that by using a few numbers of samples from the compressed image, we can reproduce an MR image that is very close to the actual MR image. In this study, we used five body-part MR images in an experiment. One of the very interesting outcomes of this work is that the T2-type recovered MR weighted images are very close to the actual MR images.