MR image reconstruction based on compressed sensing using Poisson sampling pattern

Abstract

Magnetic Resonance Imaging is a medical imaging modality used to produce good quality images of soft tissue in ligaments and other internal body organs. MRI is non-invasive scanning technique based on the principle of Nuclear Magnetic Resonance. The MRI scan time depends on the size of the scanned area and the number of images being reconstructed. This scan time reduction may reduce the artifacts in the reconstruction by improving the patient comfort. Compressed sensing (CS) theory helps MRI to reduce the scan time by reconstructing MR images with fewer sampled measurements. Application of CS to MRI gives acceleration in MR image acquisition. This paper focuses on randomly under sampled k-space data and use of CS-MR image reconstruction. This work compares variable density mask and Poisson mask and show their usefulness in Compressed Sensing applied to MRI image reconstruction. Image reconstruction using Nonlinear conjugate gradient method has been performed on the cardiac dataset at different acceleration factors. Further in the paper, reconstructed images are quantified by Peak Signal To Noise Ratio (PSNR).