Evolutionary Computational Approach for Artifact-Free Image Reconstruction from Reduced Samples: Application to Fourier EMRI

Electron magnetic resonance imaging (EMRI) is emerging as a potential biomedical-imaging technology for noninvasive imaging of free radicals in biological systems. EMRI by single point imaging (SPI) modality is a Fourier imaging technique. The bioclearance of the imaging agent as well as the need to minimize the radio frequency (RF) power deposition on the live animals, dictate reduced k-space sampling. This leads to ringing (Gibbs) artifacts which are severe and seen in both directions of the 2D image, because, unlike the conventional MRI, SPI is phase encoding in both directions. To dampen the high frequency components, data tapering windows are multiplicatively applied to provide tolerable blurred resultant image with reduced Gibbs ringing. To find a compromise between blur and ringing artifact, in this paper a method of optimizing the window functions by using genetic algorithm (GA) is proposed. The proposed algorithm is validated, using Matlab codes, first reconstructing MRI image of Shepp Logan head phantom from simulated k-space data, and next tested by reconstructing real EMR images of phantoms and live mice from experimentally acquired k-space data. Image quality metrics viz. signal to noise ratio (SNR), contrast to noise ratio (CNR), peak signal to noise ratio (PSNR), root mean square error (RMSE), standard deviation (STD) and the Liu's error function F(I) are computed. Our experiments suggest GA-based Kaiser window provides good optimal blur/ringing compromise in EMR tomograms.