Convolution-Based Interpolation Kernels for Reconstruction of High Resolution EMR Images from Low Sampled k-Space Data

Electron magnetic resonance imaging (EMRI) is an emerging non-invasive imaging technology for mapping free radicals in biological systems. Unlike MRI, it is implemented as a pure phase-phase encoding technique. The fast bio-clearance of the imaging agent and the requirement to reduce radio frequency power deposition dictate collection of reduced k-space samples, compromising the quality and resolution of the EMR images. The present work evaluates various interpolation kernels to generate larger k-space samples for image reconstruction, from the acquired reduced k-space samples. Using k-space EMR data sets, acquired for phantom as well as live mice the proposed technique is critically evaluated by computing quality metrics viz. signal-to-noise ratio (SNR), standard deviation (STD), root mean square error (RMSE), peak signal to noise ratio (PSNR), contrast to noise ratio (CNR) and Lui ´s error function (F(I)). The quantitative evaluation of 24 different interpolation functions (including piecewise polynomial functions and many windowed sine functions) to upsample the k-space data for Fourier EMR image reconstruction shows that at the expense of a slight increase in computing time, the reconstructed images from upsampled data, produced using spline-sine, Welch-sine and Gaussian-sine kernels are closer to reference image with lesser distortion.