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.