Regularized optimization (RO) reconstruction for oximetric EPR imaging

Abstract

A new algorithm for EPR imaging oximetry is described and tested with experimental data for the case of one spatial and one spectral dimension. A single species with variable linewidth is assumed. Instead of creating a 2D image, two one-dimensional profiles are reconstructed: the concentration of the radical and the corresponding oxygen concentration, which reduces the dimensionality of the problem. The algorithm (i) seeks to minimize the discrepancy between experimental data and projections calculated from the profiles and (ii) uses Tikhonov regularization to constrain the smoothness of the results. This approach controllably smoothes profiles rather than the data, while preserving sharp features.