Maximum entropy reconstruction methods in electron paramagnetic resonance imaging

Abstract

Electron Paramagnetic Resonance (EPR) is a spectroscopic technique that detects and characterizes molecules with unpaired electrons (i.e., free radicals). Unlike the closely related nuclear magnetic resonance (NMR) spectroscopy, EPR is still under development as an imaging modality. Athough a number of physical factors have hindered its development, EPR's potential is quite promising in a number of important application areas, including in vivo oximetry. EPR images are generally reconstructed using a tomographic imaging technique, of which filtered backprojection (FBP) is the most commonly used. We apply two iterative methods for maximum-entropy image reconstruction in EPR. The first is the multiplicative algebraic reconstruction technique (MART), a well-known row-action method. We propose a second method, known as LSEnt (least-squares entropy), that maximizes entropy and performs regularization by maintaining a desired distance from the measurements. LSEnt is in part motivated by the barrier method of interior-point programming. We present studies in which images of two physical phantoms, reconstructed using FBP, MART, and LSEnt, are compared. The images reconstructed using MART and LSEnt have lower variance, better contrast recovery, subjectively better resolution, and reduced streaking artifact than those reconstructed using FBP. These results suggest that maximum-entropy reconstruction methods (particularly the more flexible LSEnt) may be critical in overcoming some of the physical challenges of EPR imaging.