Reconstruction of electron paramagnetic resonance images using iterative methods

Electron Paramagnetic Resonance (EPR) allows for the non-invasive imaging of free radicals in biological systems. Although a number of physical factors have hindered the development of EPR as an imaging modality, EPR offers the potential for tissue oxymetry. EPR images are typically reconstructed using a traditional filtered back-projection technique. We are attempting to improve the quality of EPR images by using maximum-entropy based iterative image reconstruction algorithms. Our investigation has so far focused on two methods, the multiplicative algebraic reconstruction technique (MART), and an algorithm that is motivated by interior-point reconstruction. MART is a row-action method that maintains strict equality in the constraints while minimizing the entropy functional. The latter method, which we have named Least-Squares Barrier Entropy (LSBEnt), transforms the constrained problem into an unconstrained problem and maximizes entropy at a prescribed distance from the measured data. EPR studies are frequently characterized by low signal-to-noise ratios and wide line widths. The effect of the backprojection streaking artifact can be quite severe and can seriously compromise a study. We have compared the iterative results with filtered backprojection on two-dimensional (2-D) EPR acquisitions of various phantoms. Encouraging preliminary results have demonstrated that one of the clear advantages of the iterative methods is their lack of streaking artifacts that plague filtered backprojection.