Optimization-based image reconstruction from sparsely sampled data in electron paramagnetic resonance imaging.

Electron paramagnetic resonance imaging (EPRI) can yield information about the 3-dimensional (3D) spatial distribution of the unpaired-electron-spin density from which the spatial distribution of oxygen concentration within tumor tissue, referred to as the oxygen image or electron paramagnetic resonance (EPR) image in this work, can be derived. Existing algorithms for reconstruction of EPR images often require data collected at a large number of densely sampled projection views, resulting in a prolonged data-acquisition time and consequently numerous practical challenges especially to in vivo animal EPRI. Therefore, a strong interest exists in shortening data-acquisition time through reducing the number of data samples collected in EPRI, and one approach is to acquire data at a reduced number of sparsely distributed projection views from which existing algorithms may reconstruct images with prominent artifacts. In this work, we investigate and develop an optimization-based technique for image reconstruction from data collected at sparsely sampled projection views for reducing scanning time in EPRI. Specifically, we design a convex optimization program in which the EPR image of interest is formulated as a solution and then tailor the Chambolle-Pock (CP) primal-dual algorithm to reconstruct the image by solving the convex optimization program. Using computer-simulated EPRI data from numerical phantoms and real EPRI data collected from physical phantoms, we perform studies on the verification and characterization of the optimization-based technique for EPR image reconstruction. Results of the studies suggest that the technique may yield accurate EPR images from data collected at sparsely distributed projection views, thus potentially enabling fast EPRI with reduced acquisition time.