A novel method for reconstructing 3D spatial EPR images from large numbers of noisy projections was developed that minimizes mean square error between the experimental projections and those from the reconstructed image. The method utilizes raw projection data and zero gradient spectrum to account for EPR line shape and hyperfine structure of the paramagnetic probe without the need for deconvolution techniques that are poorly suited for processing of high noise projections. A numerical phantom was reconstructed for method validation. Reconstruction time for the matrix of 1283 voxels and 16,384 noiseless projections was 4.6 min for a single iteration. The algorithm converged quickly, reaching R2 ~ 0.99975 after the very first iteration. An experimental phantom sample with nitroxyl radical was measured. With 16,384 projections and a field gradient of 8 G/cm, resolutions of 0.4 mm were achieved for a cubical area of 25 × 25 × 25 mm3. Reconstruction was sufficiently fast and memory efficient making it suitable for applications with large 3D matrices and fully determined system of equations. The developed algorithm can be used with any gradient distribution and does not require adjustable filter parameters that makes for simple application. A thorough analysis of the strengths and limitations of this method for 3D spatial EPR imaging is provided.
Keywords: EPR imaging; Image reconstruction; Least-squares fitting; Nitroxide radicals; Spectral deconvolution.
Copyright © 2020 Elsevier Inc. All rights reserved.