Reconstruction of X-ray Fluorescence Computed Tomography from Sparse-View Projections via L1-norm Regularized EM Algorithm

X-ray fluorescence computed tomography (XFCT) as a molecular imaging modality can simultaneously identify the localization and quantify the concentration of high-atomic-number contrast agents such as gold nanoparticles (GNPs). Commonly used benchtop pencil-beam XFCT, consisting of a polychromatic x-ray source and a single-pixel spectrometer, suffers from long scanning time and high imaging dose. Sparse-view strategy benefits XFCT to reduce both scanning time and imaging dose. Nevertheless, its reconstruction undergoes ill-posedness induced by the compressive sampling. To preserve consistent imaging quality for sparse-view XFCT, we proposed an iterative Bayesian algorithm based on L1-norm constraint, wherein the L1-norm regularization is included in the one-step-late expectation maximization (OSL-EM) algorithm with regularization parameter determined based on L-curve criteria. The proposed algorithm was verified by imaging a 3-cm-diameter water phantom with 4 inserts containing GNP solutions with concentrations of 0.02, 0.04, 0.08, and 0.16 wt.%, on an in-house-developed dual-modality transmission CT and XFCT system. Different numbers (i.e. 36, 18, 9, and 6) of projection views were used for XFCT reconstruction, to evaluate the performance of various reconstruction algorithms. L1-regularized EM algorithm demonstrated the consistent robustness to suppress background artifacts and localize low-concentration GNPs (0.02 wt.%) with submillimeter accuracy, when the number of projection views reduces from 36 to 9. Moreover, our method’s potential for small tumor spare-view XFCT imaging was validated on a mouse surgically implanted with a 6-mm GNP target.