X-ray luminescence computed tomography using a hybrid proton propagation model and Lasso-LSQR algorithm.

X-ray luminescence computed tomography (XLCT) uses external X-rays for luminescence excitation, which is becoming a promising molecular imaging technique with superb penetration depth and spatial resolution. To achieve the tomographic mapping of luminescence distribution, accurate optical propagation model and suitable reconstruction method are two keys for XLCT, but not satisfied. To overcome the limitation of the single proton propagation model (e.g., DE, SP ), we adopted a hybrid diffusion equation with third order simplified spherical harmonics (DE-SP ) model for XLCT. To enable fast iteration and accurate sparse reconstruction, we also integrated in the inversion optimization, with a novel Least Square QR-factorization based on the Lasso (Lasso-LSQR) algorithm. We first simulated the light propagation in various kinds of organs under DE model and SP model, respectively. By comparison with the Monte Carlo, these tissues can be categorized into two types, namely DE-fitted tissues that include muscle and lung, and SP -fitted tissues including heart, kidney, liver, and stomach. According to the above classification results, we built a hybrid DE-SP model to more accurately describing light transport. Numerical simulations and in vivo experiments illustrated that hybrid DE-SP model achieves superior reconstruction performance in terms of location accuracy, and spatial resolution than DE, and less computational cost than SP . The hybrid DE-SP model materializes a balance between accuracy and efficiency for XLCT.