Regularized reconstruction based on joint L1 and total variation for sparse-view cone-beam X-ray luminescence computed tomography

Abstract

As an emerging hybrid imaging modality, cone-beam X-ray luminescence computed tomography (CB-XLCT) has been proposed based on the development of X-ray excitable nanoparticles. Owing to the high degree of absorption and scattering of light through tissues, the CB-XLCT inverse problem is inherently ill-conditioned. Appropriate priors or regularizations are needed to facilitate reconstruction and to restrict the search space to a specific solution set. Typically, the goal of CB-XLCT reconstruction is to get the distributions of nanophosphors in the imaging object. Considering that the distributions of nanophosphors inside bodies preferentially accumulate in specific areas of interest, the reconstruction of XLCT images is usually sparse with some locally smoothed high-intensity regions. Therefore, a combination of the L1 and total variation regularization is designed to improve the imaging quality of CB-XLCT in this study. The L1 regularization is used for enforcing the sparsity of the reconstructed images and the total variation regularization is used for maintaining the local smoothness of the reconstructed image. The implementation of this method can be divided into two parts. First, the reconstruction image was reconstructed based on the fast iterative shrinkage-thresholding (FISTA) algorithm, then the reconstruction image was minimized by the gradient descent method. Numerical simulations and phantom experiments indicate that compared with the traditional ART, ADAPTIK and FISTA methods, the proposed method demonstrates its advantage in improving spatial resolution and reducing imaging time.