Iterative CT reconstruction integrating SART and conjugate gradient

Iterative CT reconstruction methods have advantages over analytical reconstruction methods because of their robustness to both noise and incomplete projection data, which have great potential for dose reduction in real applications. The SART algorithm, which is one of the well-established iterative reconstruction methods, has been examined extensively, and GPU has been applied to improve their efficiency. Although it has been proved that SART may globally converge, its convergence is very slow, especially after the first several iterations. Hundreds of iterations may be needed for accurate reconstruction. This slow convergence requires heavy data transfer between global memory and texture memory inside GPU. Therefore, preconditioned conjugate gradient (CG) method, which converges much faster than SART, may be combined with SART for better performance. Since CG is sensitive to initialization, the reconstruction results from SART after a few iterations may be used as the initialization for CG. Preliminary experimental results on CPU show that this framework converges much faster than SART and CG, which demonstrates its potential in real applications.