Convergence analysis of a polyenergetic SART algorithm

TL;DR

This paper analyzes the convergence properties of the polyenergetic SART algorithm (pSART), revealing that it is not guaranteed to converge theoretically but tends to do so in practical CT imaging scenarios.

Contribution

The paper provides a mathematical convergence analysis of pSART, showing conditions under which it converges or diverges, and compares it to the classical SART algorithm.

Findings

pSART is not guaranteed to converge in general.

Convergence depends on system matrix and energy modeling.

Numerical experiments suggest practical convergence in CT applications.

Abstract

Purpose: We analyze a recently proposed polyenergetic version of the simultaneous algebraic reconstruction technique (SART). This algorithm, denoted pSART, replaces the monoenergetic forward projection operation used by SART with a post-log, polyenergetic forward projection, while leaving the rest of the algorithm unchanged. While the proposed algorithm provides good results empirically, convergence of the algorithm was not established mathematically in the original paper. Methods: We analyze pSART as a nonlinear fixed point iteration by explicitly computing the Jacobian of the iteration. A necessary condition for convergence is that the spectral radius of the Jacobian, evaluated at the fixed point, is less than one. A short proof of convergence for SART is also provided as a basis for comparison. Results: We show that the pSART algorithm is not guaranteed to converge, in general. The Jacobian of the iteration depends on several factors, including the system matrix and how one models the energy dependence of the linear attenuation coefficient. We provide a simple numerical example that shows that the spectral radius of the Jacobian matrix is not guaranteed to be less than one. A second set of numerical experiments using realistic CT system matrices, however, indicates that conditions for convergence are likely to be satisfied in practice. Conclusion: Although pSART is not mathematically guaranteed to converge, our numerical experiments indicate that it will tend to converge at roughly the same rate as SART for system matrices of the type encountered in CT imaging. Thus we conclude that the algorithm is still a useful method for reconstruction of polyenergetic CT data.