Acceleration of ML iterative algorithms for CT by the use of fast start images

Abstract

This report develops a new strategy for the acceleration of a maximum likelihood (ML) iterative reconstruction algorithm for CT, by selecting a starting image which is closer to the solution of the ML algorithm than the commonly used filtered backprojection image. The starting image is obtained by passing both the acquired projection data and the reconstructed volume though a novel de-noising algorithm which uses the same image penalty function as the ML reconstruction. Clinical examples suggest that a savings of 5-10 iterations of the separable paraboloidal surrogates algorithm per volume is possible when using this new acceleration strategy.