Statistical reconstruction (SR) methods provide a general and flexible framework for obtaining tomographic images from projections. For several applications SR has been shown to outperform analytical algorithms in terms of resolution -noise trade-off achieved in the reconstructions. A disadvantage of SR is the long computational time required to obtain the reconstructions, in particular when large data sets characteristic for x-ray computer tomography (CT) are involved. As was shown recently, by combining statistical methods with block iterative acceleration schemes [e.g., like in the ordered subsets convex (OSC) algorithm], the reconstruction time for x-ray CT applications can be reduced by about two orders of magnitude. There are, however, some factors lengthening the reconstruction process that hamper both accelerated and standard statistical algorithms to similar degree. In this simulation study based on monoenergetic and scatter-free projection data, we demonstrate that one of these factors is the extremely high number of iterations needed to remove artifacts that can appear around high-contrast structures. We also show (using the OSC method) that these artifacts can be adequately suppressed if statistical reconstruction is initialized with images generated by means of Radon inversion algorithms like filtered back projection (FBP). This allows the reconstruction time to be shortened by even as much as one order of magnitude. Although the initialization of the statistical algorithm with FBP image introduces some additional noise into the first iteration of OSC reconstruction, the resolution -noise trade-off and the contrast-to-noise ratio of final images are not markedly compromised.
- Iterative reconstruction
- Maximum likelihood
- Ordered subsets
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging