Acceleration of image-based resolution modelling reconstruction using an expectation maximization nested algorithm

• Published in: Physics in medicine & biology 2013-08, Vol.58 (15), p.5061-5083
• Main Authors: Angelis, G I, Reader, A J, Markiewicz, P J, Kotasidis, F A, Lionheart, W R, Matthews, J C
• Format: Article
• Language: English
• Subjects: Algorithms; Fluorodeoxyglucose F18; Humans; Image Processing, Computer-Assisted - methods; Imaging, Three-Dimensional; Models, Theoretical; Phantoms, Imaging; Positron-Emission Tomography; Time Factors
• ISSN: 0031-9155; 1361-6560
• DOI: 10.1088/0031-9155/58/15/5061

Recent studies have demonstrated the benefits of a resolution model within iterative reconstruction algorithms in an attempt to account for effects that degrade the spatial resolution of the reconstructed images. However, these algorithms suffer from slower convergence rates, compared to algorithms where no resolution model is used, due to the additional need to solve an image deconvolution problem. In this paper, a recently proposed algorithm, which decouples the tomographic and image deconvolution problems within an image-based expectation maximization (EM) framework, was evaluated. This separation is convenient, because more computational effort can be placed on the image deconvolution problem and therefore accelerate convergence. Since the computational cost of solving the image deconvolution problem is relatively small, multiple image-based EM iterations do not significantly increase the overall reconstruction time. The proposed algorithm was evaluated using 2D simulations, as well as measured 3D data acquired on the high-resolution research tomograph. Results showed that bias reduction can be accelerated by interleaving multiple iterations of the image-based EM algorithm solving the resolution model problem, with a single EM iteration solving the tomographic problem. Significant improvements were observed particularly for voxels that were located on the boundaries between regions of high contrast within the object being imaged and for small regions of interest, where resolution recovery is usually more challenging. Minor differences were observed using the proposed nested algorithm, compared to the single iteration normally performed, when an optimal number of iterations are performed for each algorithm. However, using the proposed nested approach convergence is significantly accelerated enabling reconstruction using far fewer tomographic iterations (up to 70% fewer iterations for small regions). Nevertheless, the optimal number of nested image-based EM iterations is hard to be defined and it should be selected according to the given application.