Optimized image reconstruction for emission tomography using ordered subsets, median root prior and a Web-based interface

The main objective of this work is the development and evaluation of iterative image reconstruction (IIR) methods for multitracer dynamic positron emission tomography (mdPET) studies. The ordered subsets (OS) technique applied for the acceleration of the maximum likelihood expectation maximization (ML-EM) IIR algorithm, is here extended to include the weighted-least squares (WLS), image space reconstruction algorithm (ISRA) and the space alternating generalized EM (SAGE). The median root prior (MRP) has been successfully applied as a Bayesian regularization to control the noise level in the reconstructed images. During the initial implementation, streak artifacts in the emission images were visible, especially on long attenuation paths. Iterative reconstruction of the transmission images with OS-acceleration and MRP regularization and subsequent calculation of the attenuation correction factors (ACFs) is shown to effectively remove these artifacts from the emission images. All methods are implemented on distributed Pentium systems and a Java-script is used for the initiation of the reconstruction. An efficient implementation for mdPET studies allows the reconstruction of one frame (63 slices) in a few minutes.