Hybrid PET-MR list-mode kernelized expectation maximization reconstruction for quantitative PET images of the carotid arteries

Ordered subsets expectation maximization (OSEM) has been widely used in PET imaging. Although Bayesian algorithms have been shown to perform better, they are still not used in the clinical practice due to the difficulty of choosing appropriate and robust regularization parameters. The recently introduced kernelized expectation maximization (KEM) has shown some promise to work successfully for different applications. Therefore, we propose a list mode hybrid KEM (LM-HKEM) for static reconstructions, which we implemented in the open source Software for Tomographic Image Reconstruction (STIR) library. The proposed algorithm uses both MR and PET update images to create a feature vector for each voxel in the image, which contains the information about the local neighborhood. So as not to over-smooth the reconstructed images a 3×3×3 voxels kernel was used. Three real datasets were acquired with the Siemens mMR: a phantom to validate the algorithm and two patient carotid artery studies to show the possible applications of the method. The reconstructed images are assessed and compared for different algorithms: OSEM, OSEM with median root prior (MRP), KEM and LM-HKEM. The results show better quantification performance for the phantom low count images with around 4% bias compared to 7% for KEM and over 11% for OSEM and OSEM with (MRP). Our results show that the proposed technique can be used to improve quantification at low- count condition and it shows promising performance in terms of stability as for different subsets, with comparable number of events, we used the same parameters values. Emphasis is given on the reconstruction of the carotid artery and the characterization of atherosclerosis.