Variational method for motion corrected reconstruction with MRI information in positron emission tomography

Abstract

Motion-corrected image reconstruction approaches receive arising attention in nuclear medicine. With new hybrid scanners like PET/MRI the combination of multiple sources for motion information is possible. In current practice, separate gated data is reconstructed. Single gated reconstructions, in which the motion effect is negligible have inferior quality. The low quality is caused by bad signal-to-noise ratio in gated PET data. The motion in these gated images, in comparison to one reference gate, can be corrected successively and then obtained by overlaying. Disadvantages are artifacts caused by unclear convergence properties of the alternating iteration method of the reconstruction and the motion estimation. To solve this, we use a novel variational approach for motion estimation and image reconstruction of the PET data, based on a Bayesian model. With MRI information we receive more precise motion information, so that we can improve the PET motion estimation with MRI motion data. To verify this approach, the first experiments were done with the XCAT thorax phantom to evaluate results. We expand our experiments to real patient data to demonstrate its feasibility for clinical use. In conclusion, the results in our approach shows a more reasonable tracer distribution and a better reconstruction of the myocardium. The motion estimation is as good as the state-of-the-art motion correction with some benefits. The outlook deals with the problem of attenuation correction in motion-corrected PET reconstruction which has severe impact on the quality of reconstructed images, and with other options to improve our method.