Motion Correction of Multi-Frame PET Data

Abstract

Motion correction algorithms are necessary in PET studies where the patient cannot remain totally static. Otherwise, artifacts appear in the final reconstructed image, degrading the spatial resolution or even misplacing the radiation distribution source. In this work we propose a new enhanced motion correction algorithm (EMAF) based on the multiple acquisition frames (MAF). This methodology can be applied to two PET system geometries (a ring and a multi-panel system). Using simulated data, the comparison of static and motion corrected profiles shows minimum differences keeping more than 99% of the events after the process, with a minimal inter-frame error (the maximum range of loss counts is around 0.6%). To measure the quality of the correction method, two error metrics as peak signal to noise ratio (PSNR) and intensity matching precision (IMP) are proposed. A Mini-Derenzo in a ring PET system reveals a remarkable improvement both for these two metrics (44.5 dB and 98.7%) when compared to the uncorrected images (41.1 dB and 56%, respectively). An equivalent analysis for a point-like source also shows a sizeable enhancement both in contrast and resolution (64 dB and 98% versus 62.5 dB and 17.9%, PSNR and IMP correspondingly). The proposed algorithm minimizes the image artifacts and its simplicity, independency of PET configuration system and rapid reconstruction and registration times, makes it a useful tool in preclinical PET studies.