Motion artifact reduction on parametric PET images of neuroreceptor binding.

UNLABELLED

PET studies of cerebral neuroreceptors are often recorded over periods ranging from 1 to 2 h, and head movements during the studies not only lead to blurred images but also may seriously disturb the kinetic analysis. We report the effect of motion on parametric images of the distribution volume ratio (DVR), as well as possible improvements if the dynamic PET data are corrected for head movements.

METHODS

The study was performed with the 5-hydroxytryptamine 2A receptor ligand (18)F-altanserin. During PET scanning, which was performed in list mode for 1 h, the position of the head was monitored by an infrared motion-tracking system. The list mode data were sorted into time frames of between 10 s and 2 min. Motion was corrected using the multiple-acquisition-frame (MAF) approach, which calculates individual attenuation files for each emission frame and its corresponding head position to avoid misalignment of transmission and emission data. After reconstruction of attenuation-corrected emission frames, each image frame was realigned to match the head position of the first frame of the emission scan. The resulting motion-corrected dynamic images were evaluated using the noninvasive Logan plot to obtain parametric images of DVR.

RESULTS

DVR images of motion-affected (18)F-altanserin scans showed artifacts whose extent depended on the amount of movement. The artifacts were mainly at the border between gray matter and white matter and at the outer border of gray matter. They were seen as discontinuities and small spots whose values exceeded the expected DVR values or were even negative and that disappeared when motion correction was applied. These effects in human data were also seen on simulated (18)F-altanserin images that contained no statistical noise.

CONCLUSION

Whereas the native PET images looked just blurred if the patient moved during the PET scan, parametric images of the Logan DVR, which are calculated by pixelwise linear regression, contained severe discontinuities primarily at the cortical edge. MAF-based motion correction was able to avoid these errors.