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    Publication Date: 2018-11-02
    Description: Head motion occurring during brain PET studies leads to image blurring and to bias in measured local quantities. The objective of this work was to implement a correction method for PET data acquired with the mMR synchronous PET/MR scanner. Methods: A list-mode–based motion-correction approach has been designed. The developed rebinner chronologically reads the recorded events from the Siemens list-mode file, applies the estimated geometric transformations, and frames the detected counts into sinograms. The rigid-body motion parameters were estimated from an initial dynamic reconstruction of the PET data. We then optimized the correction for 11 C-Pittsburgh compound B ( 11 C-PIB) scans using simulated and actual data with well-controlled motion. Results: An efficient list-mode–based motion correction approach has been implemented, fully optimized, and validated using simulated and actual PET data. The average spatial resolution loss induced by inaccuracies in motion parameter estimates and by the rebinning process was estimated to correspond to a 1-mm increase in full width at half maximum with motion parameters estimated directly from the PET data with a temporal frequency of 20 s. The results show that the rebinner can be safely applied to the 11 C-PIB scans, allowing almost complete removal of motion-induced artifacts. The application of the correction method to a large cohort of 11 C-PIB scans led to the following observations: first, that more than 21% of the scans were affected by motion greater than 10 mm (39% for subjects with Mini–Mental State Examination scores below 20), and second, that the correction led to quantitative changes in Alzheimer-specific cortical regions of up to 30%. Conclusion: The rebinner allows accurate motion correction at a cost of minimal resolution reduction. Application of the correction to a large cohort of 11 C-PIB scans confirmed the necessity of systematically correcting for motion to obtain quantitative results.
    Print ISSN: 0022-3123
    Topics: Medicine
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