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Published on: June 14, 2018
Development of a Dedicated Rebinner with Rigid Motion Correction for the mMR PET/MR Scanner, and Validation in a
Anthonin Reilhac1,2, Inés Merida2, Zacharie Irace2
1Clinical Imaging Research Centre, A*STAR-NUS, Singapore anthonin_reilhac@circ.a-star.edu.sg.
Abstract:
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 11C-Pittsburgh compound B (11C-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 11C-PIB scans, allowing almost complete removal of motion-induced artifacts. The application of the correction method to a large cohort of 11C-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 11C-PIB scans confirmed the necessity of systematically correcting for motion to obtain quantitative results.
Insights
Head motion during brain PET scans causes blurring and bias. This study developed a list-mode motion correction method for PET/MR scanners, significantly improving image accuracy and quantitative results for 11C-Pittsburgh compound B scans.
Area of Science:
- Neuroimaging
- Medical Physics
- Radiochemistry
Background:
- Head motion during Positron Emission Tomography (PET) studies degrades image quality, causing blurring and quantitative bias.
- Accurate quantification in PET imaging is crucial for diagnosing and monitoring neurodegenerative diseases like Alzheimer's.
Purpose of the Study:
- To implement and validate a list-mode based motion correction method for PET data acquired on a simultaneous PET/MR scanner.
- To assess the impact of motion correction on quantitative accuracy and image resolution using 11C-Pittsburgh compound B (11C-PIB) scans.
Main Methods:
- A list-mode rebinner was developed to process PET events, apply motion transformations, and reconstruct sinograms.
- Rigid-body motion parameters were estimated from dynamic PET data, and correction was optimized using simulated and real 11C-PIB scans.
- The method was applied to a large cohort of 11C-PIB scans to evaluate its effectiveness and impact.
Main Results:
- The motion correction method effectively removed motion-induced artifacts in 11C-PIB scans with minimal loss of spatial resolution (approx. 1 mm FWHM increase).
- Over 21% of scans showed motion exceeding 10 mm, with 39% in subjects with cognitive impairment (MMSE < 20).
- Motion correction resulted in quantitative changes up to 30% in Alzheimer's-specific cortical regions.
Conclusions:
- The developed list-mode rebinner provides accurate head motion correction for PET/MR imaging with negligible impact on image resolution.
- Systematic motion correction is essential for obtaining reliable quantitative results in 11C-PIB PET studies, particularly in patient populations prone to motion.
- This method enhances the diagnostic utility of PET imaging for neurodegenerative diseases by improving data accuracy.
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