Development of a Dedicated Rebinner with Rigid Motion Correction for the mMR PET/MR Scanner, and Validation in a Large Cohort of ¹¹C-PIB Scans

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. 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 forC-Pittsburgh compound B (C-PIB) scans using simulated and actual data with well-controlled motion. 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 theC-PIB scans, allowing almost complete removal of motion-induced artifacts. The application of the correction method to a large cohort of 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%. The rebinner allows accurate motion correction at a cost of minimal resolution reduction. Application of the correction to a large cohort of C-PIB scans confirmed the necessity of systematically correcting for motion to obtain quantitative results.

Longitudinal trajectory of Amyloid‐related hippocampal subfield atrophy in nondemented elderly

“…For MACC PET data, motion correction was performed prior to tomographic reconstruction into a single static frame, using an in‐house developed rebinner with rigid motion correction, binning into 20s frames (Reilhac et al, ). Motion corrected images were reconstructed into a volume of 344 × 344 × 344 voxels (voxel size = 2.09 × 2.09 × 2.03 mm 3 ) using 3D Ordinary Poisson Ordered‐subsets Expectation Maximization algorithm (Panin, Kehren, Michel, & Casey, ) with all corrections performed (including resolution modeling) and using three iterations and 21 subsets.…”

Improved amyloid burden quantification with nonspecific estimates using deep learning

“…All subjects underwent a 30-min PET scan on the 3 T Biograph mMR (Siemens Healthcare GmbH) at the Clinical Imaging Research Centre (CIRC), 40 min after injection of 370 (± 15%) MBq of [ 11 C]PiB. Each PET listmode datum was motion-corrected using an in-house developed rebinner [20] and reconstructed into a 3D matrix of 344 × 344 × 127 with voxel size of 2.09 × 2.09 × 2.03 mm 3 . 3D ordinary Poisson ordered-subsets expectation maximization with resolution modeling using 3 iterations and 21 subsets was applied with all corrections.…”

MRI-Driven PET Image Optimization for Neurological Applications

“…MRI provides high brain soft-tissue contrast and temporal resolution, which makes it ideal for demonstrating the dynamic head motion. With the involvement of PET and MRI dual-modalities in brain imaging, MRI-driven MC (MRMC) for voluntary head motion during a lengthy PET study can be crucial in maintaining the alignment between a series of PET and MRI images (Reilhac et al, 2018). Hybrid PET/MRI system allows for acquiring MRI images with dynamically changing anatomy at well as list-mode PET acquisition.…”

“…Several researchers suggested that direct reconstruction using event-by-event motion correction for dynamic brain PET is achievable and robust (Germino et al, 2017; Jiao et al, 2017). On the other hand, accurate MC may be achieved at a cost of resolution loss to some extent (Reilhac et al, 2018). These issue may also be solved by the combination of MRI navigator sequence and optical motion tracking system, which complement each other’s advantages.…”