Motion correction in PET has become more important as system resolution has improved. The purpose of this study was to evaluate the accuracy of event-by-event and frame-based MC methods in human brain PET imaging.
Motion compensated image reconstructions were performed with static and dynamic simulated high resolution research tomograph data with frame-based image reconstructions, using a range of measured human head motion data. Image intensities in high-contrast regions of interest (ROI) and parameter estimates in tracer kinetic models were assessed to evaluate the accuracy of the motion correction methods.
Given accurate motion data, event-by-event motion correction can reliably correct for head motions. The average ROI intensities and the kinetic parameter estimatesV T and BP ND were comparable to the true values. The frame-based motion correction methods with correctly aligned attenuation map using the average of externally acquired motion data or motion data derived from image registration give comparable quantitative accuracy. For large intraframe (>5 mm) motion, the frame-based methods produced ∼9% bias in ROI intensities, ∼5% in V T, and ∼10% in BP ND estimates. In addition, in real studies that lack a ground truth, the normalized weighted residual sum of squared difference is a potential figure-of-merit to evaluate the accuracy of motion correction methods.
The authors conclude that frame-based motion correction methods are accurate when the intraframe motion is less than 5 mm and when the attenuation map is accurately aligned. Given accurate motion data, event-by-event motion correction can reliably correct for head motion in human brain PET studies.
The authors thank Zhongdong Sun for programming support and the staff of the Yale-PET Center for the studies that formed the basis of this work. This work was supported by Grant No. R01NS058360 from the National Institute of Neurological Disorders and Stroke. This publication was also made possible by CTSA Grant No. UL1 RR024139 from the National Center for Research Resources (NCRR) and the National Center for Advancing Translational Science (NCATS), components of the National Institutes of Health (NIH), and NIH roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of NIH.
II. MATERIALS AND METHODS
II.A. Simulation of list-mode HRRT data
II.A.1. Simulation of static list-mode HRRT data
II.A.2. Simulation of dynamic list-mode HRRT data
II.B. Motion data
II.C. Image reconstruction
II.D. Motion correction methods
II.E. Image analysis and parameter estimation
II.E.1. ROI intensity quantification
II.E.2. Statistical analysis
II.E.3. Parameter estimation in tracer kinetic model
II.E.4. Weighted residual sum of square difference between TACs
III.A. Static study
III.A.1. Static images
III.A.2. ROI quantification
III.A.3. Statistical analysis
III.B. Dynamic study
III.B.1. Parametric images
III.B.2. Parameter estimates
III.B.3. WRSS between raw TAC and fitted curve
IV.A. Quantification of motion
IV.B. Comparison of the motion correction methods
IV.C. ROI-dependence of motion
IV.D. Accuracy of motion correction in real studies
IV.E. Effects of frame duration on motion
IV.F. Effects of random and scattered events on motion correction
IV.G. Notable features of the MOLAR algorithm
- Image reconstruction
- Image registration
- Positron emission tomography
- Medical imaging
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