Direct Parametric Reconstruction With Joint Motion Estimation/Correction for Dynamic Brain PET Data.

Area of Science:

• Medical Imaging
• Nuclear Medicine
• Biophysics

Background:

• Direct reconstruction of parametric images from raw positron emission tomography (PET) data improves quantitative analysis.
• Subject motion during PET scans (1-2 hours) significantly degrades image quality and quantitative accuracy.
• Existing methods struggle to effectively address motion artifacts in dynamic PET imaging.

Purpose of the Study:

• To develop a framework for joint estimation of subject motion and direct reconstruction of motion-corrected parametric images from raw PET data.
• To reduce the impact of motion-induced tissue-to-voxel mapping distortions.
• To improve the accuracy of quantitative analysis in dynamic PET studies affected by motion.

Main Methods:

• Formulation of a joint motion estimation and direct parametric reconstruction within a maximum likelihood framework.
• Development of efficient algorithms for estimating motion and kinetic parameters from raw PET photon count data.
• Incorporation of motion-compensated attenuation correction and spatially aligned temporal PET data.

Main Results:

• Evaluations on simulated [¹¹C]raclopride data (Zubal brain phantom) and real clinical [¹⁸F]florbetapir data (Alzheimer's disease patient) were performed.
• The proposed joint direct parametric reconstruction and motion correction approach demonstrated improved accuracy in quantifying dynamic PET data.
• Significant improvements were observed even in the presence of substantial subject motion.

Conclusions:

• The developed framework effectively addresses the challenge of subject motion in dynamic PET imaging.
• Joint direct parametric reconstruction with motion correction offers enhanced accuracy for quantitative PET analysis.
• This method holds promise for more reliable diagnosis and monitoring in neurological disorders using PET.