Intra-frame Motion Compensation in Multi-frame Brain PET Imaging

Abstract

Purpose: Inter-frame and intra-frame motion can adversely impact the performance of dynamic brain PET imaging. Only correcting the former can still result in degraded qualitative and quantitative performance. Meanwhile, patient motion introduces mismatches between transmission and emission data which may lead to incorrect attenuation and scatter compensation in the reconstruction process. As a result, the reconstructed dynamic images may carry erroneous estimates of radioactivity distribution. We seek a solution to this problem.
Methods: We investigated the use of iterative deconvolution coupled with a proposed use of time-weighted averaging of motion-transformed transmission images to correct the transmission-emission mismatch artifacts in dynamic brain PET images. We performed simulations using real-patient motion profile acquired by the infrared Polaris Vicra motion tracking device which estimates 3-D motion transformations during PET acquisition. This was followed by frame-based motion correction employing three different transmission-emission alignment strategies: transmission image transformed by (1) mean motion transformation, (2) median motion transformation, and (3) the proposed time-weighted average of motion-transformed transmission images.
Results-: The results demonstrate that the proposed approach of using time-weighted averaging of motion transformed transmission images outperforms conventional methods by substantially reducing the transmission-emission mismatch artifacts in the reconstructed images. Coupled with an alignment of the reconstructed frames for inter-frame motion correction and a subsequent iterative deconvolution approach for intra-frame motion correction, the resulting motion compensated images showed superior quality, considerable reduction in error norm and enhanced noise-bias performance compared to conventional methods of transmission-emission mismatch compensation. The performance was consistent across different levels of intra-frame motion, and the algorithm was amenable to different framing schemes.
Conclusion: In frame-based motion correction of dynamic PET images, it is feasible to achieve intra-frame motion compensation using time-weighted averaging of motion transformed transmission images coupled with a post-reconstruction iterative deconvolution procedure to compensate for intra-frame motion.