Data-driven motion detection and event-by-event correction for brain PET: Comparison with Vicra

Abstract

Head motion degrades image quality and causes erroneous parameter estimates in tracer kinetic modeling in brain PET studies. Existing motion correction methods include frame-based image-registration (FIR) and correction using real-time hardware-based motion tracking (HMT) information. However, FIR cannot correct for motion within one predefined scan period while HMT is not readily available in the clinic since it typically requires attaching a tracking device to the patient. In this study, we propose a motion correction framework with a data-driven algorithm, i.e., using the PET raw data itself, to address these limitations. Methods: We propose a data-driven algorithm, Centroid of Distribution (COD), to detect head motion. In COD, the central coordinates of the line of response (LOR) of all events are averaged over 1-sec intervals to generate a COD trace. A point-to-point change in the COD trace in one direction that exceeded a user-defined threshold was defined as a time point of head motion, which was followed by manually adding additional motion time points. All the frames defined by such time points were reconstructed without attenuation correction and rigidly registered to a reference frame. The resulting transformation matrices were then used to perform the final motion compensated reconstruction. We applied the new COD framework to 23 human dynamic datasets, all containing large head motions, with ¹⁸F-FDG (N = 13) and ¹¹C-UCB-J (N = 10), and compared its performance with FIR and with HMT using the Vicra, which can be considered as the “gold standard”. Results: The COD method yielded 1.0±3.2% (mean ± standard deviation across all subjects and 12 grey matter regions) SUV difference for ¹⁸F-FDG (3.7±5.4% for ¹¹C-UCB-J) compared to HMT while no motion correction (NMC) and FIR yielded -15.7±12.2% (-20.5±15.8%) and -4.7±6.9% (-6.2±11.0%), respectively. For ¹⁸F-FDG dynamic studies, COD yielded differences of 3.6±10.9% in Ki value as compared to HMT, while NMC and FIR yielded -18.0±39.2% and -2.6±19.8%, respectively. For ¹¹C-UCB-J, COD yielded 3.7±5.2% differences in VT compared to HMT, while NMC and FIR yielded -20.0±12.5% and -5.3±9.4%, respectively. Conclusion: The proposed COD-based data-driven motion correction method outperformed FIR and achieved comparable or even better performance as compared to the Vicra HMT method in both static and dynamic studies.