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 1 predefined scan period, and 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, that is, 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 of all events are averaged over 1-s intervals to generate a COD trace. A point-to-point change in the COD trace in 1 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 motion, with 18 F-FDG (n 5 13) and 11 C-UCB-J ((R)-1-((3-( 11 C-methyl-11 C)pyridin-4-yl)methyl)-4-(3,4,5-trifluorophenyl)pyrrolidin-2-one) (n 5 10) and compared its performance with FIR and with HMT using Vicra (an optical HMT device), which can be considered the gold standard. Results: The COD method yielded a 1.0% ± 3.2% (mean ± SD across all subjects and 12 gray matter regions) SUV difference for 18 F-FDG (3.7% ± 5.4% for 11 C-UCB-J) compared with HMT, whereas 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 18 F-FDG dynamic studies, COD yielded differences of 3.6% ± 10.9% in K i value as compared with HMT, whereas NMC and FIR yielded −18.0% ± 39.2% and −2.6% ± 19.8%, respectively. For 11 C-UCB-J, COD yielded 3.7% ± 5.2% differences in V T compared with HMT, whereas 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 than the Vicra HMT method in both static and dynamic studies.
In PET/CT imaging, CT is used for PET attenuation correction (AC). Mismatch between CT and PET due to patient body motion results in AC artifacts. In addition, artifact caused by metal, beam-hardening and count-starving in CT itself also introduces inaccurate AC for PET. Maximum likelihood reconstruction of activity and attenuation (MLAA) was proposed to solve those issues by simultaneously reconstructing tracer activity (λ-MLAA) and attenuation map (μ-MLAA) based on the PET raw data only. However, μ-MLAA suffers from high noise and λ-MLAA suffers from large bias as compared to the reconstruction using the CT-based attenuation map (μ-CT). Recently, a convolutional neural network (CNN) was applied to predict the CT attenuation map (μ-CNN) from λ-MLAA and μ-MLAA, in which an image-domain loss (IM-loss) function between the μ-CNN and the ground truth μ-CT was used. However, IM-loss does not directly measure the AC errors according to the PET attenuation physics, where the line-integral projection of the attenuation map (μ) along the path of the two annihilation events, instead of the μ itself, is used for AC. Therefore, a network trained with the IM-loss may yield suboptimal performance in the μ generation. Here, we propose a novel line-integral projection loss (LIP-loss) function that incorporates the PET attenuation physics for μ generation. Eighty training and twenty testing datasets of whole-body 18 F-FDG PET and paired ground truth μ-CT were used. Quantitative evaluations showed that the model trained with the additional LIP-loss was able to significantly outperform the model trained solely based on the IM-loss function.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.