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Phys Med Biol. 2019 Mar 1. doi: 10.1088/1361-6560/ab0bc9. [Epub ahead of print]

Event-by-event non-rigid data-driven PET respiratory motion correction methods: comparison of principal component analysis and centroid of distribution.

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Biomedical Engineering, Yale University, 801 Howard Avenue, P.O. Box 208048, New Haven, Connecticut, 06520, UNITED STATES.
Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, UNITED STATES.
Institute of Nuclear Medicine, University College London, London, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND.
Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, Connecticut, UNITED STATES.


Respiratory motion is a major cause of degradation of PET image quality. Respiratory gating and motion correction can be performed to reduce the effects of respiratory motion; these methods require motion information, typically obtained from external tracking systems. Various groups have studied data-driven (DD) motion estimation methods. Recently, a data-driven respiratory motion estimation method was established by calculating the centroid of distribution (COD) of listmode events, which was then used with event-by-event respiratory motion correction (EBE-MC), and showed results comparable to those with an external motion tracking device. The EBE-MC method only corrected for rigid motion, so that non-rigid components still contributed to motion-induced blurring. A nonrigid respiratory motion correction (NRMC) was later developed to overcome this problem, but was only evaluated using signal from an external monitor. Thus, it is desirable to further develop data-driven to achieve the best respiratory motion correction results. 
 We evaluated 2 data-driven respiratory motion detection methods, COD and Principal Component Analysis (PCA), by comparing the extracted motion trace to that acquired by the Anzai system in dynamic studies with two tracers. PCA was chosen as a preliminary study indicated that it produced stable results than other DD methods. We then developed and performed DD-EBE-NRMC using either COD- or PCA-derived respiratory motion information. Data-driven correction results were compared with Anzai-based results. For all tested studies, both COD and PCA showed good-to-excellent match with Anzai signals, with PCA showing a higher correlation with Anzai signals. The DD-EBE-NRMC results showed that both COD and PCA provide comparable image quality improvement as the Anzai-based correction. Although COD showed a lower correlation with Anzai than PCA, COD-based NRMC results are comparable to those of PCA, both of which showed great reduction in motion-induced blurring.


Data-driven; Nonrigid Event-by-event Motion Correction; Respiratory Motion


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