13 March 2013 Recursive Bayesian estimation of respiratory motion using a modified autoregressive transition model
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Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 866935 (2013) https://doi.org/10.1117/12.2006878
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
Compensation for respiratory motion has been identified as a crucial factor in achieving high resolution Nuclear Medicine (NM) imaging. Many motion correction approaches have been studied and they are seen to have advantages over simpler approaches such as respiratory gating. However, all motion correction approaches rely on an assumption or estimation of respiratory motion. This paper builds upon previous work in recursive Bayesian estimation of respiratory motion assuming a stereo camera observation of the motion of the external torso surface. This paper compares the performance of a modified autoregressive transition model against the previously presented linear transition model used when estimating motion within a 4D dataset generated from the XCAT phantom.
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Ashrani Aizzuddin Abd. Rahni, Ashrani Aizzuddin Abd. Rahni, Emma Lewis, Emma Lewis, Kevin Wells, Kevin Wells, } "Recursive Bayesian estimation of respiratory motion using a modified autoregressive transition model", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866935 (13 March 2013); doi: 10.1117/12.2006878; https://doi.org/10.1117/12.2006878
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