Abstract
Respiratory motion presents significant challenges for PET/ CT acquisitions, potentially leading to inaccurate SUV quantitation. Non Rigid Registration [NRR] of gated PET images is quite challenging due to large motion, intrinsic noise, and the need to preserve definitive features like tumors. In this work, we use non-local spatio-temporal constraints within group-wise NRR to get a stable framework which can work with few number of PET gates, and handle the above challenges of PET data. Additionally, we propose metrics for measuring alignment and artifacts introduced by NRR which is rarely addressed. Our results are quantitatively compared to related works, on 20 clinical PET cases.
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Thiruvenkadam, S., Shriram, K., Manjeshwar, R., Wollenweber, S. (2015). Robust PET Motion Correction Using Non-local Spatio-temporal Priors. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9350. Springer, Cham. https://doi.org/10.1007/978-3-319-24571-3_77
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DOI: https://doi.org/10.1007/978-3-319-24571-3_77
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