Abstract
Image quality in abdominal PET is degraded by respiratory motion. In this paper we compare existing data-driven gating methods for motion correction which are based on manifold learning, with a proposed method in which a convolutional neural network learns estimated motion fields in an end-to-end manner, and then uses those estimated motion fields to motion correct the PET frames. We find that this proposed network approach is unable to outperform manifold learning methods in the literature, in terms of the image quality of the motion corrected volumes. We investigate possible explanations for this negative result and discuss the benefits of these unsupervised approaches which remain the state of the art.
This work was supported by the Engineering and Physical Sciences Research Council under Grant EP/M009319/1 and by the Wellcome EPSRC Centre for Medical Engineering at Kings College London (WT203148/Z/16/Z).
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We would like to thank nVidia for kindly donating the Quadro P6000 GPU used in this research.
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Clough, J.R., Balfour, D.R., Prieto, C., Reader, A.J., Marsden, P.K., King, A.P. (2018). Evaluation of Strategies for PET Motion Correction - Manifold Learning vs. Deep Learning. In: Stoyanov, D., et al. Understanding and Interpreting Machine Learning in Medical Image Computing Applications. MLCN DLF IMIMIC 2018 2018 2018. Lecture Notes in Computer Science(), vol 11038. Springer, Cham. https://doi.org/10.1007/978-3-030-02628-8_7
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