Evaluation of Strategies for PET Motion Correction-Manifold Learning vs. Deep Learning

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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.
Original languageEnglish
Pages (from-to)61-69
JournalLecture Notes in Computer Science
Early online date24 Oct 2018
Publication statusE-pub ahead of print - 24 Oct 2018


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