Published on Thu Nov 08 2018

Repetitive Motion Estimation Network: Recover cardiac and respiratory signal from thoracic imaging

Xiaoxiao Li, Vivek Singh, Yifan Wu, Klaus Kirchberg, James Duncan, Ankur Kapoor

Repetitive Motion Estimation Network (RMEN) learns spatio-temporal repetition patterns. RMEN significantly decreased the QRS peaks detection offsets by 59.3%. Results showed that RMEN could handle the irregular cardiac and respiratory motion cases.

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Abstract

Tracking organ motion is important in image-guided interventions, but motion annotations are not always easily available. Thus, we propose Repetitive Motion Estimation Network (RMEN) to recover cardiac and respiratory signals. It learns the spatio-temporal repetition patterns, embedding high dimensional motion manifolds to 1D vectors with partial motion phase boundary annotations. Compared with the best alternative models, our proposed RMEN significantly decreased the QRS peaks detection offsets by 59.3%. Results showed that RMEN could handle the irregular cardiac and respiratory motion cases. Repetitive motion patterns learned by RMEN were visualized and indicated in the feature maps.

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