Repetitive Motion Estimation Network: Recover cardiac and respiratory signal from thoracic imaging
Year: 2018
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Li, Xiaoxiao, Singh, Vivek, Wu, Yifan, Kirchberg, Klaus, Duncan, James, Kapoor, Ankur
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2899822797