An Unsupervised Deep Learning Framework for Retrospective Gating of Catheter-Based Cardiac Imaging
Motion artifacts are a major challenge in the in vivo application of catheter-based cardiac imaging modalities. Gating is a critical tool for suppressing motion artifacts. Electrocardiogram (ECG) gating requires a trigger device or synchronous ECG recordings for retrospective analysis. Existing retr...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | IET Signal Processing |
| Online Access: | http://dx.doi.org/10.1049/2024/5664618 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|