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...
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | IET Signal Processing |
| Online Access: | http://dx.doi.org/10.1049/2024/5664618 |
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| _version_ | 1832558582245097472 |
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| author | Zheng Sun Yue Yao Ru Wang |
| author_facet | Zheng Sun Yue Yao Ru Wang |
| author_sort | Zheng Sun |
| collection | DOAJ |
| description | 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 retrospective software gating methods extract gating signals through separate steps based on changes in vessel morphology or image features, which require a high computational cost and are prone to error accumulation. In this paper, we report on an end-to-end unsupervised learning framework for retrospective image-based gating (IBG) of catheter-based intracoronary images, named IBG Network. It establishes a direct mapping from a continuously acquired image sequence to a gated subsequence. The network was trained on clinical data sets in an unsupervised manner, addressing the difficulty of obtaining the gold standard in deep learning-based motion suppression techniques. Experimental results of in vivo intravascular ultrasound and optical coherence tomography sequences show that the proposed method has better performance in terms of motion artifact suppression and processing efficiency compared with the state-of-the-art nonlearning signal-based and IBG methods. |
| format | Article |
| id | doaj-art-4508a0be83c34056b0c2243e6334490f |
| institution | Kabale University |
| issn | 1751-9683 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-4508a0be83c34056b0c2243e6334490f2025-02-03T01:31:59ZengWileyIET Signal Processing1751-96832024-01-01202410.1049/2024/5664618An Unsupervised Deep Learning Framework for Retrospective Gating of Catheter-Based Cardiac ImagingZheng Sun0Yue Yao1Ru Wang2Department of Electronic and Communication EngineeringDepartment of Electronic and Communication EngineeringDepartment of Electronic and Communication EngineeringMotion 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 retrospective software gating methods extract gating signals through separate steps based on changes in vessel morphology or image features, which require a high computational cost and are prone to error accumulation. In this paper, we report on an end-to-end unsupervised learning framework for retrospective image-based gating (IBG) of catheter-based intracoronary images, named IBG Network. It establishes a direct mapping from a continuously acquired image sequence to a gated subsequence. The network was trained on clinical data sets in an unsupervised manner, addressing the difficulty of obtaining the gold standard in deep learning-based motion suppression techniques. Experimental results of in vivo intravascular ultrasound and optical coherence tomography sequences show that the proposed method has better performance in terms of motion artifact suppression and processing efficiency compared with the state-of-the-art nonlearning signal-based and IBG methods.http://dx.doi.org/10.1049/2024/5664618 |
| spellingShingle | Zheng Sun Yue Yao Ru Wang An Unsupervised Deep Learning Framework for Retrospective Gating of Catheter-Based Cardiac Imaging IET Signal Processing |
| title | An Unsupervised Deep Learning Framework for Retrospective Gating of Catheter-Based Cardiac Imaging |
| title_full | An Unsupervised Deep Learning Framework for Retrospective Gating of Catheter-Based Cardiac Imaging |
| title_fullStr | An Unsupervised Deep Learning Framework for Retrospective Gating of Catheter-Based Cardiac Imaging |
| title_full_unstemmed | An Unsupervised Deep Learning Framework for Retrospective Gating of Catheter-Based Cardiac Imaging |
| title_short | An Unsupervised Deep Learning Framework for Retrospective Gating of Catheter-Based Cardiac Imaging |
| title_sort | unsupervised deep learning framework for retrospective gating of catheter based cardiac imaging |
| url | http://dx.doi.org/10.1049/2024/5664618 |
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