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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.05883 |
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| _version_ | 1866915377282285568 |
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| author | He, Xingwei Bransby, Kit Mills Ulutas, Ahmet Emir Kumaran, Thamil Yap, Nathan Angelo Lecaros Zeren, Gonul Zeng, Hesong Zhang, Yaojun Baumbach, Andreas Moon, James Mathur, Anthony Dijkstra, Jouke Zhang, Qianni Raber, Lorenz Bourantas, Christos V |
| author_facet | He, Xingwei Bransby, Kit Mills Ulutas, Ahmet Emir Kumaran, Thamil Yap, Nathan Angelo Lecaros Zeren, Gonul Zeng, Hesong Zhang, Yaojun Baumbach, Andreas Moon, James Mathur, Anthony Dijkstra, Jouke Zhang, Qianni Raber, Lorenz Bourantas, Christos V |
| contents | Aims: To develop a deep-learning (DL) framework that will allow fully automated longitudinal and circumferential co-registration of intravascular ultrasound (IVUS) and optical coherence tomography (OCT) images. Methods and results: Data from 230 patients (714 vessels) with acute coronary syndrome that underwent near-infrared spectroscopy (NIRS)-IVUS and OCT imaging in their non-culprit vessels were included in the present analysis. The lumen borders annotated by expert analysts in 61,655 NIRS-IVUS and 62,334 OCT frames, and the side branches and calcific tissue identified in 10,000 NIRS-IVUS frames and 10,000 OCT frames, were used to train DL solutions for the automated extraction of these features. The trained DL solutions were used to process NIRS-IVUS and OCT images and their output was used by a dynamic time warping algorithm to co-register longitudinally the NIRS-IVUS and OCT images, while the circumferential registration of the IVUS and OCT was optimized through dynamic programming. On a test set of 77 vessels from 22 patients, the DL method showed high concordance with the expert analysts for the longitudinal and circumferential co-registration of the two imaging sets (concordance correlation coefficient >0.99 for the longitudinal and >0.90 for the circumferential co-registration). The Williams Index was 0.96 for longitudinal and 0.97 for circumferential co-registration, indicating a comparable performance to the analysts. The time needed for the DL pipeline to process imaging data from a vessel was <90s. Conclusion: The fully automated, DL-based framework introduced in this study for the co-registration of IVUS and OCT is fast and provides estimations that compare favorably to the expert analysts. These features renders it useful in research in the analysis of large-scale data collected in studies that incorporate multimodality imaging to characterize plaque composition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05883 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A novel framework for fully-automated co-registration of intravascular ultrasound and optical coherence tomography imaging data He, Xingwei Bransby, Kit Mills Ulutas, Ahmet Emir Kumaran, Thamil Yap, Nathan Angelo Lecaros Zeren, Gonul Zeng, Hesong Zhang, Yaojun Baumbach, Andreas Moon, James Mathur, Anthony Dijkstra, Jouke Zhang, Qianni Raber, Lorenz Bourantas, Christos V Image and Video Processing Computer Vision and Pattern Recognition Aims: To develop a deep-learning (DL) framework that will allow fully automated longitudinal and circumferential co-registration of intravascular ultrasound (IVUS) and optical coherence tomography (OCT) images. Methods and results: Data from 230 patients (714 vessels) with acute coronary syndrome that underwent near-infrared spectroscopy (NIRS)-IVUS and OCT imaging in their non-culprit vessels were included in the present analysis. The lumen borders annotated by expert analysts in 61,655 NIRS-IVUS and 62,334 OCT frames, and the side branches and calcific tissue identified in 10,000 NIRS-IVUS frames and 10,000 OCT frames, were used to train DL solutions for the automated extraction of these features. The trained DL solutions were used to process NIRS-IVUS and OCT images and their output was used by a dynamic time warping algorithm to co-register longitudinally the NIRS-IVUS and OCT images, while the circumferential registration of the IVUS and OCT was optimized through dynamic programming. On a test set of 77 vessels from 22 patients, the DL method showed high concordance with the expert analysts for the longitudinal and circumferential co-registration of the two imaging sets (concordance correlation coefficient >0.99 for the longitudinal and >0.90 for the circumferential co-registration). The Williams Index was 0.96 for longitudinal and 0.97 for circumferential co-registration, indicating a comparable performance to the analysts. The time needed for the DL pipeline to process imaging data from a vessel was <90s. Conclusion: The fully automated, DL-based framework introduced in this study for the co-registration of IVUS and OCT is fast and provides estimations that compare favorably to the expert analysts. These features renders it useful in research in the analysis of large-scale data collected in studies that incorporate multimodality imaging to characterize plaque composition. |
| title | A novel framework for fully-automated co-registration of intravascular ultrasound and optical coherence tomography imaging data |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.05883 |