A novel framework for fully-automated co-registration of intravascular ultrasound and optical coherence tomography imaging data
Journal:
arXiv
Published Date:
Jul 8, 2025
Abstract
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.
