A novel framework for fully-automated co-registration of intravascular ultrasound and optical coherence tomography imaging data

2025-07-08

AI Summary

Manual, time-consuming, and inefficient registration of intravascular ultrasound (IVUS) and optical coherence tomography (OCT) in coronary artery multimodal imaging hinders clinical translation. Method: We propose the first end-to-end deep learning framework for fully automatic, high-precision longitudinal and circumferential co-registration, integrating lumen/branch/calcification feature extraction, dynamic time warping (DTW), and dynamic programming—eliminating manual parameter tuning and sequential processing while unifying and decoupling registration constraints. Results: Evaluated on 77 clinical coronary vessels, our method achieves correlation coefficients of 0.992 (longitudinal) and 0.908 (circumferential), with Williams indices of 0.961 and 0.973, respectively; average processing time per vessel is <90 seconds. Performance matches expert-level accuracy. This work establishes a generalizable, robust, and clinically feasible registration paradigm for multimodal plaque quantification.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

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.

Problem

• Automated co-registration of IVUS and OCT images
• Deep-learning for longitudinal and circumferential alignment
• Fast processing for large-scale multimodal imaging data

Innovation

• Deep-learning for IVUS-OCT co-registration
• Dynamic time warping for longitudinal alignment
• Dynamic programming optimizes circumferential registration