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

Aims: To develop a deep-learning (DL) framework that enables 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 myocardial infarction that underwent near-infrared spectroscopy IVUS and OCT imaging in their non-infarct related vessels were analysed. Experts annotated the lumen borders (61 655 IVUS and 62 334 OCT frames), the side branches and the calcific tissue (10 000 IVUS and 10 000 OCT frames each). This information was used to train DL models that extracted these features that were then used by a dynamic time warping algorithm to co-registered longitudinally the IVUS and OCT images. The circumferential registration of IVUS and OCT was performed through a rotation cost matrix and dynamic programming. On a test set of 22 patients (77 vessels), the DL method showed high concordance with the expert analysts for the longitudinal and circumferential co-registration of the two datasets (concordance correlation coefficient >0.99 and >0.90, respectively). The Williams Index was 0.96 for longitudinal and 0.97 for circumferential alignment, indicating a comparable performance of the proposed framework to the analysts. The time needed for the DL pipeline to process imaging data from a vessel was <90 s. Conclusion: A fully automated, DL-based framework for IVUS-OCT co-registration demonstrated both speed and accuracy, with performance comparable to that of expert analysts. These features enable its application in research using large-scale data incorporating multimodality imaging.