Intravascular ultrasound (IVUS) and optical coherence tomography (OCT) are complementary imaging modalities to assess atherosclerosis in vivo. Combining both modalities in a single imaging system has been shown to improve the characterization of vulnerable plaques that are likely to cause acute coronary events. However, fundamental differences in tissue sensitivities and acquisition protocols make the registration of sequences challenging. Anatomical landmarks used to align IVUS and OCT sequences can be masked or lack visual similarity between modalities which renders manual alignment time-consuming and prone to observer variability, limiting its clinical use. Existing methods impose strict frame-level correspondences leading to instability in low information regions, and rely on a two-step registration process that compounds alignment errors. We propose IntraCross, a novel graph matching framework that learns partial assignments between landmarks rather than enforcing rigid frame-by-frame matching, enabling flexible correspondences while rejecting unmatchable landmarks. This is the first method to perform both temporal and rotational registration simultaneously, aligning with clinical workflows. We extend existing partial matching techniques from 2D to 3D sequences and incorporate a temporal prior to regularize the matching process. Testing in 77 vessels from 22 patients showed a high agreement with expert analysts (Williams Index=1.1; p=0.62, 0.89, 0.07) and our approach outperforms existing methods reported in the literature for circumferential registration (p=0.01, 0.04). • A novel graph matching method for Intravascular sequence registration that learns partial correspondence between anatomical landmarks in different modalities, addressing error caused by forced frame-by-frame matching of prior work. • We extend existing graph matching techniques from single modality to multi modality and from 2D image to 3D video sequence. • IntraCross represents the consensus better than individual experts and offers statistically significant improvements against prior work.
Bransby et al. (Tue,) studied this question.