IntraCross: Cross-modality graph matching for intravascular sequence registration

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).

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Publication Details

Journal
Computers in Biology and Medicine
Published
2026-02-24
DOI
https://doi.org/10.1016/j.compbiomed.2026.111566
Primary Topic
Graph Theory and Algorithms
Type
article
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IntraCross: Cross-modality graph matching for intravascular sequence registration

Greg Slabaugh, Christos V. Bourantas, Xingwei He, Qianni Zhang et al.
Computers in Biology and Medicine
Graph Theory and Algorithms
article

IntraCross: Cross-modality graph matching for intravascular sequence registration

Greg Slabaugh, Christos V. Bourantas, Xingwei He, Qianni Zhang, Kit Mills Bransby, Nathan Yap, Jonas Häner, Ahmet Emir Ulutas, Yasushi Ueki, Lorenz Räber, Ryota Kakizaki, Konstantinos C. Koskinas, Jouke Dijkstra
article en

Abstract

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).

Computers in Biology and MedicineVol. 205
Queen Mary University of London (GB), Leiden University Medical Center (NL), University Hospital of Bern (CH), William Harvey Research Institute (GB)
Zero hunger
Openalex Percentile: Top 47%
Graph Theory and Algorithms
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