Morphology-based non-rigid registration of coronary computed tomography and intravascular images through virtual catheter path optimization

TL;DR

This paper introduces a morphology-based framework for accurately aligning intravascular images with coronary CT scans, addressing non-rigid distortions and improving clinical research capabilities.

Contribution

It presents a novel virtual catheter path optimization method for rigid and non-rigid registration of multimodal coronary images, validated on a multi-center patient cohort.

Findings

Outperforms existing methods in bifurcation alignment accuracy

Reduces manual effort in multi-modal clinical studies

Enables machine learning-based co-registration approaches

Abstract

Coronary computed tomography angiography (CCTA) provides 3D information on obstructive coronary artery disease, but cannot fully visualize high-resolution features within the vessel wall. Intravascular imaging, in contrast, can spatially resolve atherosclerotic in cross sectional slices, but is limited in capturing 3D relationships between each slice. Co-registering CCTA and intravascular images enables a variety of clinical research applications but is time consuming and user-dependent. This is due to intravascular images suffering from non-rigid distortions arising from irregularities in the imaging catheter path. To address these issues, we present a morphology-based framework for the rigid and non-rigid matching of intravascular images to CCTA images. To do this, we find the optimal virtual catheter path that samples the coronary artery in CCTA image space to recapitulate the coronary artery morphology observed in the intravascular image. We validate our framework on a multi-center cohort of 40 patients using bifurcation landmarks as ground truth for longitudinal and rotational registration. Our registration approach significantly outperforms other approaches for bifurcation alignment. By providing a differentiable framework for multi-modal vascular co-registration, our framework reduces the manual effort required to conduct large-scale multi-modal clinical studies and enables the development of machine learning-based co-registration approaches.