  Coronary Computed Tomography Angiography (CCTA) provides information on the
presence, extent, and severity of obstructive coronary artery disease.
Large-scale clinical studies analyzing CCTA-derived metrics typically require
ground-truth validation in the form of high-fidelity 3D intravascular imaging.
However, manual rigid alignment of intravascular images to corresponding CCTA
images is both time consuming and user-dependent. Moreover, intravascular
modalities suffer from several non-rigid motion-induced distortions arising
from distortions in the imaging catheter path. To address these issues, we here
present a semi-automatic segmentation-based framework for both rigid and
non-rigid matching of intravascular images to CCTA images. We formulate the
problem in terms of finding the optimal \emph{virtual catheter path} that
samples the CCTA data to recapitulate the coronary artery morphology found in
the intravascular image. We validate our co-registration framework on a cohort
of $n=40$ patients using bifurcation landmarks as ground truth for longitudinal
and rotational registration. Our results indicate that our non-rigid
registration significantly outperforms other co-registration approaches for
luminal bifurcation alignment in both longitudinal (mean mismatch: 3.3 frames)
and rotational directions (mean mismatch: 28.6 degrees). By providing a
differentiable framework for automatic multi-modal intravascular data fusion,
our developed co-registration modules significantly reduces the manual effort
required to conduct large-scale multi-modal clinical studies while also
providing a solid foundation for the development of machine learning-based
co-registration approaches.
