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.

翻译：冠状动脉计算机断层扫描血管造影（CCTA）可提供阻塞性冠状动脉疾病的3D信息，但无法完整显示血管壁内的高分辨率特征。相比之下，血管内成像能够在横截面切片中空间解析动脉粥样硬化斑块，但在捕捉各切片间的3D关系方面存在局限。将CCTA与血管内影像进行联合配准可实现多种临床研究应用，但该过程耗时且依赖操作者经验。这主要源于血管内影像存在由成像导管路径不规则引起的非刚性形变。为解决这些问题，我们提出一种基于形态学的框架，用于实现血管内影像与CCTA影像的刚性及非刚性匹配。具体而言，我们通过寻找最优虚拟导管路径，在CCTA图像空间中采样冠状动脉，以复现血管内影像中观察到的冠状动脉形态学特征。我们在包含40例患者的多中心队列中，以血管分叉标志作为纵向与旋转配准的金标准验证了本框架。实验表明，我们的配准方法在分叉对齐任务上显著优于其他方法。通过提供可微分多模态血管联合配准框架，本研究大幅减少了开展大规模多模态临床研究所需的人工工作量，并为开发基于机器学习的联合配准方法奠定了基础。