基于形态学的冠状动脉CT与血管内图像非刚性配准：通过虚拟导管路径优化
Morphology-based non-rigid registration of coronary computed tomography and intravascular images through virtual catheter path optimization
AI总结：
提出基于形态学的可微分框架，通过优化虚拟导管路径实现CCTA与血管内图像的非刚性配准，在多中心40例患者中显著优于其他方法，减少人工并支持机器学习配准。
摘要：
冠状动脉CT血管造影（CCTA）可提供阻塞性冠状动脉疾病的3D信息，但无法完全可视化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分辨动脉粥样硬化，但在捕捉每个切片之间的3D关系方面存在局限。将CCTA与血管内图像进行共同配准可实现多种临床研究应用，但这一过程耗时且依赖操作者。这是因为血管内图像受到因成像导管路径不规则而产生的非刚性畸变的影响。为解决这些问题，我们提出了一种基于形态学的框架，用于血管内图像与CCTA图像的刚性及非刚性匹配。为此，我们寻找最优虚拟导管路径，该路径在CCTA图像空间中采样冠状动脉，以重现血管内图像中观察到的冠状动脉形态。我们使用分叉地标作为纵向和旋转配准的金标准，在40名患者的多中心队列上验证了我们的框架。我们的配准方法在分叉对齐方面显著优于其他方法。通过提供一种用于多模态血管共同配准的可微分框架，我们的框架减少了开展大规模多模态临床研究所需的人工工作量，并促进了基于机器学习的共同配准方法的发展。
英文摘要：
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.
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