Percutaneous coronary intervention is widely applied for the treatment of coronary artery disease under the guidance of X-ray coronary angiography (XCA) image. However, the projective nature of XCA causes the loss of 3D structural information, which hinders the intervention. This issue can be addressed by the deformable 3D/2D coronary artery registration technique, which fuses the pre-operative computed tomography angiography volume with the intra-operative XCA image. In this study, we propose a deep learning-based neural network for this task. The registration is conducted in a segment-by-segment manner. For each vessel segment pair, the centerlines that preserve topological information are decomposed into an origin tensor and a spherical coordinate shape tensor as network input through independent branches. Features of different modalities are fused and processed for predicting angular deflections, which is a special type of deformation field implying motion and length preservation constraints for vessel segments. The proposed method achieves an average error of 1.13 mm on the clinical dataset, which shows the potential to be applied in clinical practice.

中文翻译：

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CAR-Net：基于深度学习的 3D/2D 冠状动脉配准变形模型


经皮冠状动脉介入治疗在X射线冠状动脉造影（XCA）图像引导下广泛应用于冠状动脉疾病的治疗。然而，XCA的投影性质导致3D结构信息的丢失，这阻碍了干预。这个问题可以通过可变形 3D/2D 冠状动脉配准技术来解决，该技术将术前计算机断层扫描血管造影体积与术中 XCA 图像融合在一起。在本研究中，我们针对此任务提出了一种基于深度学习的神经网络。登记按照分段进行。对于每个血管段对，保存拓扑信息的中心线通过独立分支被分解为原点张量和球坐标形状张量作为网络输入。融合和处理不同模态的特征以预测角偏转，这是一种特殊类型的变形场，意味着血管段的运动和长度保持约束。该方法在临床数据集上的平均误差为1.13 mm，显示出在临床实践中应用的潜力。