4D-CAT: Synthesis of 4D Coronary Artery Trees from Systole and Diastole (2409.01725v2)
Abstract: The three-dimensional vascular model reconstructed from CT images is widely used in medical diagnosis. At different phases, the beating of the heart can cause deformation of vessels, resulting in different vascular imaging states and false positive diagnostic results. The 4D model can simulate a complete cardiac cycle. Due to the dose limitation of contrast agent injection in patients, it is valuable to synthesize a 4D coronary artery trees through finite phases imaging. In this paper, we propose a method for generating a 4D coronary artery trees, which maps the systole to the diastole through deformation field prediction, interpolates on the timeline, and the motion trajectory of points are obtained. Specifically, the centerline is used to represent vessels and to infer deformation fields using cube-based sorting and neural networks. Adjacent vessel points are aggregated and interpolated based on the deformation field of the centerline point to obtain displacement vectors of different phases. Finally, the proposed method is validated through experiments to achieve the registration of non-rigid vascular points and the generation of 4D coronary trees.
- A. Garavand, A. Behmanesh, N. Aslani, H. Sadeghsalehi, and M. Ghaderzadeh, “Towards diagnostic aided systems in coronary artery disease detection: a comprehensive multiview survey of the state of the art,” International Journal of Intelligent Systems, vol. 2023, pp. 1–19, 2023.
- C. Xu, Y. Yi, M. Xu, J. Yan, Y.-B. Guo, J. Wang, Y. Wang, Y.-M. Li, Z.-Y. Jin, and Y.-N. Wang, “Coronary artery stent evaluation by cta: impact of deep learning reconstruction and subtraction technique,” American Journal of Roentgenology, vol. 220, no. 1, pp. 63–72, 2023.
- F. Fu, J. Wei, M. Zhang, F. Yu, Y. Xiao, D. Rong, Y. Shan, Y. Li, C. Zhao, F. Liao et al., “Rapid vessel segmentation and reconstruction of head and neck angiograms using 3d convolutional neural network,” Nature communications, vol. 11, no. 1, p. 4829, 2020.
- Y. Fu, Y. Lei, T. Wang, K. Higgins, J. D. Bradley, W. J. Curran, T. Liu, and X. Yang, “Lungregnet: an unsupervised deformable image registration method for 4d-ct lung,” Medical physics, vol. 47, no. 4, pp. 1763–1774, 2020.
- B. Kim and J. C. Ye, “Diffusion deformable model for 4d temporal medical image generation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2022, pp. 539–548.
- C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 652–660.
- M. Cuturi and M. Blondel, “Soft-dtw: a differentiable loss function for time-series,” in International conference on machine learning. PMLR, 2017, pp. 894–903.
- J. Yang, H. Li, D. Campbell, and Y. Jia, “Go-icp: A globally optimal solution to 3d icp point-set registration,” IEEE transactions on pattern analysis and machine intelligence, vol. 38, no. 11, pp. 2241–2254, 2015.
- Y. Wang and J. M. Solomon, “Deep closest point: Learning representations for point cloud registration,” in Proceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 3523–3532.
- X. Huang, G. Mei, and J. Zhang, “Feature-metric registration: A fast semi-supervised approach for robust point cloud registration without correspondences,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 11 366–11 374.
- S. Çimen, A. Gooya, M. Grass, and A. F. Frangi, “Reconstruction of coronary arteries from x-ray angiography: A review,” Medical image analysis, vol. 32, pp. 46–68, 2016.
- Y. Wang, C. Yan, Y. Feng, S. Du, Q. Dai, and Y. Gao, “Storm: Structure-based overlap matching for partial point cloud registration,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 1, pp. 1135–1149, 2022.
- N. Chen, L. Liu, Z. Cui, R. Chen, D. Ceylan, C. Tu, and W. Wang, “Unsupervised learning of intrinsic structural representation points,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 9121–9130.
- X. Zhang, F. Liu, Y. Gu, X. Xiong, C. Jiang, J. Feng, and D. Shen, “Spr-net: Structural points based registration for coronary arteries across systolic and diastolic phases,” in International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2023, pp. 791–801.
- W. Wu, J. Zhang, W. Peng, H. Xie, S. Zhang, and L. Gu, “Car-net: a deep learning-based deformation model for 3d/2d coronary artery registration,” IEEE Transactions on Medical Imaging, vol. 41, no. 10, pp. 2715–2727, 2022.