Abstract
Virtual interventions enable the physics-based simulation of device deployment within coronary arteries. This framework allows for counterfactual reasoning by deploying the same device in different arterial anatomies. However, current methods to create such counterfactual arteries face a trade-off between controllability and realism. In this study, we investigate how Latent Diffusion Models (LDMs) can custom synthesize coronary anatomy for virtual intervention studies based on mid-level anatomic constraints such as topological validity, local morphological shape, and global skeletal structure. We also extend diffusion model guidance strategies to the context of morpho-skeletal conditioning and propose a novel guidance method for continuous attributes that adaptively updates the negative guiding condition throughout sampling. Our framework enables the generation and editing of coronary anatomy in a controllable manner, allowing device designers to derive mechanistic insights regarding anatomic variation and simulated device deployment. Our code is available at https://github.com/kkadry/Morphoskel-Diffusion.
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References
Beetz, M., Banerjee, A., Grau, V.: Generating subpopulation-specific biventricular anatomy models using conditional point cloud variational autoencoders. In: Puyol Antón, E., et al. (eds.) STACOM 2021. LNCS, vol. 13131, pp. 75–83. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-93722-5_9
Byrne, N., Clough, J.R., Valverde, I., Montana, G., King, A.P.: A persistent homology-based topological loss for CNN-based multiclass segmentation of CMR. IEEE Trans. Med. Imaging 42(1), 3–14 (2022)
Chung, H., Kim, J., Mccann, M.T., Klasky, M.L., Ye, J.C.: Diffusion posterior sampling for general noisy inverse problems. arXiv preprint arXiv:2209.14687 (2022)
Clough, J.R., Byrne, N., Oksuz, I., Zimmer, V.A., Schnabel, J.A., King, A.P.: A topological loss function for deep-learning based image segmentation using persistent homology. IEEE Trans. Pattern Anal. Mach. Intell. 44(12), 8766–8778 (2020)
Dassault Systèmes: Abaqus Finite Element Analysis Software. Vélizy-Villacoublay, France, version 2023 edn (2023). https://www.3ds.com
Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. In: Advances in Neural Information Processing Systems, vol. 34, pp. 8780–8794 (2021)
Dong, P., Bezerra, H.G., Wilson, D.L., Gu, L.: Impact of calcium quantifications on stent expansions. J. Biomech. Eng. 141(2), 021010 (2019)
Doradla, P., et al.: Biomechanical stress profiling of coronary atherosclerosis: identifying a multifactorial metric to evaluate plaque rupture risk. Cardiovasc. Imaging 13(3), 804–816 (2020)
Dou, H., Virtanen, S., Ravikumar, N., Frangi, A.F.: A generative shape compositional framework: towards representative populations of virtual heart chimaeras. arXiv preprint arXiv:2210.01607 (2022)
Gandikota, R., Materzynska, J., Fiotto-Kaufman, J., Bau, D.: Erasing concepts from diffusion models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2426–2436 (2023)
Gupta, S., et al.: Learning topological interactions for multi-class medical image segmentation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13689, pp. 701–718. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19818-2_40
He, Y., et al.: Manifold preserving guided diffusion. arXiv preprint arXiv:2311.16424 (2023)
Ho, J., Salimans, T.: Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022)
Holm, N.R., et al.: OCT or angiography guidance for PCI in complex bifurcation lesions. N. Engl. J. Med. 389(16), 1477–1487 (2023)
Kadry, K., Gupta, S., Nezami, F.R., Edelman, E.R.: Probing the limits and capabilities of diffusion models for the anatomic editing of digital twins. arXiv preprint arXiv:2401.00247 (2023)
Kadry, K., Olender, M.L., Marlevi, D., Edelman, E.R., Nezami, F.R.: A platform for high-fidelity patient-specific structural modelling of atherosclerotic arteries: from intravascular imaging to three-dimensional stress distributions. J. R. Soc. Interface 18(182), 20210436 (2021)
Karanasiou, G.S., et al.: Design and implementation of in silico clinical trial for bioresorbable vascular scaffolds. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 2675–2678. IEEE (2020)
Karanasiou, G.S., et al.: An in silico trials platform for the evaluation of stent design effect in post-implantation outcomes. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4970–4973. IEEE (2022)
Karras, T., Aittala, M., Aila, T., Laine, S.: Elucidating the design space of diffusion-based generative models. arXiv preprint arXiv:2206.00364 (2022)
Madani, A., Bakhaty, A., Kim, J., Mubarak, Y., Mofrad, M.R.: Bridging finite element and machine learning modeling: stress prediction of arterial walls in atherosclerosis. J. Biomech. Eng. 141(8), 084502 (2019)
Marlevi, D., Edelman, E.R.: Vascular lesion-specific drug delivery systems: JACC state-of-the-art review. J. Am. Coll. Cardiol. 77(19), 2413–2431 (2021)
Mori, H., Torii, S., Kutyna, M., Sakamoto, A., Finn, A.V., Virmani, R.: Coronary artery calcification and its progression: what does it really mean? JACC: Cardiovas. Imaging 11(1), 127–142 (2018)
Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 311–320. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_28
Nichol, A., et al.: Glide: towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:2112.10741 (2021)
Otsuka, F., Yasuda, S., Noguchi, T., Ishibashi-Ueda, H.: Pathology of coronary atherosclerosis and thrombosis. Cardiovasc. Diagn. Ther. 6(4), 396 (2016)
Pham, J., Kong, F., James, D.L., Marsden, A.L.: Virtual shape-editing of patient-specific vascular models using regularized kelvinlets. IEEE Trans. Biomed. Eng. (2024)
Pham, J., Wyetzner, S., Pfaller, M.R., Parker, D.W., James, D.L., Marsden, A.L.: svMorph: interactive geometry-editing tools for virtual patient-specific vascular anatomies. J. Biomech. Eng. 145(3), 031001 (2023)
Poletti, G., et al.: Towards a digital twin of coronary stenting: a suitable and validated image-based approach for mimicking patient-specific coronary arteries. Electronics 11(3), 502 (2022)
Qiao, M., et al.: Generative modelling of the ageing heart with cross-sectional imaging and clinical data. In: Camara, O., et al. (eds.) STACOM 2022. LNCS, vol. 13593, pp. 3–12. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-23443-9_1
Qiao, M., et al.: Cheart: a conditional spatio-temporal generative model for cardiac anatomy. arXiv preprint arXiv:2301.13098 (2023)
Ralapanawa, U., Sivakanesan, R.: Epidemiology and the magnitude of coronary artery disease and acute coronary syndrome: a narrative review. J. Epidemiol. Glob. Health 11(2), 169 (2021)
Sato, M., Bitter, I., Bender, M.A., Kaufman, A.E., Nakajima, M.: Teasar: tree-structure extraction algorithm for accurate and robust skeletons. In: Proceedings the Eighth Pacific Conference on Computer Graphics and Applications, pp. 281–449. IEEE (2000)
Sawaya, F.J., et al.: Contemporary approach to coronary bifurcation lesion treatment. JACC: Cardiovasc. Interv. 9(18), 1861–1878 (2016)
Shit, S., et al.: clDice-a novel topology-preserving loss function for tubular structure segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16560–16569 (2021)
Silversmith, W., Bae, J.A., Li, P.H., Wilson, A.: Kimimaro: skeletonize densely labeled 3D image segmentations (2021). https://doi.org/10.5281/zenodo.5539913
Sonck, J., et al.: Clinical validation of a virtual planner for coronary interventions based on coronary CT angiography. Cardiovasc. Imaging 15(7), 1242–1255 (2022)
Song, J., et al.: Loss-guided diffusion models for plug-and-play controllable generation (2023)
Stone, P.H., et al.: Prediction of progression of coronary artery disease and clinical outcomes using vascular profiling of endothelial shear stress and arterial plaque characteristics: the prediction study. Circulation 126(2), 172–181 (2012)
Straughan, R., Kadry, K., Parikh, S.A., Edelman, E.R., Nezami, F.R.: Fully automated construction of three-dimensional finite element simulations from optical coherence tomography. Comput. Biol. Med. 165, 107341 (2023)
Verhülsdonk, J., et al.: Shape of my heart: cardiac models through learned signed distance functions. arXiv preprint arXiv:2308.16568 (2023)
Virmani, R., Burke, A.P., Farb, A., Kolodgie, F.D.: Pathology of the vulnerable plaque. J. Am. Coll. Cardiol. 47(8S), C13–C18 (2006)
Wang, W., et al.: Semantic image synthesis via diffusion models. arXiv preprint arXiv:2207.00050 (2022)
Wiesner, D., et al.: Generative modeling of living cells with so (3)-equivariant implicit neural representations. arXiv preprint arXiv:2304.08960 (2023)
Zhao, S., et al.: Patient-specific computational simulation of coronary artery bifurcation stenting. Sci. Rep. 11(1), 16486 (2021)
Acknowledgments
We thank Vivek Gopalakrishnan, Neel Dey, Neerav Karani, Anurag Vaidya, and Ajay Manicka for discussion and comments. Funding for this project was provided by the National Institutes of Health (GM 49039) and Shockwave Medical.
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Kadry, K. et al. (2025). A Diffusion Model for Simulation Ready Coronary Anatomy with Morpho-Skeletal Control. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15136. Springer, Cham. https://doi.org/10.1007/978-3-031-73229-4_23
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