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Non-rigid registration based on hierarchical deformation of coronary arteries in CCTA images

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Abstract

In this paper, we propose an accurate and rapid non-rigid registration method between blood vessels in temporal 3D cardiac computed tomography angiography images of the same patient. This method provides auxiliary information that can be utilized in the diagnosis and treatment of coronary artery diseases. The proposed method consists of the following four steps. First, global registration is conducted through rigid registration between the 3D vessel centerlines obtained from temporal 3D cardiac CT angiography images. Second, point matching between the 3D vessel centerlines in the rigid registration results is performed, and the corresponding points are defined. Third, the outliers in the matched corresponding points are removed by using various information such as thickness and gradient of the vessels. Finally, non-rigid registration is conducted for hierarchical local transformation using an energy function. The experiment results show that the average registration error of the proposed method is 0.987 mm, and the average execution time is 2.137 s, indicating that the registration is accurate and rapid. The proposed method that enables rapid and accurate registration by using the information on blood vessel characteristics in temporal CTA images of the same patient.

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References

  1. Murray CJ, Lopez AD. Alternative projections of mortality and disability by cause 1990–2020: global burden of disease study. Lancet. 1997;349(9064):1498–504.

    Article  Google Scholar 

  2. Min JK, Shaw LJ, Berman DS. The present state of coronary computed tomography angiography: a process in evolution. J Am Coll Cardiol. 2010;55(10):957–65.

    Article  Google Scholar 

  3. Zhang W, Yuqian Z. Hierarchical registration of brain images based on B-splines and laplacian commutators. Optik. 2021;241:167022.

    Article  Google Scholar 

  4. Fontana L, Mastropietro A, Scalco E, Peruzzo D, Beretta E, Strazzer S, Arrigoni F, Rizzo G. Multi-steps registration protocol for multimodal MR images of hip skeletal muscles in a longitudinal study. Appl Sci. 2020;10(21):7823.

    Article  Google Scholar 

  5. Kuiper RJ, van Stralen M, Sakkers RJ, Bergmans RH, Zijlstra F, Viergever MA, Weinans H, Seevinck PR. CT to MR registration of complex deformations in the knee joint through dual quaternion interpolation of rigid transforms. Phys Med Biol. 2021;66(17):175024.

    Article  Google Scholar 

  6. Xu P, Chen C, Wang X, Li W, Sun J. ROI-based intraoperative MR-CT registration for image-guided multimode tumor ablation therapy in hepatic malignant tumors. IEEE Access. 2020;8:13613–9.

    Article  Google Scholar 

  7. Tang S, Wang Y. MR-guided liver cancer surgery by nonrigid registration. In: Proceedings of the international conference  on medical image analysis and clinical application 2010 2010, pp. 113–7.

  8. Ou Y, Sotiras A, Paragios N. Deformable registration via attribute matching and mutual-saliency weighting. Med Image Anal. 2011;15(4):622–39.

    Article  Google Scholar 

  9. Luo J, Toews M, Machado I, Frisken S, Zhang M, Preiswerk F, Sedghi A, Ding H, Pieper S, Golland P, Sugiyama M, Golby A, Wells WM. A feature-driven active framework for ultrasound-based brain shift compensation. In: Proceedings of the international conference on medical image computing and computer-assisted intervention 2018, pp. 30–8.

  10. Machado I, Toews M, Luo J, Unadkat P, Essayed W, George E, Wells WM. Non-rigid registration of 3D ultrasound for neurosurgery using automatic feature detection and matching. Int J Comput Assist Radiol Surg. 2018;13(10):1525–38.

    Article  Google Scholar 

  11. Zhou H, Rivaz H. Registration of pre-and postresection ultrasound volumes with noncorresponding regions in neurosurgery. IEEE J Biomed Health Inform. 2016;20(5):1240–9.

    Article  Google Scholar 

  12. Gerard IJ, Kersten-Oertel M, Petrecca K, Sirhan D, Hall JA, Collins DL. 2017 brain shift in neuronavigation of brain tumors: a review. Med Image Anal. 2017;35:403–20.

    Article  Google Scholar 

  13. Park TY, Lee J, Shin J, Kim KW, Kang HC. Non-rigid liver registration in liver computed tomography images using elastic method with global and local deformation. J Med Imaging Health Inform. 2021;11(3):810–6.

    Article  Google Scholar 

  14. Zhang DP, Risser L, Friman O, Metz C, Neefjes L, Mollet N, Niessen W, Rueckert D. Nonrigid registration and template matching for coronary motion modeling from 4D CTA. Lecture notes in computer science. 2010, vol 6204, pp. 210–21.

  15. Hadjiiski L, Zhou C, Chan HP, Chughtai A, Agarwal P, Kuriakose J, Patel S, Wei J, Kazerooni E. Automated registration of coronary arterial trees from multiple phases in coronary CT angiography (cCTA). In: Proceedings of the SPIE medical imaging 2013, p. 8670:86703 M.

  16. Luo Y, Feng J, Xu M, Zhou J, Min JK, Xiong G. Registration of coronary arteries in computed tomography angiography images using hidden markov model. In: Proceedings of the annual international conference of the ieee engineering in medicine and biology society. 2015, pp. 1993–6.

  17. Zeng S, Feng J, An Y, Lu B, Lu J, Zhou J. Towards Accurate and Complete Registration of Coronary Arteries in CTA Images. In: Proceedings of the medical image computing and computer-assisted intervention 2018, pp. 419–27.

  18. Cao Q, Broersen A, Kitslaar PH, Yuan M, Lelieveldt BP, Dijkstra J. Automatic coronary artery plaque thickness comparison between baseline and follow-up CCTA images. Med Phys. 2020;47(3):1083–93.

    Article  Google Scholar 

  19. Lim S, Park TY, Jeong H, Lee J. Accurate vascular structure extraction method in 2D X-ray angiogram. J King Comput. 2017;13(1):82–90.

    Google Scholar 

  20. Song Y, Lee J, Shin Y. B-spline based accurate nonrigid registration of ROI: application to chest CT. J King Comput. 2016;12(2):87–96.

    Google Scholar 

  21. Han D, Shim H, Jeon B, Jang Y, Hong Y, Jung S, Ha S, Chang HJ. Automatic coronary artery segmentation using active search for branches and seemingly disconnected vessel segments from coronary CT angiography. PLoS ONE. 2016;11(8):e0156837.

    Article  Google Scholar 

  22. Lee W. Technical aspect of coronary CT angiography: imaging tips and safety issues. J Korean Med Assoc. 2007;50(2):104–8.

    Article  Google Scholar 

  23. Park TY, Kang S, Koo G, Lee J. Fast and accurate rigid Registration Method of Cardiac vessels in 3D follow-up cardiac CTA images. J King Comput. 2017;13(4):59–67.

    Google Scholar 

  24. Rivest-Henault D, Sundar H, Cheriet M. Nonrigid 2D/3D registration of coronary artery models with live fluoroscopy for guidance of cardiac interventions. IEEE Trans Med Imaging. 2012;31(8):1557–72.

    Article  Google Scholar 

  25. Hong H, Lee J, Yim Y. Automatic lung nodule matching on sequential CT images. Comput Biol Med. 2008;38(5):623–34.

    Article  Google Scholar 

  26. Park TY. Convolutional neural network-based segmentation and non-rigid registration in multi-modality images for image-guided intervention. Ph. D. Thesis, Soong-Sil University, 2019.

  27. Kaila G, Kitslaar P, Tu S, Penicka M, Dijkstra J, Lelieveldt B. Fusion of CTA and XA data using 3D centerline registration for plaque visualization during coronary intervention. In: Proceedings of the medical imaging 2016: image-guided procedures, robotic interventions, and modeling. 2016;9786, p. 978606.

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Acknowledgements

This study was a basic research project (No. 2020R1A2C1102727) supported by the National Research Foundation of Korea and financed by the Government (Ministry of Science and ICT) in 2020. In addition, this study was a basic research project (No. 2020R1A6A3A01099507) supported by the National Research Foundation of Korea and financed by the Government (Ministry of Education) in 2020.

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Correspondence to Jeongjin Lee.

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Jeong, H., Park, T., Khang, S. et al. Non-rigid registration based on hierarchical deformation of coronary arteries in CCTA images. Biomed. Eng. Lett. 13, 65–72 (2023). https://doi.org/10.1007/s13534-022-00254-8

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