KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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3D Reconstruction of 3D Printed Medical Metal Implants

3D 출력 의료용 금속 임플란트에 대한 3D 복원

  • Received : 2022.07.13
  • Accepted : 2022.11.02
  • Published : 2023.05.31
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Abstract

Since 3D printed medical implant parts usually have surface defects, it is necessary to inspect the surface after manufacturing. In order to automate the surface inspection, it is effective to 3D scan the implant and reconstruct it as a scan model such as a point cloud. When constructing a scan model, the characteristics of the shape and material of the implant must be considered because it has characteristics different from those of general 3D printed parts. In this paper, we present a method to reconstruct the 3D scan model of a 3D printed metal bone-plate that is one kind of medical implant parts. Multiple partial scan data are produced by multi-view 3D scan, and then, we reconstruct a scan model by alignment and merging of partial data. We also present the process of the scan model reconstruction through experiments.

3D 출력된 의료용 임플란트(implant) 부품은 보통 표면에 결함이 발생되므로, 출력 후 표면을 검사하는 과정이 필요하다. 자동화된 표면 검사를 수행하기 위해서는 임플란트를 3D 스캔하여 점군(point cloud)과 같은 스캔 모델로 복원하는 방법이 효과적이다. 스캔 모델을 구성할 때, 임플란트는 일반적인 3D 출력 제조 부품과 다른 특성들을 가지므로, 임플란트의 형태와 재료의 특성에 대한 고려가 필요하다. 본 논문에서는 의료용 임플란트 부품의 한 종류인 금속 bone-plate의 3D 출력물에 대해 스캔 모델로 복원하는 방법을 제안한다. 다각도의 시점에서 3D 스캔을 수행하여 다수의 부분 스캔 데이터를 생성한 뒤, 이들에 대해 정렬(alignment)과 정합(merging)을 수행하여 스캔 모델로 복원한다. 또한, 실험을 통해 스캔 모델로 복원하는 과정을 보인다.

Keywords

Acknowledgement

이 논문은 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구(NRF-2020R1A2C1008912)이며, ASK 2022(춘계학술발표대회)에서 발표된 논문을 확장한 논문임.

References

  1. National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, The forecast analysis report for innovative medical devices, 2017.
  2. U. Seo and K. -J. Kim, "3D printing based patient-specific orbital implant design and production by using a depth image," Journal of Korea Multimedia Society, Vol.23, No.8, pp.903-914, 2020. https://doi.org/10.9717/KMMS.2020.23.8.903
  3. Y. J. Jeong, D. H. Choi, and K. -J. Kim, "Algorithm for fabricating 3D breast implants by using MRI and 3D Scan Data," Journal of Korea Multimedia Society, Vol.22, No. 12, pp.1385-1395, 2019. https://doi.org/10.9717/KMMS.2019.22.12.1385
  4. S. Lee et al., "A review of three-dimensional printing technology for medical applications," Journal of the Korean Society of Radiology, Vol.80, No.2, pp. 213-225, 2019. https://doi.org/10.3348/jksr.2019.80.2.213
  5. R. J. Mobbs, M. Coughlan, R. Thompson, C. E. Sutterlin, and K. Phan, "The utility of 3D printing for surgical planning and patient-specific implant design for complex spinal pathologies: Case report," Journal of Neurosurgery, Vol.26, pp.513-8, 2017. https://doi.org/10.3171/2016.9.SPINE16371
  6. R. K. Chen, Y. -A. Jin, J. Wensman, and A. Shih, "Additive manufacturing of custom orthoses and prostheses - A review," Additive Manufacturing, Vol.12, Part A, pp.77-89, 2016. https://doi.org/10.1016/j.addma.2016.04.002
  7. H. Lal and M. K. Patralekh, "3D printing and its applications in orthopaedic trauma: A technological marvel," Journal of Clinical Orthopaedics and Trauma, Vol.9, No.3, pp.260-268, 2018. https://doi.org/10.1016/j.jcot.2018.07.022
  8. D. Cerniglia and N. Montinaro, "Defect detection in additively manufactured components: Laser ultrasound and laser thermography comparison," Procedia Structural Integrity, Vol.8, pp.154-162, 2018. https://doi.org/10.1016/j.prostr.2017.12.016
  9. L. Chen, X. Yao, P. Xu, S. K. Moon, and G. Bi, "Rapid surface defect identification for additive manufacturing with in-situ point cloud processing and machine learning," Virtual and Physical Prototyping, Vol.16, No.1, pp.50-67, 2021. https://doi.org/10.1080/17452759.2020.1832695
  10. A. Plessis, I. Yadroitsava, and I. Yadroitsev, "Effects of defects on mechanical properties in metal additive manufacturing: A review focusing on X-ray tomography insights," Materials and Design, Vol.187, 2020.
  11. C. A. Madrigal, J. W. Branch, A. Restrepo, and D. Mery, "A method for automatic surface inspection using a model-based 3d descriptor," Sensors, Vol.17, s17102262, 2017.
  12. I. Jovancevic et al., "3D point cloud analysis for detection and characterization of defects on airplane exterior surface," Journal of Nondestructive Evaluation, Vol.36, No.74, pp.1-17, 2017. https://doi.org/10.1007/s10921-016-0379-z
  13. CloudCompare 3D Point cloud and mesh processing software Open Source Project, http://www.cloudcompare.org
  14. B. Ye and K. -J. Kim, "Scan model construction for 3D printout of metal implant parts," Proceedings of the Annual Spring Conference of Korea Information Processing Society Conference (KIPS) 2022, Vol.29, No.1, pp.18-20, 2022.
  15. J. Zhu, Z. Li, S. Du, L. Ma, T. Zhang. "Surface reconstruction via efficient and accurate registration of multiview range scans," Optical Engineering, Vol.53, No.10, pp. 102104, 2014.
  16. P. J. Besl and N. D. McKay, "A method for registration of 3-D shapes," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.14, No.2, pp.239-256, 1992. https://doi.org/10.1109/34.121791
  17. Y. Chen and G. Medioni, "Object modelling by registration of multiple range images," Image and Vision Computing, Vol.10, No.3, pp.145-155, 1992. https://doi.org/10.1016/0262-8856(92)90066-C
  18. Point Cloud Library, PCL 1.11.1 [Internet], https://pointclo uds.org
  19. S. Byun, K. Jung, S. Im, and M. Chang, "Registration of 3D scan data using image reprojection," International Journal of Precision Engineering and Manufacturing, Vol. 18, No.9, pp.1221-1229, 2017. https://doi.org/10.1007/s12541-017-0143-z