nach oben

International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

06.01.2021 | Original Article

Deformation modeling based on mechanical properties of liver tissue for virtuanormal vectors of trianglesl surgical simulation

verfasst von: Jing Yang, Ming Hu, Xinge Shi, Deming Zhao, Lingtao Yu

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 2/2021

Einloggen, um Zugang zu erhalten

Abstract

Purpose

In this paper, a method for rapidly constructing a virtual surgical simulation system is proposed. A deformation model based on the mechanical properties of the liver and a rapid collision detection between the surgical micro-instruments and the liver tissue are included in this method. The purpose of this work is to improve the accuracy and real time of particle model deformation interaction in virtual surgery system.

Methods

Firstly, a finite element model is established based on the constitutive model parameters of liver tissue. According to the simulation results, a mathematical model of node displacement is established. Secondly, the virtual liver is established based on the fast model reconstruction method, and the virtual manipulator is controlled by Geomagic Touch manipulator. Based on the hybrid bounding box, a rapid collision detection process between the instrument and liver is realized and the proposed deformation method is used to simulate the deformation of liver tissue.

Results

The simulation and experiment results show that the proposed deformation model can achieve high deformation interaction accuracy. The collision detection algorithm based on the hybrid bounding boxes can realize the collision between the liver and the instrument, and the established virtual surgical simulation system can simulate the liver tissue deformation in the case of small loading displacement.

Conclusions

The effectiveness of the collision detection algorithm and deformation model was verified by an established virtual surgery simulation system. The proposed rapid construction method of virtual surgical simulation is feasible.

Ziqiang Ni, Tianmiao Wang, Da Liu (2015) Survey on medical robotics. J Mech Eng 51(13):45–52. https://doi.org/10.3901/jme.2015.13.045CrossRef

Wang W, Li JM, Wang SX, Su H, Jiang XM (2016) System design and animal experiment study of a novel minimally invasive surgical robot. Int J Med Robot Comput Assist Surg 12:73–84. https://doi.org/10.1002/rcs.1658CrossRef

Peters BS, Armijo PR, Krause C, Choudhury SA, Oleynikov D (2018) Review of emerging surgical robotic technology. Surg Endosc 32(4):1636–1655. https://doi.org/10.1007/s00464-018-6079-2CrossRefPubMed

Wang ZY, Zi B, Wang DM, Qian J, You W, Yu LT (2019) External force self-sensing based on cable-tension disturbance observer for surgical robot end-effector. IEEE Sens J 19(13):5274–5284. https://doi.org/10.1109/jsen.2019.2903776CrossRef

Shinichiro Yamada, Shimada Mitsuo, Imura Satoru, Morine Yuji, Tetsuya Ikemoto Yu, Saito Chie Takasu, Yoshikawa Masato, Teraoku Hiroki, Yoshimoto Toshiaki, Takata Atsushi (2016) Effective stepwise training and procedure standardization for young surgeons to perform laparoscopic left hepatectomy. Surg Endosc 31(6):2623–2629. https://doi.org/10.1007/s00464-016-5273-3CrossRef

Basdogan C, Sedef M, Harders M, Wesarg S (2007) VR-based simulators for training in minimally invasive surgery. IEEE Comput Graphics Appl 27(2):54–66. https://doi.org/10.1109/mcg.2007.51CrossRef

Guedes HG, Ferreira ZMCC, Leão LRD, Souza Montero EF, Otoch JP, Artifon ELA (2019) Virtual reality simulator versus box-trainer to teach minimally invasive procedures: a meta-analysis. Int J Surg 61:60–68. https://doi.org/10.1016/j.ijsu.2018.12.001CrossRefPubMed

Mohammadreza Faieghi O, Tutunea-Fatan Remus, Eagleson Roy (2020) Parallelized collision detection with applications in virtual bone machining. Comput Methods Programs Biomed 188:105263. https://doi.org/10.1016/j.cmpb.2019.105263CrossRefPubMed

ElBadrawy Asma A, Hemayed Elsayed E, Fayek Magda B (2012) Rapid collision detection for deformable objects using inclusion-fields applied to cloth simulation. J Adv Res 3(3):245–252. https://doi.org/10.1016/j.jare.2011.07.006CrossRef

10.

Xie K, Yang J, Zhu YM (2007) Fast collision detection based on nose augmentation virtual surgery. Comput Methods Programs Biomed 88(1):1–7. https://doi.org/10.1016/j.cmpb.2007.06.004CrossRefPubMed

11.

Wang Y, Hu Y, Fan J, Zhang Y, Zhang Q (2012) Collision detection based on bounding box for NC machining simulation. Phys Procedia 24:247–252. https://doi.org/10.1016/j.phpro.2012.02.037CrossRef

12.

Camara M, Mayer E, Darzi A, Pratt P (2016) Soft tissue deformation for surgical simulation: a position-based dynamics approach. Int J Comput Assist Radiol Surg 11(6):919–928. https://doi.org/10.1007/s11548-016-1373-8CrossRefPubMedPubMedCentral

13.

Luo M, Xie H, Xie L, Cai P, Gu LX (2014) A robust and real-time vascular intervention simulation based on Kirchhoff elastic rod. Comput Med Imaging Graph 38(8):735–743. https://doi.org/10.1016/j.compmedimag.2014.08.002CrossRefPubMed

14.

Zhou JY, Luo Z, Li CQ, Deng M (2018) Real-time deformation of human soft tissues: a radial basis meshless 3D model based on Marquardt’s algorithm. Comput Methods Programs Biomed 153:237–252. https://doi.org/10.1016/j.cmpb.2017.09.008CrossRefPubMed

15.

Zhang JN, Zhong YM, Gu CF (2017) Energy balance method for modelling of soft tissue deformation. Comput Aided Des 93:15–25. https://doi.org/10.1016/j.cad.2017.07.006CrossRef

16.

Tonutti M, Gras G, Yang GZ (2017) A machine learning approach for real-time modelling of tissue deformation in image-guided neurosurgery. Artif Intell Med 80:39–47. https://doi.org/10.1016/j.artmed.2017.07.004CrossRefPubMed

17.

Zou YN, Liu PX (2017) A high-resolution model for soft tissue deformation based on point primitives. Comput Methods Programs Biomed 148:113–121. https://doi.org/10.1016/j.cmpb.2017.06.013CrossRefPubMed

18.

Zhang GM, Xia JJ, Liebschner M, Zhang XY, Daeseung K, Zhou XB (2016) Improved Rubin–Bodner model for the prediction of soft tissue deformations. Med Eng Phys 38(11):1369–1375. https://doi.org/10.1016/j.medengphy.2016.09.008CrossRefPubMedPubMedCentral

19.

Peterlík I, Courtecuisse H, Rohling R, Abolmaesumi P, Nguan C, Stéphane Cotin, Salcudean S (2018) Fast elastic registration of soft tissues under large deformations. Med Image Anal 45:24–40. https://doi.org/10.1016/j.media.2017.12.006CrossRefPubMed

20.

Zhang J, Zhong Y, Smith J, Gu C (2016) A new ChainMail approach for real-time soft tissue simulation. Bioengineered 7(4):246–252. https://doi.org/10.1080/21655979.2016.1197634CrossRefPubMedPubMedCentral

21.

Rodríguez A, Leon A, Arroyo G (2016) Parallel deformation of heterogeneous ChainMail models: Application to interactive deformation of large medical volumes. Comput Biol Med 79:222–232. https://doi.org/10.1016/j.compbiomed.2016.10.012CrossRefPubMed

22.

Suzuki S, Suzuki N, Hattori A, Uchiyama A, Kobayashi S (2004) Sphere-filled organ model for virtual surgery system. IEEE Trans Med Imaging 23(6):714–722. https://doi.org/10.1109/tmi.2004.826947CrossRefPubMed

23.

Vidal FP, Villard PF, Lutton E (2012) Tuning of patient-specific deformable models using an adaptive evolutionary optimization strategy. IEEE Trans Biomed Eng 59(10):2942–2949. https://doi.org/10.1109/tbme.2012.2213251CrossRefPubMed

24.

Xu S, Liu XP, Zhang H, Hu L (2011) A nonlinear viscoelastic tensor-mass visual model for surgery simulation. IEEE Trans Instrum Meas 60(1):14–20. https://doi.org/10.1109/TIM.2010.2065450CrossRef

25.

Joldes GR, Wittek A, Miller K (2008) An efficient hourglass control implementation for the uniform strain hexahedron using the Total Lagrangian formulation. Commun Numer Methods Eng 24(11):1315–1323. https://doi.org/10.1002/cnm.1034CrossRef

26.

Liu XP, Xu S, Zhang H, Hu L (2011) A new hybrid soft tissue model for visio-haptic simulation. IEEE Trans Instrum Meas 60(99):3570–3581. https://doi.org/10.1109/tim.2011.2161142CrossRef

27.

Jahya A, Herink M, Misra S (2013) A framework for predicting three-dimensional prostate deformation in real time. Int J Med Robot Comput Assist Surg 9(4):e52–e60. https://doi.org/10.1002/rcs.1493CrossRef

28.

Mönch Jeanette, Mühler Konrad, Hansen Christian, Oldhafer Karl-Jürgen, Stavrou Gregor, Hillert Christian, Logge Christoph, Preim Bernhard (2013) The LiverSurgeryTrainer: training of computer-based planning in liver resection surgery. Int J Comput Assist Radiol Surg 8:809–818. https://doi.org/10.1007/s11548-013-0812-zCrossRefPubMed

29.

Pellicer-Valero OJ, Rupérez María José, Martínez-Sanchis Sandra, Martín-Guerrero José D (2020) Real-time biomechanical modeling of the liver using machine learning models trained on finite element method simulations. Expert Syst Appl 143:113083. https://doi.org/10.1016/j.eswa.2019.113083CrossRef

30.

Mattei GioGiorgio, Ahluwalia Arti (2016) Sample, testing and analysis variables affecting liver mechanical properties: a review. Acta Biomater 45:60–71. https://doi.org/10.1016/j.actbio.2016.08.055CrossRefPubMed

31.

Karimi A, Shojaei A (2018) An experimental study to measure the mechanical properties of the human liver. Dig Dis-eases 36:150–155. https://doi.org/10.1159/000481344CrossRef

32.

Yang J, Yu L, Wang L, Wang W, Cui J (2018) The estimation method of Friction in unconfined compression tests of soft tissue. Proc Inst Mech Eng [H] 232(6):573–587. https://doi.org/10.1177/0954411918774377CrossRef

33.

Lingtao Yu, Tao Wang, Huajian Song, Zhengyu Wang, Baoyu Zhang (2014) An optimization algorithm of collision detection applied to virtual surgery. J Harbin Eng Univ 35(9):1164–1170. https://doi.org/10.3969/j.issn.1006-7043.201302019CrossRef

Titel: Deformation modeling based on mechanical properties of liver tissue for virtuanormal vectors of trianglesl surgical simulation
verfasst von: Jing Yang
Ming Hu
Xinge Shi
Deming Zhao
Lingtao Yu
Publikationsdatum: 06.01.2021
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 2/2021
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-020-02297-7

Update Radiologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen

Live-Webinar "Chronische Unterbauch- und Viszeralschmerzen in der Praxis managen"

Springer Medizin

Deformation modeling based on mechanical properties of liver tissue for virtuanormal vectors of trianglesl surgical simulation

Abstract

Purpose

Methods

Results

Conclusions

Neu im Fachgebiet Radiologie

Endlich: Zi zeigt, mit welchen PVS Praxen zufrieden sind

Akuter Schwindel: Wann lohnt sich eine MRT?

Screening-Mammografie offenbart erhöhtes Herz-Kreislauf-Risiko

S3-Leitlinie zu Pankreaskrebs aktualisiert

Update Radiologie

Live-Webinar "Chronische Unterbauch- und Viszeralschmerzen in der Praxis managen"

Springer Medizin

Abstract

Purpose

Methods

Results

Conclusions

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 2/2021

Hard frame detection for the automated clipping of surgical nasal endoscopic video

Survival prediction of patients suffering from glioblastoma based on two-branch DenseNet using multi-channel features

Automatic airway tree segmentation based on multi-scale context information

Correction to: Enhancement of needle visualization and localization in ultrasound

Acknowledgement to reviewers

Usability of cooperative surgical telemanipulation for bone milling tasks

Neu im Fachgebiet Radiologie

Endlich: Zi zeigt, mit welchen PVS Praxen zufrieden sind

Akuter Schwindel: Wann lohnt sich eine MRT?

Screening-Mammografie offenbart erhöhtes Herz-Kreislauf-Risiko

S3-Leitlinie zu Pankreaskrebs aktualisiert

Update Radiologie