Needle insertion (placement) into human body organs is a frequently performed procedure in clinical practice. Its success largely depends on the accuracy with which the needle tip reaches the anatomical target. As the tissue deforms due to interactions with the needle, the target tends to change its position. One possible way to decrease the risk of missing the target can be to account for tissue deformations when planning the needle insertion. This can be achieved by employing computational biomechanics models to predict the tissue deformations.
In this study, for computing the tissue deformations due to needle insertion, we employed a meshless formulation of computational mechanics that uses a spatial discretisation in a form of a cloud of points. We used the previously verified Meshless Total Lagrangian Explicit Dynamics (MTLED) algorithm that facilitates accurate and robust prediction of soft continua/soft tissues mechanical responses under large deformations. For modelling of interactions between the needle and soft tissues, we propose a kinematic approach that directly links deformation of the tissue adjacent to the needle with the needle motion. This approach does not require any assumptions about the exact mechanisms of such interactions. Its parameters can be determined directly from observation of the tissue sample/body organ deformations during needle insertion.
We evaluated the performance of our kinematic approach for modelling the interactions between the needle and the tissue through application in modelling of needle insertion into a cylindrical sample (diameter of 30 mm and height of 17 mm) of Sylgard 527 (by Dow Corning) silicone gel and comparing the results obtained from the model with the experimentally measured force acting on the needle. The needle insertion depth was up to 10 mm. For modelling of the constitutive responses of Sylgard 527 gel, we used the neo-Hookean hyperelastic material model with the shear modulus experimentally determined from compression of Sylgard 527 gel samples.
The general behaviour of the needle force–insertion depth relationship was correctly predicted by our framework that combines a meshless method of computational mechanics for computing the deformations and kinematic approach for modelling the interactions between the needle and soft tissues. The predicted force magnitude differed by only 25% from the experimentally observed value. These differences require further analysis. One possible explanation can be that the neo-Hookean material model we used here may not be sufficient to correctly represent Sylgard 527 gel constitutive behaviour.
Needle insertion simulation Meshless methods Meshless Total Lagrange Explicit Dynamics Hyperelastic material models Computational biomechanics
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This research was supported by the Australian Government through the Australian Research Council's Discovery Projects funding scheme (project DP160100714). Adam Wittek and Anton Khau thank Dr Barry Doyle for use of the VascLab tension–compression rig.
Zhu Y, Magee D, Ratnalingam R, Kessel D (2007) A training system for ultrasound-guided needle insertion procedures. In Ayache N., Ourselin S. and Maeder A. (eds) Proc. of Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007, Springer, Berlin, pp 566–574Google Scholar
Vega RA, Holloway KL, Larson PS (2014) Image-guided deep brain stimulation. Neurosurg Clin N Am 25:159–172CrossRefGoogle Scholar
Apesteguía L, Pina LJ (2011) Ultrasound-guided core-needle biopsy of breast lesions. Insights Imaging 2:493–500CrossRefGoogle Scholar
Leibinger A, Oldfield MJ, Rodriguez Y Baena F (2016) Minimally disruptive needle insertion: a biologically inspired solution. Interface Focus 6:20150107CrossRefGoogle Scholar
Fichtinger G, Deguet A, Masamune K, Balogh E, Fischer GS, Mathieu H, Taylor RH, Zinreich SJ, Fayad LM (2005) Image overlay guidance for needle insertion in CT scanner. IEEE Trans Biomed Eng 52:1415–1424CrossRefGoogle Scholar
Ungi T, Beiko D, Fuoco M, King F, Holden MS, Fichtinger G, Siemens DR (2014) Tracked ultrasound snapshots enhance needle guidance for percutaneous renal access: a pilot study. J Endourol 28:1040–1045CrossRefGoogle Scholar
Bui HP, Tomar S, Courtecuisse H, Cotin S, Bordas SPA (2018) Real-time error control for surgical simulation. IEEE Trans Biomed Eng 65:596–607CrossRefGoogle Scholar
Courtecuisse H, Allard J, Kerfriden P, Bordas SPA, Cotin S, Duriez C (2014) Real-time simulation of contact and cutting of heterogeneous soft-tissues. Med Image Anal 18:394–410CrossRefGoogle Scholar
DiMaio SP, Salcudean SE (2003) Needle insertion modeling and simulation. IEEE Trans Robot Autom 19:864–875CrossRefGoogle Scholar
Oldfield M, Dini D, Giordano G, Rodriguez y Baena F (2013) Detailed finite element modelling of deep needle insertions into a soft tissue phantom using a cohesive approach. Comput Methods Biomech Biomed Engin 16:530–543CrossRefGoogle Scholar
Horton A, Wittek A, Joldes GR, Miller K (2010) A meshless Total Lagrangian explicit dynamics algorithm for surgical simulation. Int J Numer Methods Biomed Eng 26:977–998CrossRefGoogle Scholar
Joldes GR, Chowdhury H, Wittek A, Miller K (2017) A new method for essential boundary conditions imposition in explicit meshless methods. Eng Anal Bound Elem 80:94–104MathSciNetCrossRefGoogle Scholar
Miller K, Horton A, Joldes GR, Wittek A (2012) Beyond finite elements: a comprehensive, patient-specific neurosurgical simulation utilizing a meshless method. J Biomech 45:2698–2701CrossRefGoogle Scholar
Joldes G, Bourantas G, Zwick B, Chowdhury H, Wittek A, Agrawal S, Mountris K, Hyde D, Warfield SK, Miller K (2019) Suite of meshless algorithms for accurate computation of soft tissue deformation for surgical simulation. Med Image Anal 56:152–171.CrossRefGoogle Scholar
Jin X, Joldes GR, Miller K, Yang KH, Wittek A (2014) Meshless algorithm for soft tissue cutting in surgical simulation. Comput Methods Biomech Biomed Engin 17:800–817CrossRefGoogle Scholar
Leibinger A, Forte AE, Tan Z, Oldfield MJ, Beyrau F, Dini D, Rodriguez Y Baena F (2016) Soft tissue phantoms for realistic needle insertion: a comparative study. Ann Biomed Eng 44:2442–2452CrossRefGoogle Scholar
Chowdhury HA, Wittek A, Miller K, Joldes GR (2017) An element free Galerkin method based on the modified moving least squares approximation. J Sci Comput 71:1197–1211MathSciNetCrossRefGoogle Scholar
Washio T, Chinzei K (2004) Needle force sensor, robust and sensitive detection of the instant of needle puncture. In: 7th International Conference on Medical Image Computing and Computer-Assisted Intervention MICCAI 2004, Proceedings: Lecture Notes in computer Science 3217, Berlin, Springer, pp 113–120CrossRefGoogle Scholar
Babuska I, Oden JT (2004) Verification and validation in computational engineering and science: basic concepts. Comput Methods Appl Mech E 193:4057–4066MathSciNetCrossRefGoogle Scholar
Bottan S, Poulikakos D, Kurtcuoglu V (2012) Phantom model of physiologic intracranial pressure and cerebrospinal fluid dynamics. IEEE Trans Biomed Eng 59:1532–1538CrossRefGoogle Scholar
Ma J, Wittek A, Singh S, Joldes G, Washio T, Chinzei K, Miller K (2010) Evaluation of accuracy of non-linear finite element computations for surgical simulation: study using brain phantom. Comput Methods Biomech Biomed Engin 13:783–794CrossRefGoogle Scholar
Agrawal S, Wittek A, Joldes G, Bunt S, Miller K (2015) Mechanical properties of brain–skull interface in compression. In: Doyle B, Miller K, Wittek A, Nielsen MFP (eds) Computational Biomechanics for Medicine X: New Approaches and New Applications. Springer, Cham, pp 83–91Google Scholar
Wittek A, Dutta-Roy T, Taylor Z, Horton A, Washio T, Chinzei K, Miller K (2008) Subject-specific non-linear biomechanical model of needle insertion into brain. Comput Methods Biomech Biomed Engin 11:135–146CrossRefGoogle Scholar
Ivarsson J, Viano DC, Lövsund P, Aldman B (2000) Strain relief from the cerebral ventricles during head impact: experimental studies on natural protection of the brain. J Biomech 33:181–189CrossRefGoogle Scholar
Basati SS, Harris TJ, Linninger AA (2011) Dynamic brain phantom for intracranial volume measurements. IEEE Trans Biomed Eng 58:1450–1455CrossRefGoogle Scholar