Using robotic systems in order to determine biomechanical ...

BIOMECH 2007 

Medicine meets Engineering 

June 13th - June 15th 2007 

Regensburg, Germany 

Using robotic Systems to Determine Biomechanical 

Properties of Soft Tissues 

Elizete Kunkel, Ralf Westphal, Markus Rilk, Analía Moral, Daniel Rode, Friedrich M Wahl 

a.moral@tu-bs.de 

Institute for Robotics and Process Control 

Technical University of Braunschweig, Germany 

www.cs.tu-bs.de/rob

Introduction 

Project: Robot assisted ESS (Endoscopic Sinus Surgery) 

Intelligent guidance of the endoscope through a robotic arm so that the surgeon 

can operate with both hands. In cooperation with ENT-Bonn 

Institute for Robotics and Process Control, Technical University of Braunschweig, Germany 2 

Analia Moral: a.moral@tu-bs.de - www.cs.tu-bs.de/rob

Introduction 

Robot assisted ESS (Endoscopic Sinus Surgery): Specific tasks 

• automatic 3D reconstruction of the nasal cavity, their structures and the paranasal 

sinuses 

Frontal 

Sinus 

Sphenoidal 


Ethmoidal 


Maxillary 



Analia Moral: a.moral@tu-bs.de - - www.cs.tu-bs.de/rob

Introduction 


• biomechanical properties of the nasal cavity structures (FEM) (Kunkel, 2007) 

Anterior 

segment 

Case 1: punctual Case2: horizontal 

contact contact 

Case 

2: 

Lateral 

Case 3: vertical 

contact 

Case 

3: 

Cross 

Posterior 

segment 

Anterior 

segment 

13.6 mm 

10.3 mm 

Posterior 

segment 

Posterior 

segment inner 

47.7 mm 



Introduction 


• safe control strategy for robotic endoscope guidance (Rilk, 2005) 

3D- Model 

+ 

Biomechanical Model 

+ 

Medical Experience 



Contents 

• Motivation 

• Materials and Methods 

• Experimental Results 

• Summary and Conclusions 



Motivation 

• biomechanical tests consisting in the measurement of 

contact forces and deformations in tissue phantoms and 

porcine soft tissues (liver, brain, stomach and intestine) 

during the tests 

• experimental methodology for future assesses in the 

determination of material properties (as elastic module and 

Poison’s ratio) of human nasal tissues 

• modeling and simulation of the nasal structures using FEM 

for Robot Assisted Endonasal Surgery 



Materials and Methods 

• Experimental Set-up: 

Microindenter 

Robotic Systems: TX40 – RX60 



Experimental Set-up: 


Handle 

Caliper 

• 5-DOF 

• Shaft with a cylindrical flat ended probe 

• Handles 

• Resolution of the displacements: 1 µm 

• 6-axis force/torque sensor 

(ATI Nano 43SI-36-0.5) 

• Maximal force: 36 N 

• Custom software to record forces and 

displacements 

Probe 

Force/torque sensor 



Experimental Set-up: Robotic Systems (RX60 and TX40) 

1 

2 

• 6 DOF (only revolute joints) 

• Maximal lineal speed: 8 / 8.2 m/s 

• Repeatability ± 0.02 mm 

Stäubli Robots Specifications 

Reach at Wrist 

RX60 

665 mm 

TX40 

515 mm 

• End effector assembled with 

a flat ended cylindrical probe and a 

force/torque sensor 

Nominal Load Capacity 

Maximum Load Capacity 

2.5 kg 

10 kg 

1.7 kg 

2.3 kg 



Materials and Methods 

Samples: 

• Phantoms (PVC) (Di Maio, 2003) 

• Porcine‘s soft tissues (liver, brain, mucose) 



Samples Preparation: 

Phantoms 

• Isotropic and linear behavior (Di Maio, 2003) 

• PVC (M-F Manufacturing, TX) 

Super-soft 

Soft 

100 mm 

Hard 

100 mm 

30 mm 

E (0,01 MPa - 0,1 MPa) in the range of E of human tissues (Fung, 1993) 

Es.soft = 0,065 MPa (Di Maio, 2003) 

Esoft = 0,147 MPa (IRP, 2006) 



Samples Preparation: 

Porcine Soft Tissue 

Liver 

Brain 

From the local abatoar 

No preconditioning 

Fresh 

No embebed into saline solution 

Mucose: 

Stomach / Intestine / Nasal cavity 



Procedures 

Used in both experimental setups 

Repeated with all samples 

Procedure: 

• Small recipients 

• Offset correction 

• Probe perpendicular to the surface‘s 

sample 

• Preload of 0.02 N 

• Constant load rate: 0.2 mm/s 

• Maximal indentation depth: 

4mm / 1mm 

load 

soft tissue 

diameter of the probe 

rigid substrate 

indentation 

depth 



Experimental Results 

To verify the response of the force/torque sensor mounted in both robotic systems: 

Stäubli TX40 / Microindenter 

Stäubli RX60 

4 N 



Experimental Results: Using Phantoms I 

Indentation tests in Soft (s) and Super Soft (ss) tissue phantoms 


Stäubli TX40 

0.25 

0.2 

Es = 0.147 MPa 

Es = 0.147 MPa 

Ess = 0.065 MPa 

] 

[N 

d 

a 

lo 

0.15 

0.1 

0.05 

Es s = 0.065 MPa 

0 

-0.1 0 0.1 0.2 0.3 0.4 0.5 

depth [mm] 



Experimental Results: Using Phantoms II 

Indentation tests in soft tissue phantoms using different sized indenters 

Jin et al., 2004 

L oad [N ] 

-0.25 

-0.2 

-0.15 

-0.1 

2 mm 

4 mm 

6 mm 

Stäubli TX40 

-0.05 


3 

2.5 

0 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 

Depth [mm] 

6 mm 

L oad [N ] 

-0.25 

-0.2 

-0.15 

-0.1 

-0.05 

0 

6 mm 

4 mm 

2 mm 

lo ad [N] 

2 

1.5 

1 

0.5 

4mm 

2mm 

0 0.1 0.2 0.3 0.4 0.5 

Depth [mm] 

0 

0 0.5 1 1.5 2 2.5 3 3.5 4 

depth [mm] 



Experimental Results: Using Phantoms III 

Hysteresis tests in soft tissue phantoms 


Stäubli TX40 



Experimental Results: Using Soft Tissues I 

Hysteresis tests in porcine‘s brain 


Stäubli TX40 

0.1 

0.08 

0.06 

Load [N] 

Force [N] 

0.04 

0.02 

0 

Depth [mm] 

-0.02 

-2 -1 0 1 2 3 4 

Time [s] 

Depth [mm] 



Experimental Results: Using Soft Tissue II 

Indentation test on porcine‘s brain tissue: Stäubli TX40 

(Miller et al., 2000) 

Load rate 

Displacement 

In-vitro / in-vivo 

Maximal force 

Time 



Experimental Results: Using Soft Tissue III 

Hysteresis tests in porcines‘s liver 


Stäubli TX40 

1.2 

1 

0.8 

Load [N] 

F orce [N ] 

0.6 

0.4 

0.2 

0 

Depth [mm] 

-0.2 

0 0.5 1 1.5 2 2.5 3 3.5 4 

Depth 

Time 

[mm] 

[s] 



Experimental Results: Using Soft Tissue VI 

Hysteresis tests in porcines‘s liver : very low load rate 



Experimental Results: Soft Tissue V 

Indentation tests in porcines‘s mucosa 

Microindenter (stomach) 

14 

Stäubli TX40 (nasal cavity) 

12 

10 

Load [N] 

load [N] 

8 

6 

4 

2 

depth [mm] 

0 

0 0.2 0.4 0.6 0.8 1 

depth [mm] 



Experimental Results: 

2 

1.8 

1.6 

mucose 

d [N] 

a 

lo 

1.4 

1.2 

1 

0.8 

0.6 

0.4 

soft 

super-soft 

liver 

0.2 

brain 

0 

0 0.5 1 1.5 2 2.5 3 

depth [mm] 




Tingelhoff et al., 2006 

94.9 % ≤ 7N 




1. Nasal Septum Cartilage 

2. Inferior Turbinate 

3. Middle Turbinate 

1 

3 

2 

9 

4 8 

6 7 

5 

M 

EB 

PU 

IT 

MT 

IT 

M 

4. Sphenoidal Sinus 

5. Uncinate Process 

6. Bulla Ethmoidalis 

7. Lamina Papyracea 

8. Canalis Carotis 

9. Lamina Cribrosa 

[N ] F 

F orce, 6 

4 

Yie ld Force 

Plas tic Region 

10 

Ultimate Force 

8 

2 

Elas tic Region 

Ultimate 

Deformation 

2 2.5 3 3.5 4 4.5 

De formation, L [mm] 




Kunkel et al., 2007 



Summary and Conclusions 

• Mechanical tests performed on phantoms and porcine tissues 

using different methodologies 

• Results are comparable to those in the literature [2], [3] and 

[6] 

• Basis and experience to work with robotic systems: reduction 

of time, repeatability, loading rate, displacements. 

• to work with bone from human cadaver heads (ex-vivo) 

and nasal cartilage from porcine heads (in-vitro) (Kunkel 

et al., 2007; Moral et. al, 2007) 



References for this work 

[1] FUNG, YC, Biomechanics. Mechanical Properties of Living Tissues. Springer-Verlag, 

New York. 2nd ed., 1993. 

[2] MILLER K, Biomechanics of soft tissues. Med Sci Monit, 2000;6(1):158-167. 

[3] MILLER K, Chinkei K, Orssengo G, Bednarz P. Mechanical properties of brain tissue in-vivo: 

experiment and computer simulation. Journal of Biomechanics 33(2000)1369-1376. 

[4] JIN H, Lewis JK. Determination of Poisson ratio of articular cartilage by indentation using 

different-sized indenters. Transactions of the ASME. 126.(2004)138-145. 

[5] HU T, Reality-based soft tissue Probing: Experiments and computational model for application 

to minimally invasive surgery: Doctor Thesis: Drexel University. 2006. 

[6] BROUWER I, Ustin J, Bentley L, Sherman A, Dhruv N, Tendick F. Measuring In Vivo Animal 

Soft Tissue Properties for Haptic Modeling in Surgical Simulation. Medicine Meets Virtual Reality 

69(2001)69-74. 

[7] DIMAIO SP, Modelling, simulation and planning of needle motion in soft tissue. Doctor Thesis. 

University of British Columbia, 2003. 


Analia Moral: a.moral@tu-bs.de

Related works of the authors 

K. Tingelhoff, A. Moral, M. Kunkel, M. Rilk, I. Wagner, K. Eichhorn. F. Wahl, F. Bootz, 

Comparison between Manual and Semi-automatic Segmentation of Nasal Cavity and 

Paranasal Sinuses from CT images, 29th Annual International Conference of the IEEE 

Engineering in Medicine and Biology Society, Lyon, France. August, 2007 (accepted) 

A. Moral, M. Kunkel, M. Rilk, I. Wagner, K. Eichhorn. F. Wahl, F. Bootz, 3D Endoscopic 

Approach for Endonasal Sinus Surgery, 29th Annual International Conference of the IEEE 

Engineering in Medicine and Biology Society, Lyon, France. August, 2007 (accepted) 

A. Moral, M. Kunkel, M. Rilk, F. Wahl, K. Tingelhoff, F. Bootz, Paranasal Sinuses 

segmentation/reconstruction for robot assisted endonasal surgery, Thematic Conference on 

Computational Vision and Medical Image Processing ECCOMAS VIPIMAGE, Porto, Portugal. 

October, 2007 (accepted) 

M. Kunkel, A. Moral , M. Rilk, F. Wahl, Influence of preconditioning on the relaxation 

behavior of porcine septal cartilage using different sized indenters, XXI Congress of 

International Society of Biomechanics ISB, Taipei, Taiwan. July, 2007 (accepted) 



Related works of the authors 

M. Kunkel, A. Moral , R. Westphal, D. Rode, M. Rilk, F. Wahl, Using robotic systems in 

order to determine biomechanical properties of soft tissues, Studies in Health Technology 

and Informatics, IOS book series, Holland 2nd Symposium on Applied Biomechanics, 

Medicine Meets Engineering, BIOMECH, Regensburg, Germany. June, 2007 

M. Kunkel, R. Westphal, A. Moral , I. Wagner, K. Eichhorn. F. Wahl, F. Bootz, Ex vivo 

indentation tests to determine mechanical properties of soft tissues, Deutscher Kongress 

für Biomechanik, 5. Jahrestagung der Deutschen Gesellschaft für Biomechanik (DGfB), 

Köln, Deutschland. February, 2007. 

Rilk, M.; Winkelbach; S. und Wahl, F. Partikelfilter-basiertes Tracking chirurgischer 

Instrumente in Endoskopbildern. Bildverarbeitung für die Medizin 2006, Springer, ISBN: 3- 

540-32136-5, pp. 414-418. 

Institute for Robotics and Process Control, Technical University of Braunschweig, Germany 31

Thank you for your attention ! 

Questions ? 



Experimental Results 

Load [N] 

Depth [mm] 

Load [N] 

Depth [mm] 

Institute for Robotics and Process Control, Technical University of Braunschweig, Germany 33

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