Para Eval Mansoor

8/4/2019 Para Eval Mansoor

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Figure 1: Open (left) and laparoscopic (right) incisions. Laparoscopicsurgery requires limited incisions compared to open surgery

Parameter evaluation for Virtual Laparoscopic

Simulation

Shamyl Bin Mansoor, Zaheer Mukhtar, Muddassir Malik, Zohaib Amjad, Hammad QureshiSchool of EECS,

National university of Sciences & Technology,

Islamabad, Pakistan

{shamyl.mansoor,zaheer.mukhtar,zohaib.amjad,muddassir.malik,hammad.qureshi}@seecs.edu.pk

Abstract — Virtual Reality based surgical simulators have become

quite common for training of surgeons for different surgical

skills. Simulators have been widely used particularly in minimal

invasive surgery. In this paper we find parameters that would be

required to create a real time working simulation for exercises

given in the Fundamentals of Laparoscopic Surgery curriculum.

We use peg transfer exercise as our example in this work and

create simulations for parameter analysis using SOFA, an opensource surgical framework [1]. The parameters we choose are

generic and can be used to create other more complex

simulations like cholecystectomy [2] (gall bladder removal) and

appendectomy (appendix removal). We show the implementation

of these parameters and their behavior in a virtual reality

surgical simulation. This work can be used by researchers and

developers to choose the right parameters in the context of the

simulation they are developing. It also shows the cost and

behavior of achieving good visualization (frames per second),

physical characteristics and a realistic behavioral model to be

used in simulations for training purposes.

Keywords: Surgical Simulations, Virtual Reality,

Biomechanical Modeling, Laparoscopic Surgery

I. I NTRODUCTION

Simulators have played an important role in the training of personnel for skills that would be difficult to acquire otherwise.Pilots are trained using sophisticated flight simulators whichallow training under scenarios that would either not be possiblein real life or would be too expensive and dangerous to bereplicated for training purposes. For example pilots are trainedfor dangerous scenarios such as recovering from a stall, landingin bad weather conditions and crash landing scenarios. Other areas in which simulators are used include marine simulators,military command and control simulators and surgicalsimulators etc. Surgery is generally a critical procedure inwhich a slight mistake can be fatal. Therefore, it requiresacquisition of skills that are difficult to learn for a new

practitioner and require a lot of time and practice. Surgicalsimulators provide a mechanism and platform where goodsurgery skills may be acquired and developed in a costeffective manner. Moreover, the error margin in a surgery isvery limited therefore the training system should ideally allowsurgeons to learn from their mistakes, contain mechanisms for training surgeons and also evaluate the skill of the surgeon ashe/she performs the simulated surgery.

Open or traditional surgery requires a number of challenging skills that are typically attained over a long periodof time with lots of hands on experience. However, in recentyears laparoscopic or minimal invasive surgery has becomequite popular as it allows for quick patient recovery, lesschance of post operative infections and allows a surgeon to

perform many such operations in a single day, which is due to

the small incisions that are made in the body as shown in figure1. These advantages have resulted in laparoscopic surgery

becoming more common than traditional invasive surgery,while making the task of surgeons more challenging anddifficult. Performing minimal invasive surgery requires specialskills like expert hand eye coordination, precise movement of laparoscopic instruments within a confined space and theability to successfully perform surgery without damaging anyof the organs. These special psychomotor skills cannot beacquired without hands on practice.

The paper is formatted as follows. Firstly in section II wediscuss recent work in the surgical simulations area, then insection III we discuss the importance of modeling the pegtransfer exercise for our experimentation. In section IV wediscuss the methods that we have used in order to model thisexercise using the open source framework SOFA, in section Vwe present the selection of parameters to evaluate and describeour results and finally conclude in section VI, analyzing our results and discuss the future improvements.

This research has been funded by National ICT R&D Fund, Ministry of IT&T, Government of Pakistan



Figure 2 Peg Transfer Exercise. Torus is moved from one peg

to another peg using both hands.

II. R ECENT WORK

Recently a lot of research has been conducted to createrealistic real time surgical simulators that allow surgeons totrain in performing complex tasks and procedures. The need for such simulators is critical since training on human beings isunethical and dangerous. Therefore simulators are required thatare realistic in behavior allowing surgeons to learn in anenvironment that simulates the real world effectively. Although

this is a difficult task, but with the computation power that isavailable these days it has become feasible to develop thesecomplex surgical simulations that exhibit realistic behavior inreal time. It is also required that behavior of surgery simulationenvironment is interactive therefore efficient algorithms for deformable modeling and collision detection is required [3].

There are several techniques available that allow themodeling of physical characteristics of objects in a virtualreality environment [4]. For example in order tomathematically model the behavior of a rigid object,

Newtonian mechanics proves useful as it can be used to modelthe behavior of the object in response to forces in theenvironment, such as friction, gravity etc. Hence it is important

that the Newtonian mechanics behind these models isaccurately simulated. Similarly for a non-rigid or a deformableobject, deformation should occur when a force is applied. For instance when a pen is poked in ones’ hand, the skin along withthe flesh deforms in the direction of the force applied.Moreover the deformation is not at a single point but on asurface and can be non-linear in nature. We must cater for such

behavior while modeling a procedure that involves deformableand non-deformable objects.

There are several methods in literature that make it possibleto realistically model the behavior of an object [4]. One of these methods [5] uses hierarchical multi-resolution finiteelement modeling to improve the real time behavior of

deformable objects. Another method [6] has been proposed thatimproves the real time performance of deformable bodysimulation using non-linear finite elements whereas [7] utilizeGPU (Graphical Processing Unit) processing to improve thereal time performance for soft tissue modeling using non-linear FEM. In this paper, we experiment with different techniques tomodel an exercise that is used to train surgeons for laparoscopic instrument handling, depth perception and handeye coordination. We experiment with different parameters andfind the best ones that are suitable in modeling the exercise.The results can be used in modeling and simulating of other laparoscopic surgery simulation exercises as well.

III. THE PEG TRANSFER EXERCISE In this paper we analyze parameters that are best suited to

create simulations which are part of the basic exercises in theFundamentals of Laparoscopic Surgery curriculum [8]. Weshow our results by simulating the peg transfer exercise usingdifferent parameters. These parameters define the behavior of the model with respect to application of different forces. Thefundamentals of Laparoscopic Surgery curriculum (FLS) has

been developed as a standard learning curriculum for surgeonsto attain the basic psychomotor skill set that is required for

performing laparoscopic surgery. There are five basic exercises

that a surgical resident, fellow or practicing surgeon must perform and acquire expertise in. Competence level of asurgeon is evaluated on the basis of his performance on thedifferent exercises that form the part of the fundamentals of laparoscopic surgery. These exercises include peg transfer,

pattern cutting, ligating loop, suturing with an intracorporealknot and suturing with an extracorporeal knot.

In laparoscopic surgery a surgeon must use a monocular optical system to develop new cues to enable depth

perception, be able to manipulate objects within a confinedspace and use both hands in a complimentary manner to cut,dissect, and suture. In this paper we concentrate on the pegtransfer exercise which is used for developing hand eyecoordination skills. It is one of the basic exercises of the FLScurriculum which requires the user to lift pegs from one side of the board, transfer them in mid air to the other hand and replacethem on the other side of the board. Figure 2 shows a snapshotof the peg transfer exercise that we have developed usingSOFA.

This exercise is important as it trains surgeons to attain ahigher level of hand eye coordination which is required in

performing laparoscopic surgery. Peg transfer exercise isavailable in the commercial simulators including LapSim[9]and LapMentor [10]. Using the LapSim system available at theHoly Family Hospital [11] (our project collaborators) we

performed the peg transfer exercise and noted that there areseveral problems with the LapSim’s peg transfer simulation.The pegs when pulled hard actually pass through the cylindersand the virtual laparoscopic instrument collides with the boxsurface incorrectly. It was noted that the instrument actuallysinks into the box, which in reality should act like two rigidobjects colliding with each other. Their collision models arenot accurate and perhaps one of the reasons for that is their model focuses more on visualization than on actual modeling

of the behavior. In our experiments we carefully model theexercise with proper application of collision models so that the

behavior is as realistic as possible. Applying multiple collisionmodels results in decreased frame rate as more calculations arerequired. The method for developing the exercise is detailed inthe next section.

IV. MODELING THE EXERCISE

In this section, we elaborate different methods that we haveused to model the peg transfer exercise. We use an open sourceframework SOFA to develop our exercises. SOFA is a modular



framework that includes a number of different algorithms thatcan be applied to test the behavior of the simulation. In order tomodel and simulate the peg transfer exercise we find the bestcombination of techniques based on the criteria defined by[12]. Although the criterion has been defined for soft-tissuemodeling, it is quite similar to what we require for performingour peg transfer exercise. The factors required for modelingsoft tissue consist of identification of the relevant

biomechanical properties and efficient computation andvalidation of the response behavior. We model our objects inthis exercise by simulating the behavior of rigid plastic objects

by adopting young’s modulus and Poisson distribution valuesthat correspond to plastic materials. We also determine theefficiency of our system by computing the time required for effective visualization and good frame rates.

In order to effectively model an object the first requirementis a good physical model of the object which can be a surfaceor a volumetric model. In our exercise we have usedtetrahedrons to model objects. The second requirement is tomodel the behavior of the object. We need to model threeobjects in our peg transfer exercise; pegs, floor and torus. The

floor is considered as a rigid object with no elastic properties.Similarly we consider the cylinders as rigid objects with noelastic properties. The torus which is the shape of a donut has

been modeled as a deformable object. These characteristics aresimilar to the real life peg transfer model that contains rigidcylinders, rigid floor, however the torus may be made up of

plastic, rubber or metal. In this exercise along with accuracy of the peg model, we are also concerned about accuracy of modeling the collisions between the pegs and their surroundings that consists of the cylinders and the floor.

These collisions should be realistic and perform in real timeas the users should be able to visualize a realistic behavior. If the frame rate goes below 20 Hz then the simulation will show

jitter and will respond slowly to the user interaction. This willaffect the trainee’s learning process and it will also make ithard to evaluate trainee’s skills. Therefore it is important thatwhenever a simulation is modeled, it should be real-time in

behavior and support interactivity.

The volumetric mesh models that we have used in our simulations have been modeled in 3D studio max andconverted to meshes readable by SOFA using the methoddeveloped by [13]. This method uses the surface model togenerate the tetrahedrons based mesh that is readable by SOFAand used in our simulations.

Figure 3 FEM on the left and Mass Spring on the right

The physical model of the peg can be modeled in two ways;using Finite Element Modeling [14] or using Mass Springmodels. In finite element modeling an object is divided intosmaller finite elements and forces and displacements on eachelement are calculated and then as a final step integrated tocalculate the overall deformation effect of forces acting on theobject. In mass springs models, all the connections in a meshare modeled using springs. The elasticity of the spring models

the deformable behavior of the object. We have modeled the peg using both methods but FEM is better in terms of modelingrealistic behavior than mass spring models [14] which are notas accurate. Figure 3 shows the model of the peg that we havecreated using tetrahedron FEM and Mass Spring method tosimulate its deformable behavior.

The FEM based model treats the tetrahedron as the baseelement and finds the deformation on every element and itseffect on neighboring tetrahedrons. In mass spring model every

point is connected with springs, therefore every edge of eachtetrahedron is considered a spring. In both methods theelasticity is defined by the young modulus value which modelsthe stress strain relationship for each element. SOFA only

needs this value to be given as a parameter and generates thestiffness matrix. Stiffness matrix can however be definedseparately and passed as a parameter to both FEM and MassSpring methods if the stiffness of each element is non-uniform.

We have conducted experiments with dense and coarsemeshes of the volumetric models. The denser an object is themore realistic it becomes but requires more computation timesas can be seen from the decrease in the frame rate of thesimulation in our results. On the other hand a coarse objectwould require fewer computations but would behave in a lessrealistic manner.

Figure 4 Point collision model with visual model

The other important factor in this simulation is the collisionmodels. Collision detection is a very important component of any virtual reality system. Collision detection pipeline first

performs coarse collision that is fast and then performsaccurate collision that is slow to compute. There are three kindsof collision algorithms in general use [15]. In the firstalgorithm all the objects are enclosed in spheres or bounding

boxes and collision is performed between them. The issue inthis algorithm is that objects that have a large variation inwidth, depth and height result in visibly wrong collisions. Inthe second method axis aligned bounding boxes are used.These boxes are variable in one of the principle directions andare therefore suitable for enclosing a wide variety of objects.This algorithm is however slower than the first one. In the thirdalgorithm bounding boxes are aligned with respect to the Eigenvector of the object. This results in fine collision detection.

We have applied and experimented with four types of collision models, namely point, line, triangle and spherical



models. In SOFA all these collision models can be appliedindependently as well as grouped together for a single object.We have tested our frame rate and behavior by combiningdifferent collision models.

A point model simply considers the vertex as a collision point and if there is a collision detected there is a collisionresponse that should be generated. Figure 4 shows some pointsin 3- dimensional space which model the floor in our pegtransfer exercise with point collision model applied. We cansee the point model maps to every vertex of the floor, whereasthere is no collision on the edges. In order to have collision onthe edges we apply the line collision model which maps to theedges of the shape. There is a response if a collision is detectedonly at these edges. In our simulation independent collisionmodels are not of much use but are useful only if grouped. Thetriangle model when applied to a surface breaks it into trianglesor can be applied to a mesh model that is made of triangles. Inorder for our model to work as a floor, we need to apply thetriangle model, as our mesh is triangular; therefore the collisionmodel maps all the triangles of the mesh, filling the hollowspace left due to the line model. So now if a peg falls on the

floor, it collides and bounces based on the collision response of the floor as well as the peg’s elastic properties. Figure 5 showsthe line and triangle models applied to the floor mesh.

Figure 5 Line collision model on the left and triangle model on the right

Directly testing the geometry of two volumetric objects for collision when they contain hundreds of polygons is veryexpensive. Bounding volumes are used which cover the wholevolume of the object for overlap before the geometryintersection test can be performed [15]. One such model is thespherical model which when applied to a mesh creates spheresof a defined radius that cover the volume of the mesh. Theimportant parameter here is the radius of the sphere which can

be increased to cover the overall volume of the model. Here wenotice that for our regular shaped floor mesh, the trianglemodel suffices and we do not need the sphere collision model.Figure 6 shows the spherical model applied to our floor meshwith radius values as 1 and 10 respectively. On the left the

sphere model does not cover the volume of the object whereasincreasing the radius of the sphere covers the volume of thefloor mesh but using the sphere model for our floor mesh is notsuitable as it covers extra volume due to the volume of thespheres. If we decrease the radius then we don’t cover theentire volume of the floor.

Figure 6 Sphere collision model with different values for radii

The sphere collision model is more useful for irregular shapes as can be seen in figure 6.

V. EXPERIMENTAL R ESULTS AND DISCUSSION

We have modeled our simulations using the open sourceframework SOFA on an Intel Core i3 processor 3.06 GHz with2GB of RAM and built in graphics memory of 64 MB. Our simulation has a rigid floor, 6 rigid cylinders and a deformable

peg, the shape of a donut. The experiments have been carriedout with two kinds of physical modeling, FEM and MassSprings. We vary the density of our peg model and combine itwith different collision models to note down the frames per

second which determines the visualization performance. Sincewe have used physical modeling, that means that our simulations are modeled based on the physical characteristicsrather than being just ordinary graphical models.

In order to evaluate the different parameters we haveconducted experiments on our simulation by varying the

parameter N which is the number of tetrahedrons that modelthe density of peg in our simulations. N is varied in our experiments and simulation is run to note the visualization

performance. Table 1 show the results based on the FiniteElement model of the peg. On the x-axis are the differentcollision models which include the line, triangle, spherical, lineand triangle combined, line and sphere combined, sphere and

triangle combined and finally sphere, triangle and linecombined. The colors denote the density of the mesh. Reddenotes the coarsest model while Orange denotes the densestmodel. On the y axis are the frames per second.

Table 1 Frames per second for FEM based Model

We can see a general trend in the results that in everycollision model the more dense the object mesh is, the lower the frame rate is, which is understandable since a more densemesh requires more calculations for the behavior model as well



[7] O. Comas et al, Efficient nonlinear FEM for soft tissue modeling and itsGPU implementation within the open source framework SOFA,Proceedings of the International Symposium on Computational Modelsfor Biomedical Simulations (ISBMS), 2008

[8] http://www.flsprogram.org

[9] http://www.surgical-science.com

[10] http://www.simbionix.com

[11] http://www.telemedicine.pk

[12] Herve Delingette, Toward Realistic Soft-Tissue Modeling in medical simulation, IEEE,Vol 86, No 3, March 1998

[13] http://smart.seecs.nust.edu.pk/tutorial.html

[14] Andrew Nealen, Matthias Muller, Richard Keiser, Eddy Boxerman andMark Carlson, Physically Based Deformable Models, Compyter Graphics forum, Volume 25, number 4 pp. 809-836, 2006

[15] Christer Ericson, Real-Time Collision Detection, Elsevier, ISBN 1-55860-732-3

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