A Computer Model of Soft Tissue Interaction
with a Surgical Aspirator
Vincent Mora, Di Jiang, Rupert Brooks, and Se´bastien Delorme
Industrial Materials Institute - National Research Council, Canada
vincent.mora@imi-cnrc-nrc.gc.ca
Abstract. Surgical aspirators are one of the most frequently used neu-
rosurgical tools. Effective training on a neurosurgery simulator requires
a visually and haptically realistic rendering of surgical aspiration. How-
ever, there is little published data on mechanical interaction between soft
biological tissues and surgical aspirators. In this study an experimental
setup for measuring tissue response is described and results on calf brain
and a range of phantom materials are presented. Local graphical and
haptic models are proposed. They are simple enough for real-time appli-
cation, and closely match the observed tissue response. Tissue resection
(cutting) with suction is simulated using a volume sculpting approach. A
simulation of suction is presented as a demonstration of the effectiveness
of the approach.
1 Introduction
Rapidly evolving surgical techniques, patient safety concerns, and the inherent
inefficiency of operating room training are strongly driving the need for innova-
tive simulation technologies [1,2,3]. Clinical adoption of virtual reality simulation
would result in accelerated training, rapid adoption of new techniques, better
surgeries with minimal risk and consequently improved patient care [4]. Our re-
search project aims to develop a simulator capable of training medical students
to perform surgical resection of brain tumors.
As a fundamental surgical device in neurosurgery [5,6,7], the surgical aspirator
must be accurately modelled. This tool has two main functions: (A) aspiration,
which is either the non-traumatic removal of blood and fluid or the removal of
soft tissue [8,9], and (B) tissue holding [5]. Surgical aspirators are included in
commercial simulators but studies on their mechanical behavior are scarce in
the literature and do not provide enough experimental data to develop a model
suitable for a simulator [10,11,12,13,14,15,16,17].
A surgical aspirator model is suitable for a simulator if it allows to perform
functions (A) and (B) in real time [18]. The objective of this study is (1) to
measure the interaction between the surgical aspirator and brain tissue, (2) to
propose a haptic and graphic model of aspirator tissue interaction based on
experimental evidence, and (3) to implement an algorithm for tissue holding and
removal with a virtual aspirator. Fluid removal is out of the scope of this paper.
As a secondary objective potential phantom material were also experimentally
tested in order to find a substitute for brain tissue mechanical testing.
G.-Z. Yang et al. (Eds.): MICCAI 2009, Part I, LNCS 5761, pp. 51–58, 2009.
c
© NRC Canada 2009

52 V. Mora et al.
kPa
digital
microscope
vacuum
vent
movable platform
manometer
h=||u(0)||
a) b)
c)
vent cover
suction tube
(internal diameter = 1.19mm)
cu
r
Fig. 1. a) Overview of the experimental setup. b) Photograph taken by the digital
microscope before the sample detaches in order to determine the local deformation
(white dotted line). Note the ruler used for calibration on the upper right corner. c)
Photograph taken by the digital microscope just after the sample detached from the
suction tube.
2 Experimentation
One calf brain was collected at a slaughterhouse, transported in PBS (phosphate
buffered saline) and frozen at −80◦C. It was then thawed 24h prior to the ex-
periment. The tests were carried out on the surface of the calf brain covered
by the pia matter with and without moisturizing. The pia matter was removed
and the test with moisturizing was repeated. Two phantom materials have been
tested: dessert gelatine diluted in boiling water with mass concentration of 5%,
10% and 15%; and two different brands of soft tofu.
The experimental setup, shown in figure 1 a, consisted of a movable platform,
a digital microscope and a 1.19mm diameter suction tube connected to a vacuum
pump. The vacuum hose featured a vent with a sliding cover to vary the suction
pressure and a manometer capable of recording negative pressures between 4kPa
and 80kpa with a 1kPa precision at the tip of the tube. A ruler was used for
photogrammetric calibration, as shown on the upper right corner of figure 1 b
and c.
The experiment proceeded as follows:
1. The material sample was put on the movable platform (see figure 1 a).
2. The vent was totally opened.
3. The sample was raised until it contacted the suction tube. The pressure was
recorded when the sample completely blocked the tube oppening.
4. The platform was lowered and photographs were taken at 1mm intervals in
order to record the shape of the surface in the vicinity of the suction tube.

A Computer Model of Soft Tissue Interaction with a Surgical Aspirator 53
0
1
2
3
4
5
6
7
8
0 2 4 6 8 10 12
||u
|| [
mm
]
r [mm]
Jello 10%
0
1
2
3
4
5
6
7
8
0 2 4 6 8 10 12
||u
|| [
mm
]
r [mm]
Calf grey matter
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0 2 4 6 8 10
||f|
| [N
]
h [mm]
jello 5%
jello 10%
jello 15%
soft tofu SunRize
soft tofu Oriental
grey matter
grey matter moistened
grey matter membrane removed
Fig. 2. Left: profile for different value of h along with fits with the function ||u(r)|| =
h
2
/(h + αr3) (see eq. 2) with α
grey matter
= 0.07 and α
gelatine 10%
= 0.12. Right: Force
as a function of the vertical displacement h for different materials. The solid lines
are fits with the quadratic function βh2 (see eq. 1) with β
gelatine 5%
= 1.3 × 10−4,
β
gelatine 10%
= β
grey matter
= 1.2×10−3, β
gelatine 15%
= 5.6×10−3, β
tofu A
= 2.2×10−2
and β
tofu B
= 3.8 × 10−2.
5. When the sample detached from the suction tube:
(a) If the surface of the sample was undamaged, a digital photograph was
taken. The vent was then further obstructed to increase the negative
pressure at the tool tip, and the process went back to step 3.
(b) If the surface of the sample was damaged, the rupture pressure was
recorded and the experiment ended.
Profiles obtained for gelatine 10% and moisturized calf grey matter without pro-
tective membrane are shown in figure 2. The distance h between the surface of the
sample and the extremity of the suction tube was measured on the photographs
taken just after the sample detached from the tube (see figure1 c). Knowing the
internal diameter of the suction tube d, the pressure p, and assuming frictionless
contact between the tube and the sample, the magnitude of the force f when
the sample detaches from the tube is given by ||f || = 14πd
2p. The experimental
data obtained for various materials are shown in figure 2. It can be observed
that the calf grey matter has the same behavior as the gelatine 10% solution
with a lower rupture force (38mN ≡ 34kPa) that is further lowered by absence
of moisturizing (18mN ≡ 16 kPa) or the removal of the protective membrane
(9mN ≡ 8kPa).

A Computer Model of Soft Tissue Interaction with a Surgical Aspirator 55
Algorithm 1. Providing haptic and visual feedback
Input: Haptic device position
Result: Visual and haptic feedback
while simulation is not over do
calculate the deformation of the FE mesh and update postion of surface
mesh;
if grabbed vertices is empty then
grab surface mesh vertices in the vicinity of the tool tip t ;
else
compute P : average position and normal of grabbed vertices;
compute haptic force f = −βh2n ;
if ||f || < ||f ||
rupture
then
apply force f
n
to the FE mesh grabbed vertices ;
locally refine and deform graphic mesh with u(r) = h
2
h+αr
3
n ;
else
cut FE mesh and extract new surface mesh;
end
empty grabbed vertices list ;
grab surface mesh vertices in the vicinity of the projection c of t ;
end
end
where β is a material parameter that increases with the stiffness. This model
shows good agreement with experimental data in figure 2. In addition to being
rendered by the haptic device, the force can be applied to the 3D FE model to
calculate a deformation. When ||f || is smaller than the experimentally measured
value ||f ||rupture, tissue will be held by the aspirator. Beyond this critical value,
the tissue breaks, and is removed by the aspirator as described in the next
section.
3.2 Tissue Removal
To enable modelling of tissue cutting while avoiding large changes to the 3D FE
mesh a volume sculpting approach [21] is used. The boundaries of soft tissues are
modelled as the zero isosurface of a distance field, F (x), defined on the nodes of
the 3D FE mesh. Tissue removal is modelled by changing the value of F based
on the position of the surgical aspirator. A similar approach has been used to
simulate cutting of the petrous bone in [22].
If the aspiration force is greater or equal than ||f ||rupture, a spherical cut-
ting region around the tool tip, t, becomes active. Tissue within this sphere is
removed. To do this, the distance field is updated according to:
Fnew(x) = min (Fold(x), ||x− t|| − R) (1)
where R is the radius of the cutting sphere.
Once this function is changed, it is necessary to tesselate the new zero iso-
surface. This is done using one of a family of algorithms, which we refer to as

56 V. Mora et al.
Fig. 4. Simulation of suction effect on tissue using a local rendering model. Left: screen-
shot from simulation; Right: same showing refined mesh near the surgical aspirator.
marching shapes [23], which tesselate each element given the field values at their
vertices. The generated surface consist of triangles, whose vertices lie on the
edges of the FE mesh. The most widely known such algorithm is the marching
cubes of Lorensen and Cline [24], but the same approach can be used on other
shapes, such as tetrahedra, octahedra, etc.
3.3 Graphics Rendering
The displacement of a point x on the brain surface due to suction is modelled
by the function:
u(r) = h
2
h + αr3n (2)
where α is a material parameter, h is the distance between the tool tip t and
the local plane P , r is the distance between x and the projection c of t on P ,
and n is the normal to P (see figure 1 b). Figure 2 shows good agreement of the
model with the experimental data.
The triangle elements of the surface mesh that are close to the suction tool are
recursively subdivided into smaller triangles. All the nodes of the mesh are then
displaced according to the model. Figure 4 shows a screenshot of the simulated
interaction between surgical aspirator and brain tissue with a surface mesh that
is locally refined for graphics rendering. When cutting is taking place, the use
of this graphic model gives the appearance that the interior parts of the tissue
pops up into the aspiration tool.
4 Discussion
In this paper, we have described a model to simulate the effect of a surgical
aspirator on tissue. By separating the haptic and graphical models from the FE
model, the experimentally observed haptic and visual effects of aspirating tissue
were simulated in a computationally efficient manner.

A Computer Model of Soft Tissue Interaction with a Surgical Aspirator 57
h
h
x
0
x
1
x
n
x
0
x
1
t
c
c
t
a) b)
graphic mesh
finite element
mesh
deformed
Fig. 5. Definition of P and h when the 3D FE mesh is a) coarse, b) fine
The experiments with phantom material suggest that gelatine (10% mass
concentration) could be an adequate substitute for brain for mechanical testing
purposes. This preliminary result must be validated using fresh brain tissue
since freezing can alter the properties of biological tissues [25]. Other work [26]
has shown that these effects can be compensated by applying a corrective factor.
Thus we think it likely that with further work a suitable concentration of gelatine
can be found to mimic the properties of fresh brain.
The proposed model is limited by the dependency of the definition of the local
plane P on the refinement of the 3D FE mesh (see figure 5). In the experiment
P (figure 1) is the average plane of the undeformed brain surface. In the model
P (algorithm 1) is the average plane of the surface of the deformed 3D FE mesh.
If the 3D FE mesh is coarse, compared to the local deformation caused by the
aspirator, the local deformation of the 3D FE mesh is small and the model P
is close to the experimental P . However if the 3D FE mesh is fine, its local
deformation will cause the model P to diverge from the experimental P which
affects the calculation of f(h). This problem is not a real concern in our case
since if the finite element mesh can be made fine enough for this problem to be
significant, the mesh would then be fine enough to avoid the need for this local
model altogether.
Future work will focus on modelling the interaction of other neurosurgical
tools such as the Cavitron Ultrasonic Surgical Aspirator (CUSA), as well as on
validating the proposed model against a FE simulation results obtained with a
fine mesh, and against experimental data obtained on fresh human brain tissue.
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