Biomedical Engineering Reference
In-Depth Information
By applying these collision detection methods for hip 3D models, recently a
fast strategy for evaluating hip pathologies has been proposed. This method will be
covered in Sect.
8.3
. The strategy is suitable for real time medical hip simulations,
and allows differentiation between the subtypes of hip impingement. The method can
give some diagnostic information, especially in cases with combined impingement
where the major component has to be defined and treated [
13
,
15
].
For diagnosing some of the human joint diseases, it is important to obtain the joint's
range of motion. For example, loss of internal rotation in the hip is one of the first
signs of internal hip pathology and can be related to diagnoses, such as arthritis. Also,
increase in femoral or acetabular anteversion usually demonstrates an increase in the
internal rotation [
1
]. In Sect.
8.4
, a fast and accurate method for finding maximum
range of motion in joints will be explained. This joint range finder method works
based on the pre-processing stage of the cylindrical segmenting collision detection
method explained in Sect.
8.2.2
. However, the method is calculating the range of
motion without applying successive collision detection algorithms (vs. traditional
methods). In addition, it needs to be performed only once per simulation to find both
anti-clockwise and clockwise range of motion [
3
,
13
].
The orthopedic simulations are usually based on rotation and may need to have an
estimation of the joint center of rotation. Therefore, it is very important to know how
the results of a simulation depend on the joint center estimating method. In Sect.
8.5
an investigation on the sensitivity of the penetration depths of hip tissues to the
methods applied for estimating hip joint center of rotation (HJC) will be discussed.
Different centers of rotation calculated by five methods were applied during hip
movement of 10 patients. The virtual penetration depths were calculated by using
the collision detectionmethods explained in Sect.
8.2
. The results of this investigation
highlight the importance of the HJC estimation methods because of their influence
on computer-aided medical research and diagnosis [
13
,
16
].
8.2 Collision Detection for Rotating or Sliding Objects
8.2.1 Overview
In computer graphics, many methods have been proposed to speed up the processing
time for collision detection among virtual objects. Usually the methods developed
for collision detection are for either very general cases or very specific applications.
Arbabi et al. have proposed two newmethods for collision detection based on finding
the penetrating vertices (not edges) when the objects are rotating or sliding, which
can be used for a wide range of applications [
14
]. The methods take advantage of
the constraints imposed on the rotating/sliding objects in order to ignore unneces-
sary calculations of the general methods and speed up the processing. The main
strategy applied in these methods is spatial segmentation in the angular or radial
orientations. These kinds of segmentation help the methods to be more adjusted to
