This paper proposes a method for tracking a bronchoscope using a position sensor without fiducial markers.
Recently, a very small electromagnetic position sensor has become available that can be inserted into the bronchoscope's
working channel to obtain bronchoscope camera motion. In most tracking methods using position
sensors, registration is performed using the positions of fiducial markers attached to a patient's body. However,
these methods need to measure the positions of fiducial markers on both the actual patient's body and the
reference image, such as a CT image of the patient. Therefore, we propose a method for bronchoscope tracking
without fiducial markers that estimates a transformation matrix between the actual patient's body and the CT
image taken prior to bronchoscope examination. This estimation is performed by computing the correspondences
between the outputs of the position sensor and the bronchi regions extracted from the CT image. We applied the proposed method to a rubber bronchial model. Experimental results showed that average target registration error of the five bronchial branches was a minimum of about 3.0mm, and the proposed method tracked a bronchoscope camera in real time.
This paper investigates the utilization of the ultra-tiny electromagnetic tracker (UEMT) in a bronchoscope
navigation system. In a bronchoscope navigation system, it is important to track the tip of a bronchoscope or
catheter in real time. An ultra-tiny electromagnetic tracker (UEMT), which can be inserted into the working
channel of a bronchoscope, allows us to track the tip of a bronchoscope or a catheter in real time. However,
the accuracy of such UEMTs can be easily a.ected by ferromagnetic materials existing around the systems.
This research tries to utilize a method for obtaining a function that compensates the outputs of a UEMT in a
bronchoscope navigation system using a method proposed by Sato et al. This method uses a special jig combining
a UEMT and an optical tracker (OT). Prior to bronchoscope navigation, we sweep this jig around an examination
table and record outputs of both the UEMT and the OT. By using the outputs of the OT as reference data,
we calculate a higher-order polynomial that compensates the UEMT outputs. We applied this method to the
bronchoscope navigation system and performed bronchoscope navigation inside a bronchial phantom on the
examination table. The experimental results showed that this method can reduce the position sensing error from
53.2 mm to 3.5 mm on a conventional examination table. Also, by using compensated outputs, it was possible
to produce virtual bronchoscopic images synchronized with real bronchoscopic images.
This paper presents an easy and stable bronchoscope camera calibration technique for bronchoscope navigation
system. A bronchoscope navigation system is strongly expected to be developed to make bronchoscopic examinations
safer and more effective. In a bronchoscope navigation system, virtual bronchoscopic images are generated
from a 3D CT image taken prior to an examination to register a patient's body and his/her CT image. It is
absolutely indispensable to know correct intrinsic camera parameters such as focal length, aspect ratio, and the
projection center of the camera for the generation of virtual bronchoscopic images. In the case of a bronchoscope,
however, it is very complicated to obtain these camera parameters by calibration techniques applied to
conventional cameras, since a bronchoscope camera has heavy barrel-type lens distortion. Also image resolution
is quite low. Therefore, we propose an easy and stable bronchoscope camera calibration technique that does not
require any special devices. In this method, a planar calibration pattern is captured at many different angles
by moving the bronchoscope camera freely. Then we automatically detect feature points for camera calibration
from captured images. Finally, intrinsic camera parameters are estimated from these extracted feature points
by applying Zhang's calibration technique. We applied the proposed method to a conventional bronchoscope
camera. The experimental results showed that reprojection error using estimated camera parameters was about
0.7 pixels. Also stable estimation was achieved by the proposed method.