This paper presents a hybrid camera tracking method that uses electromagnetic (EM) tracking and intensitybased
image registration and its evaluation on a dynamic motion phantom. As respiratory motion can significantly
affect rigid registration of the EM tracking and CT coordinate systems, a standard tracking approach
that initializes intensity-based image registration with absolute pose data acquired by EM tracking will fail
when the initial camera pose is too far from the actual pose. We here propose two new schemes to address this
problem. Both of these schemes intelligently combine absolute pose data from EM tracking with relative motion
data combined from EM tracking and intensity-based image registration. These schemes significantly improve
the overall camera tracking performance. We constructed a dynamic phantom simulating the respiratory motion
of the airways to evaluate these schemes. Our experimental results demonstrate that these schemes can
track a bronchoscope more accurately and robustly than our previously proposed method even when maximum
simulated respiratory motion reaches 24 mm.
This paper presents a method for accelerating bronchoscope tracking based on image registration by using the
GPU (Graphics Processing Unit). Parallel techniques for efficient utilization of CPU (Central Processing Unit)
and GPU in image registration are presented. Recently, a bronchoscope navigation system has been developed for
enabling a bronchoscopist to perform safe and efficient examination. In such system, it is indispensable to track
the motion of the bronchoscope camera at the tip of the bronchoscope in real time. We have previously developed
a method for tracking a bronchoscope by computing image similarities between real and virtual bronchoscopic
images. However, since image registration is quite time consuming, it is difficult to track the bronchoscope in real
time. This paper presents a method for accelerating the process of image registration by utilizing the GPU of the
graphics card and the CUDA (Compute Unified Device Architecture) architexture. In particular, we accelerate
two parts: (1) virtual bronchoscopic image generation by volume rendering and (2) image similarity calculation
between a real bronchoscopic image and virtual bronchoscopic images. Furthermore, to efficiently use the GPU,
we minimize (i) the amount of data transfer between CPU and GPU, and (ii) the number of GPU function calls
from the CPU. We applied the proposed method to bronchoscopic videos of 10 patients and their corresponding
CT data sets. The experimental results showed that the proposed method can track a bronchoscope at 15 frames
per second and 5.17 times faster than the same method only using the CPU.
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