Navigated bronchoscopy provides benefits for endoscopists and patients, but accurate tracking information is
needed. We present a novel real-time approach for bronchoscope tracking combining electromagnetic (EM)
tracking, airway segmentation, and a continuous model of output. We augment a previously published approach
by including segmentation information in the tracking optimization instead of image similarity. Thus, the new
approach is feasible in real-time. Since the true bronchoscope trajectory is continuous, the output is modeled
using splines and the control points are optimized with respect to displacement from EM tracking measurements
and spatial relation to segmented airways. Accuracy of the proposed method and its components is evaluated
on a ventilated porcine ex-vivo lung with respect to ground truth data acquired from a human expert. We
demonstrate the robustness of the output of the proposed method against added artificial noise in the input
data. Smoothness in terms of inter-frame distance is shown to remain below 2 mm, even when up to 5 mm of
Gaussian noise are added to the input. The approach is shown to be easily extensible to include other measures
like image similarity.
Despite the increasing adoption of other imaging modalities, ultrasound guidance is widely used for surgical
procedures and clinical imaging due to its low cost, non-invasiveness, and real-time visual feedback. Many
ultrasound-guided procedures require extensive training and where possible training on simulations should be
preferred over patients. Computational resources for existing approaches to ultrasound simulation are usually
limited by real-time requirements. Unlike previous approaches we simulate freehand ultrasound images from CT
data on the Graphics Processing Unit (GPU). We build upon the method proposed by Wein et al. for estimating
ultrasound reflection properties of tissue and modify it to a computationally more efficient form. In addition
to previous approaches, we also estimate ultrasound absorption properties from CT data. Using NVIDIA's
"Compute Unified Device Architecture" (CUDA), we provide a physically plausible simulation of ultrasound
reflection, shadowing artifacts, speckle noise and radial blurring. The same algorithm can be used for simulating
either linear or radial imaging, and all parameters of the simulated probe are interactively configurable at runtime,
including ultrasound frequency and intensity as well as field geometry. With current hardware we are able
to achieve an image width of up to 1023 pixels from raw CT data in real-time, without any pre-processing and
without any loss of information from the CT image other than from interpolation of the input data. Visual
comparison to real ultrasound images indicates satisfactory results.
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