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Ma, Y.L. 
ORCID: https://orcid.org/0000-0001-5770-5843, Rhode, K. S., King, A. P., Gao, G., Chinchapatnam, P., Schaeffter, T., Razavi, R. and Saetzler, K.
(2008)
Time-varying image data visualization framework for application in cardiac catheterization procedures.
In:
Theory and Practice of Computer Graphics 2008, TPCG 2008 - Eurographics UK Chapter Proceedings.
UNSPECIFIED, pp. 137-140.
Abstract
Visualization plays an important role in image guided surgery. This paper presents a real-time 3D motion visualization method where pre-computed meshes of the beating heart are synchronized with and overlaid onto live X-ray images. This provides the surgeon with a navigational aid in guiding catheters during cardiac catheterization. In order to generate time-varying meshes of the beating heart, we first acquire a time-series of images of the patient using Magnetic Resonance Imaging (MRI). The MRI heart images used for the cardiac catheterization procedures can either be contrast-enhanced by injecting a contrast agent prior to imaging or they can be unenhanced. The contrast-enhanced images can easily be segmented and binarized using a fixed grey-level threshold. In this case, we can use an adaptive Delaunay-based surface extraction algorithm for mesh generation, for which specifically developed for noisy binary image data sets. For unenhanced images, we have to choose a semi-automated segmentation approach, where a region of interest in the patient's heart is outlined manually in an intermediate slice in the 3-D MRI data set and then propagated to neighbouring slices. In a next step, the extracted snake contours are propagated in time from the first phase of the cardiac cycle to subsequent phases using multiple snake contours. In this scenario, the final mesh is generated using a serial section reconstruction algorithm. However, due to the nature of the underlyling MRI images which frequently contain areas of inhomogenous contrast caused by motion and blood flow, it is difficult to generate a smooth mesh directly from the result of the previously described semi-automatic segmentation procedure. Therefore, we also introduce a contour-based mesh smoothing algorithm using a 1D Gaussian filter in order to post-process the snake contours along the series of cross-sections before reconstruction.
| Item Type: | Book Section |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 06 Jan 2023 11:32 |
| Last Modified: | 10 Dec 2024 01:12 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/90448 |
| DOI: |
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