Time-varying Image Data Visualization Framework for Application in Cardiac Catheterization Procedures

Ying Liang Ma, Kawal S. Rhode, Andy P. King, Gang Gao Phani Chinchapatnam, Tobias Schaeffter, Reza Razavi, Kurt Saetzler

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    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.
    LanguageEnglish
    Title of host publicationUnknown Host Publication
    Pages137-140
    Number of pages4
    DOIs
    Publication statusPublished - 2008
    EventTheory and Practice of Computer Graphics - Manchester
    Duration: 1 Jan 2008 → …

    Conference

    ConferenceTheory and Practice of Computer Graphics
    Period1/01/08 → …

    Fingerprint

    Data visualization
    Visualization
    Mesh generation
    Binary images
    Catheters
    Surgery
    Time series
    Blood
    Imaging techniques
    X rays
    Magnetic Resonance Imaging

    Cite this

    Ma, Y. L., Rhode, K. S., King, A. P., Phani Chinchapatnam, G. G., Schaeffter, T., Razavi, R., & Saetzler, K. (2008). Time-varying Image Data Visualization Framework for Application in Cardiac Catheterization Procedures. In Unknown Host Publication (pp. 137-140) https://doi.org/10.2312/LocalChapterEvents/TPCG/TPCG08/137-140
    Ma, Ying Liang ; Rhode, Kawal S. ; King, Andy P. ; Phani Chinchapatnam, Gang Gao ; Schaeffter, Tobias ; Razavi, Reza ; Saetzler, Kurt. / Time-varying Image Data Visualization Framework for Application in Cardiac Catheterization Procedures. Unknown Host Publication. 2008. pp. 137-140
    @inproceedings{1ad48bba8a634de5a09d54ea9c4019a1,
    title = "Time-varying Image Data Visualization Framework for Application in Cardiac Catheterization Procedures",
    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.",
    author = "Ma, {Ying Liang} and Rhode, {Kawal S.} and King, {Andy P.} and {Phani Chinchapatnam}, {Gang Gao} and Tobias Schaeffter and Reza Razavi and Kurt Saetzler",
    year = "2008",
    doi = "10.2312/LocalChapterEvents/TPCG/TPCG08/137-140",
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    Ma, YL, Rhode, KS, King, AP, Phani Chinchapatnam, GG, Schaeffter, T, Razavi, R & Saetzler, K 2008, Time-varying Image Data Visualization Framework for Application in Cardiac Catheterization Procedures. in Unknown Host Publication. pp. 137-140, Theory and Practice of Computer Graphics, 1/01/08. https://doi.org/10.2312/LocalChapterEvents/TPCG/TPCG08/137-140

    Time-varying Image Data Visualization Framework for Application in Cardiac Catheterization Procedures. / Ma, Ying Liang; Rhode, Kawal S.; King, Andy P.; Phani Chinchapatnam, Gang Gao; Schaeffter, Tobias; Razavi, Reza; Saetzler, Kurt.

    Unknown Host Publication. 2008. p. 137-140.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    AU - Rhode, Kawal S.

    AU - King, Andy P.

    AU - Phani Chinchapatnam, Gang Gao

    AU - Schaeffter, Tobias

    AU - Razavi, Reza

    AU - Saetzler, Kurt

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    AB - 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.

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    ER -

    Ma YL, Rhode KS, King AP, Phani Chinchapatnam GG, Schaeffter T, Razavi R et al. Time-varying Image Data Visualization Framework for Application in Cardiac Catheterization Procedures. In Unknown Host Publication. 2008. p. 137-140 https://doi.org/10.2312/LocalChapterEvents/TPCG/TPCG08/137-140