9 May 2002 Determination of biplane geometry and centerline curvature in vascular imaging
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Abstract
Three-dimensional (3-D) vessel trees can provide useful visual and quantitative information during interventional procedures. To calculate the 3-D vasculature from biplane images, the transformation relating the imaging systems (i.e., the rotation matrix R and the translation vector t) must be determined. We have developed a technique to calculate these parameters, which requires only the identification of approximately corresponding vessel regions in the two images. Initial estimates of R and t are generated based on the gantry angles, and then refined using an optimization technique. The objective function to be minimized is determined as follows. For each endpoint of each vessel in the first image, an epipolar line in the second image is generated. The intersection points between these two epipolar lines and the corresponding vessel centerline in the second image are determined. The vessel arclength between these intersection points is calculated as a fraction of the entire vessel region length in the image. This procedure is repeated for every vessel in each image. The value of the objective function is calculated from the sum of these fractions, and is smallest when the total fractional arclength is greatest. The 3-D vasculature is obtained from the optimal R and t using triangulation, and vessel curvature is then determined. This technique was evaluated using simulated curves and vessel centerlines obtained from clinical images, and provided rotational, magnification and relative curvature errors of 1 degree(s), 1% and 14% respectively. Accurate 3-D and curvature measures may be useful in clinical decision making, such as in assessing vessel tortuousity and access, during interventional procedures.
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Daryl Nazareth, Kenneth R. Hoffmann, Alan Walczak, Jacek Dmochowski, Lee R. Guterman, Stephen Rudin, Daniel R. Bednarek, "Determination of biplane geometry and centerline curvature in vascular imaging", Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); doi: 10.1117/12.467237; https://doi.org/10.1117/12.467237
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