Novel approach for 3-D reconstruction of coronary arteries from two uncalibrated angiographic images

  • Authors:

  • Jian Yang;Yongtian Wang;Yue Liu;Songyuan Tang;Wufan Chen

  • Affiliations:

  • School of Optical Engineering, Beijing Institute of Technology, Beijing, China;School of Optical Engineering, Beijing Institute of Technology, Beijing, China;School of Optical Engineering, Beijing Institute of Technology, Beijing, China;School of Optical Engineering, Beijing Institute of Technology, Beijing, China;Key Lab for Medical Image Processing, Southern Medical University, Guangzhou, China

  • Venue:
  • IEEE Transactions on Image Processing
  • Year:
  • 2009

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Abstract

Three-dimensional reconstruction of vessels from digital X-ray angiographic images is a powerful technique that compensates for limitations in angiography. It can provide physicians with the ability to accurately inspect the complex arterial network and to quantitatively assess disease induced vascular alterations in three dimensions. In this paper, both the projection principle of single view angiography and mathematical modeling of two view angiographies are studied in detail. The movement of the table, which commonly occurs during clinical practice, complicates the reconstruction process. On the basis of the pinhole camera model and existing optimization methods, an algorithm is developed for 3-D reconstruction of coronary arteries from two uncalibrated monoplane angiographic images. A simple and effective perspective projection model is proposed for the 3-D reconstruction of coronary arteries. A nonlinear optimization method is employed for refinement of the 3-D structure of the vessel skeletons, which takes the influence of table movement into consideration. An accurate model is suggested for the calculation of contour points of the vascular surface, which fully utilizes the information in the two projections. In our experiments with phantom and patient angiograms, the vessel centerlines are reconstructed in 3-D space with a mean positional accuracy of 0.665 mm and with a mean back projection error of 0.259 mm. This shows that the algorithm put forward in this paper is very effective and robust.