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. 2013:2013:236315.
doi: 10.1155/2013/236315. Epub 2013 Jul 22.

Nonrigid image registration in digital subtraction angiography using multilevel B-spline

Mansour Nejati  1 , Saeid Sadri, Rassoul Amirfattahi
Affiliations

Nonrigid image registration in digital subtraction angiography using multilevel B-spline

Mansour Nejati et al. Biomed Res Int. 2013.

Abstract

We address the problem of motion artifact reduction in digital subtraction angiography (DSA) using image registration techniques. Most of registration algorithms proposed for application in DSA, have been designed for peripheral and cerebral angiography images in which we mainly deal with global rigid motions. These algorithms did not yield good results when applied to coronary angiography images because of complex nonrigid motions that exist in this type of angiography images. Multiresolution and iterative algorithms are proposed to cope with this problem, but these algorithms are associated with high computational cost which makes them not acceptable for real-time clinical applications. In this paper we propose a nonrigid image registration algorithm for coronary angiography images that is significantly faster than multiresolution and iterative blocking methods and outperforms competing algorithms evaluated on the same data sets. This algorithm is based on a sparse set of matched feature point pairs and the elastic registration is performed by means of multilevel B-spline image warping. Experimental results with several clinical data sets demonstrate the effectiveness of our approach.

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Figures

Figure 1
Figure 1
Example of control points selection. (a) A live image from a 512 × 512 × 40 coronary angiographic image sequence. (b) Selected control points (black dots) by using the proposed feature-based approach.
Figure 2
Figure 2
Example of mask image generation from a live image by using the proposed approach. (a) A live image from a coronary angiographic image sequence. (b) Result of vessel segmentation. (c) Generated mask image.
Figure 3
Figure 3
Evaluation of different similarity measure based on RMS error obtained in determination of corresponding control points between live and mask images.
Figure 4
Figure 4
An elastic image warping example using multilevel B-spline interpolation. (a) Original image. (b) Warped image. (c) Warped test grid that shows the behavior of warp function.
Figure 5
Figure 5
Reduction of gray-level distortion artifacts. (a) A DSA image resulted after registration of related mask and live images. Due to gray-level distortion, the background in this image is not entirely homogeneous. (b) Result of gray-level distortion artifacts reduction after application of the proposed approach.
Figure 6
Figure 6
Application of the proposed method for the registration of coronary angiographic images. Rows from top to bottom correspond to mask images, live images, the original subtraction images (DSA images before registration), and the subtraction images after correction for motion artifacts, using the proposed registration technique.
See this image and copyright information in PMC

References

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