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Multiresolution Image Registration in Digital X-Ray Angiography with Intensity Variation Modeling

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Abstract

Digital subtraction angiography (DSA) is a widely used technique for visualization of vessel anatomy in diagnosis and treatment. However, due to unavoidable patient motions, both externally and internally, the subtracted angiography images often suffer from motion artifacts that adversely affect the quality of the medical diagnosis. To cope with this problem and improve the quality of DSA images, registration algorithms are often employed before subtraction. In this paper, a novel elastic registration algorithm for registration of digital X-ray angiography images, particularly for the coronary location, is proposed. This algorithm includes a multiresolution search strategy in which a global transformation is calculated iteratively based on local search in coarse and fine sub-image blocks. The local searches are accomplished in a differential multiscale framework which allows us to capture both large and small scale transformations. The local registration transformation also explicitly accounts for local variations in the image intensities which incorporated into our model as a change of local contrast and brightness. These local transformations are then smoothly interpolated using thin-plate spline interpolation function to obtain the global model. Experimental results with several clinical datasets demonstrate the effectiveness of our algorithm in motion artifact reduction.

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Acknowledgments

The authors would like to thank Dr. Hashemi of Sina Heart Hospital in Isfahan, Iran, for providing us the datasets used in the experiments.

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Authors and Affiliations

  1. Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, 517, Najafabad, Isfahan, Iran

    Mansour Nejati & Hossein Pourghassem

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  1. Mansour Nejati
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  2. Hossein Pourghassem
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Correspondence to Hossein Pourghassem.

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Nejati, M., Pourghassem, H. Multiresolution Image Registration in Digital X-Ray Angiography with Intensity Variation Modeling. J Med Syst 38, 10 (2014). https://doi.org/10.1007/s10916-014-0010-8

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