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Model-Based Motion Artifact Correction in Digital Subtraction Angiography Using Optical-Flow

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Part of the book series: Informatik aktuell ((INFORMAT))

Zusammenfassung

Digital subtraction angiography is an important method for obtaining an accurate visualization of contrast-enhanced blood vessels. The technique involves the digital subtraction of two X-ray images, one with contrast filled vessels (fill image) and one without (mask image). Unfortunately, artifacts that are introduced due to the subtraction of misaligned mask and fill images may potentially degrade the diagnostic value of an image. The techniques used for correcting such artifacts involve the use of affine image registration techniques for aligning the mask and fill images and image processing techniques for suppressing the artifacts. Although affine registration techniques often yield acceptable results, they may fail when the imaged object undergoes 3D transformations. The techniques used for suppressing artifacts may cause blurring, when a projection image can no longer be corrected using a globally uniform motion model. In this paper, we have introduced an optical-ow based local motion compensation approach, where pixel-wise deformation fields are computed based on an X-ray imaging model. A visual inspection of the results shows a significant improvement in the image quality due to a reduction in the artifacts caused by misregistrations.

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Literatur

  1. Buzug TM, Weese J. Image registration for DSA quality enhancement. Comput Med Imaging Graph. 1998;22(2):103-113.

    Article  Google Scholar 

  2. Meijering EH, Zuiderveld KJ, Viergever MA. Image registration for digital subtraction angiography. Int J Comput Vis. 1999;31(2-3):227-246.

    Google Scholar 

  3. Bentoutou Y, Taleb N, El Mezouar MC, et al. An invariant approach for image registration in digital subtraction angiography. Pattern Recognit. 2002;35(12):2853-2865.

    Article  Google Scholar 

  4. Deuerling-Zheng Y, Lell M, Galant A, et al. Motion compensation in digital subtraction angiography using graphics hardware. Comput Med Imaging Graph. 2006;30(5):279-289.

    Article  Google Scholar 

  5. Ionasec RI, Heigl B, Hornegger J. Acquisition-related motion compensation for digital subtraction angiography. Comput Med Imaging Graph. 2009;33(4):256-266.

    Article  Google Scholar 

  6. Hariharan SG, Strobel N, Kaethner C, et al. A photon recycling approach to the denoising of ultra-low dose X-ray sequences. Int J Comput Assist Radiol Surg. 2018;13(6):847-854.

    Article  Google Scholar 

  7. Hariharan SG, Strobel N, Kowarschik M, et al. Simulation of realistic low dose fluoroscopic images from their high dose counterparts. Procs BVM. 2018; p. 80-85.

    Google Scholar 

  8. Starck JL, Murtagh FD, Bijaoui A. Image Processing and Data Analysis: the Multiscale Approach. Cambridge University Press; 1998.

    Google Scholar 

  9. Liu C. Beyond pixels: exploring new representations and applications for motion analysis. Doctoral Thesis. 2009;.

    Google Scholar 

  10. Makitalo M, Foi A. Optimal inversion of the generalized anscombe transformation for poisson-Gaussian noise. IEEE Trans Image Process. 2013;22(1):91-103.

    Article  MathSciNet  Google Scholar 

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Correspondence to Sai Gokul Hariharan .

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© 2019 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature

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Hariharan, S.G. et al. (2019). Model-Based Motion Artifact Correction in Digital Subtraction Angiography Using Optical-Flow. In: Handels, H., Deserno, T., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2019. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-25326-4_31

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