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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Literatur
Buzug TM, Weese J. Image registration for DSA quality enhancement. Comput Med Imaging Graph. 1998;22(2):103-113.
Meijering EH, Zuiderveld KJ, Viergever MA. Image registration for digital subtraction angiography. Int J Comput Vis. 1999;31(2-3):227-246.
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.
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.
Ionasec RI, Heigl B, Hornegger J. Acquisition-related motion compensation for digital subtraction angiography. Comput Med Imaging Graph. 2009;33(4):256-266.
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.
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.
Starck JL, Murtagh FD, Bijaoui A. Image Processing and Data Analysis: the Multiscale Approach. Cambridge University Press; 1998.
Liu C. Beyond pixels: exploring new representations and applications for motion analysis. Doctoral Thesis. 2009;.
Makitalo M, Foi A. Optimal inversion of the generalized anscombe transformation for poisson-Gaussian noise. IEEE Trans Image Process. 2013;22(1):91-103.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-658-25326-4_31
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-25325-7
Online ISBN: 978-3-658-25326-4
eBook Packages: Computer Science and Engineering (German Language)