April 2010
Volume 51, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2010
Motion Artifact Correction in OCT Volume Scans Using Image Registration
Author Affiliations & Notes
  • M. Kraus
    Pattern Recognition Lab, University Erlangen-Nuremberg, Erlangen, Germany
  • M. A. Mayer
    Pattern Recognition Lab, University Erlangen-Nuremberg, Erlangen, Germany
    School of Advanced Optical Technologies (SAOT), University Erlangen-Nuremberg, Germany
  • R. Bock
    Pattern Recognition Lab, University Erlangen-Nuremberg, Erlangen, Germany
    School of Advanced Optical Technologies (SAOT), University Erlangen-Nuremberg, Germany
  • B. Potsaid
    Research Laboratory of Electronics and Dept of EECS, Massachusetts Inst of Technology, Cambridge, Massachusetts
  • V. Manjunath
    New England Eye Center, Boston, Massachusetts
  • J. S. Duker
    New England Eye Center, Boston, Massachusetts
  • J. Hornegger
    Pattern Recognition Lab, University Erlangen-Nuremberg, Erlangen, Germany
  • J. G. Fujimoto
    Research Laboratory of Electronics and Dept of EECS, Massachusetts Inst of Technology, Cambridge, Massachusetts
  • Footnotes
    Commercial Relationships  M. Kraus, None; M.A. Mayer, None; R. Bock, None; B. Potsaid, None; V. Manjunath, None; J.S. Duker, None; J. Hornegger, None; J.G. Fujimoto, Carl Zeiss Meditec, Inc., F; Optovue, Inc., F; Carl Zeiss Meditec, Inc., P.
  • Footnotes
    Support  NIH R01-EY011289-24, R01-EY013178-10, R01-EY019029-02, R01-EY013516-07, AFOSR FA9550-07-1-0101
Investigative Ophthalmology & Visual Science April 2010, Vol.51, 4405. doi:
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      M. Kraus, M. A. Mayer, R. Bock, B. Potsaid, V. Manjunath, J. S. Duker, J. Hornegger, J. G. Fujimoto; Motion Artifact Correction in OCT Volume Scans Using Image Registration. Invest. Ophthalmol. Vis. Sci. 2010;51(13):4405.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract
 
Purpose:
 

Artifacts resulting from eye motion during 3D-OCT volume scanning degrade image quality and are a source of reproducibility error in quantitative measurements. We present a pure software based correction method that corrects motion in all three dimensions. It uses two consecutive 3D-OCT volume scans with orthogonal fast scan axis directions.

 
Methods:
 

The two 3D-OCT data sets are registered in order to find the unknown eye motion. Registration is performed by optimizing a global objective function with two displacement fields and special regularization based on the time structure of the volume sampling process. To improve both speed and solution quality, a multi-resolution approach is used. After optimization, each volume is undistorted and a single merged volume is constructed as an adaptive weighted sum of both registration results.

 
Results:
 

Data sets of the macula and optic disc from normal subjects and patients with retinal diseases were registered. The algorithm is able to correct for motion in all three dimensions. It can handle both slow drifting as well as saccadic movement. Quantitative assessment of registration performance using mutual information and visual inspection indicate very good registration results. The resulting volumes do not show visible motion artifacts. The algorithm produces stable results over a wide range of parameters. Images show single corresponding en face slices, from left to right: Fast horizontal, fast vertical input, registered and merged result.  

 
Conclusions:
 

The proposed method constitutes a robust, software based, motion artifact correction solution able to remove distortion introduced by movement in all three dimensions.These methods promise to enable the acquisition of large 3D-OCT data sets and improve measurement reproducibility without the need for active eye tracking.

 
Keywords: image processing • imaging methods (CT, FA, ICG, MRI, OCT, RTA, SLO, ultrasound) 
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