April 2010
Volume 51, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2010
Voxel-Based Analysis of the Optic Radiation Using Diffusion Tensor Imaging in Glaucoma Patients
Author Affiliations & Notes
  • A. El-Rafei
    Pattern Recognition Lab - Department of Computer Science,
    University of Erlangen-Nuremberg, Erlangen, Germany
    Erlangen Graduate School in Advanced Optical Technologies (SAOT), Erlangen, Germany
  • T. Engelhorn
    Department of Neuroradiology,
    University of Erlangen-Nuremberg, Erlangen, Germany
  • S. Waerntges
    Department of Ophthalmology,
    University of Erlangen-Nuremberg, Erlangen, Germany
  • A. Doerfler
    Department of Neuroradiology,
    University of Erlangen-Nuremberg, Erlangen, Germany
  • J. Hornegger
    Pattern Recognition Lab - Department of Computer Science,
    University of Erlangen-Nuremberg, Erlangen, Germany
    Erlangen Graduate School in Advanced Optical Technologies (SAOT), Erlangen, Germany
  • G. Michelson
    Department of Ophthalmology,
    University of Erlangen-Nuremberg, Erlangen, Germany
    Erlangen Graduate School in Advanced Optical Technologies (SAOT), Erlangen, Germany
  • Footnotes
    Commercial Relationships  A. El-Rafei, None; T. Engelhorn, None; S. Waerntges, None; A. Doerfler, None; J. Hornegger, None; G. Michelson, None.
  • Footnotes
    Support  German Academic Exchange Service (DAAD), Erlangen Graduate School in Advanced Optical Technologies (SAOT)
Investigative Ophthalmology & Visual Science April 2010, Vol.51, 4413. doi:
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      A. El-Rafei, T. Engelhorn, S. Waerntges, A. Doerfler, J. Hornegger, G. Michelson; Voxel-Based Analysis of the Optic Radiation Using Diffusion Tensor Imaging in Glaucoma Patients. Invest. Ophthalmol. Vis. Sci. 2010;51(13):4413.

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

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Abstract
 
Purpose:
 

To establish a registration based framework for the determination of local changes of the optic radiation (OR) due to glaucoma using Diffusion Tensor Imaging (DTI)

 
Methods:
 

DTI-brain scans of 23 subjects using a 3 Tesla-MRI scanner were performed. The subjects are categorized into two age matched groups: 10 normal controls (mean age 56.9±11.9 years) and 13 primary open angle glaucoma patients (mean age 63±12.5 years).First, the OR is initially identified using the authors' already published algorithm. In the axial slice that includes the largest part of the lateral geniculate nucleus (LGN), there is a high degree of shape similarity of the OR. Therefore, it is selected for further analysis . The segmented OR is manually corrected by two DTI experts. Second, all the segmented ORs are registered to a reference OR, the largest size OR (right of the figure), using a shape based non-rigid registration approach. Third, the transformation fields of the registration are applied to the fractional anisotropy (FA) images to map them into the unified registration space. Then, the distributions of FA at each voxel in the unified space are statistically analyzed using Mann-Whitney U-test with regard to both groups. Finally, the significant voxels are determined (p-value < 0.05).

 
Results:
 

The significant regions are determined by applying the proposed analysis to the normal and glaucoma groups. A concentration of significant voxels is observed on the core of the OR-bundle especially on the right bundle as shown by red voxels in figure (left).

 
Conclusions:
 

The proposed analysis provides a framework to capture the significant local changes of the OR due to glaucoma. It can be extended to all tensor-derived parameters. Moreover, shape based registration is suitable for glaucoma as it avoids the dependence on tensor-derived parameters which are subject to changes in the presence of glaucoma.  

 
Keywords: image processing • imaging methods (CT, FA, ICG, MRI, OCT, RTA, SLO, ultrasound) • imaging/image analysis: non-clinical 
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