July 2019
Volume 60, Issue 9
Free
ARVO Annual Meeting Abstract  |   July 2019
Using Medical Image Reconstruction Methods for Denoising of OCTA Data
Author Affiliations & Notes
  • Lennart Husvogt
    Pattern Recognition Lab, Friedrich-Alexander University Erlangen-Nuremberg, Germany
  • Stefan Ploner
    Pattern Recognition Lab, Friedrich-Alexander University Erlangen-Nuremberg, Germany
  • Eric M Moult
    Massachusetts Institute of Technology, Somerville, Massachusetts, United States
  • A. Yasin Alibhai
    New England Eye Center, Tufts Medical Center, Boston, Massachusetts, United States
  • Julia Schottenhamml
    Pattern Recognition Lab, Friedrich-Alexander University Erlangen-Nuremberg, Germany
  • Jay S Duker
    New England Eye Center, Tufts Medical Center, Boston, Massachusetts, United States
  • Nadia K Waheed
    New England Eye Center, Tufts Medical Center, Boston, Massachusetts, United States
  • James G Fujimoto
    Massachusetts Institute of Technology, Somerville, Massachusetts, United States
  • Andreas K Maier
    Pattern Recognition Lab, Friedrich-Alexander University Erlangen-Nuremberg, Germany
  • Footnotes
    Commercial Relationships   Lennart Husvogt, None; Stefan Ploner, None; Eric Moult, IP related to VISTA (P); A. Yasin Alibhai, None; Julia Schottenhamml, None; Jay Duker, Carl Zeiss Meditec (F), Carl Zeiss Meditec (C), Optovue (F), Optovue (C), Topcon (F), Topcon (C); Nadia Waheed, Carl Zeiss Meditec (F), Heidelberg (F), MVRF (F), Nidek (F), Optovue (C), Topcon (F); James Fujimoto, AFOSR (F), Carl Zeiss Meditec (P), IP related to VISTA (P), NIH (F), Optovue (P), Optovue (I), Optovue (C), Topcon (F); Andreas Maier, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 3096. doi:https://doi.org/
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    • Get Citation

      Lennart Husvogt, Stefan Ploner, Eric M Moult, A. Yasin Alibhai, Julia Schottenhamml, Jay S Duker, Nadia K Waheed, James G Fujimoto, Andreas K Maier; Using Medical Image Reconstruction Methods for Denoising of OCTA Data. Invest. Ophthalmol. Vis. Sci. 2019;60(9):3096. doi: https://doi.org/.

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

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Abstract

Purpose : A commonly used method to generate OCT angiography (OCTA) data is to compute the amplitude decorrelation of repeated B-scans. Despite its prevalence, to our knowledge, amplitude decorrelation, and related metrics were developed heuristically, and lack complete theoretical descriptions. Outside of OCTA, a variety of compressed sensing-based image reconstruction algorithms have been successfully applied to magnetic resonance imaging and computed tomography. Inspired by the work in these fields, we developed a probabilistic model for amplitude decorrelation. This, and models for speckle variance and interframe variance, enable an objective-function minimization approach to OCTA data generation with optimized noise characteristics.

Methods : We generated ground-truth images by registering and merging 10 consecutively acquired 3x3mm OCTA volumes from a healthy volunteer. A compressed-sensing-based denoising method with a 3D median filter for regularization was used for reconstruction.

Results : Figure 1 shows the decreasing mean squared error, compared to our ground-truth data, of our reconstruction algorithm over 100 iterations, indicating increasingly improved noise characteristics. Figure 2 shows corresponding representative en face retinal OCTA images from the reconstruction; ground truth data are shown in panel A, and test data in panel B. Compared to median filtering (panel C), our OCTA reconstruction decreases noise while minimizing image blurring. Reconstruction results in panels D through F show how the reconstruction can be used to optimize the denoising between the original volume and the median-filtered volume.

Conclusions : State-of-the-art reconstruction techniques, such as compressed sensing, can be adopted from other medical imaging fields to improve the quality of OCTA data.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

 

The mean squared error between the ground truth and the reconstruction result is shown in blue. The mean squared error between the ground truth and the input volume after applying a median filter is shown in green.

The mean squared error between the ground truth and the reconstruction result is shown in blue. The mean squared error between the ground truth and the input volume after applying a median filter is shown in green.

 

3x3mm en face speckle variance projections of a 28 year old normal person. From top left to lower right: a: ground truth data b: input data before reconstruction, c: input data after median filtering, d: reconstruction after 10 iterations, e: 20 iterations, d: 30 iterations

3x3mm en face speckle variance projections of a 28 year old normal person. From top left to lower right: a: ground truth data b: input data before reconstruction, c: input data after median filtering, d: reconstruction after 10 iterations, e: 20 iterations, d: 30 iterations

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