Using Medical Image Reconstruction Methods for Denoising of OCTA Data

Conference contribution
(Abstract of a poster)

Publication Details

Author(s): Husvogt L, Ploner S, Moult EM, Alibhai AY, Schottenhamml J, Duker JS, Waheed NK, Fujimoto JG, Maier A
Publication year: 2019
Conference Proceedings Title: Investigative Ophthalmology & Visual Science
Pages range: 3096
ISSN: 1552-5783
Language: English


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.

FAU Authors / FAU Editors

Husvogt, Lennart
Ploner, Stefan
Lehrstuhl für Informatik 5 (Mustererkennung)
Maier, Andreas Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)
Lehrstuhl für Informatik 5 (Mustererkennung)
Schottenhamml, Julia
Lehrstuhl für Informatik 5 (Mustererkennung)

External institutions with authors

Massachusetts Institute of Technology (MIT)
Tufts Medical Center

How to cite

Husvogt, L., Ploner, S., Moult, E.M., Alibhai, A.Y., Schottenhamml, J., Duker, J.S.,... Maier, A. (2019, July). Using Medical Image Reconstruction Methods for Denoising of OCTA Data. Poster presentation at ARVO Annual Meeting 2019, Vancouver, B.C., Canada, CA.

Husvogt, Lennart, et al. "Using Medical Image Reconstruction Methods for Denoising of OCTA Data." Presented at ARVO Annual Meeting 2019, Vancouver, B.C., Canada 2019.


Last updated on 2019-14-08 at 18:23