Using Medical Image Reconstruction Methods for Denoising of OCTA Data

Husvogt L, Ploner S, Moult EM, Alibhai AY, Schottenhamml J, Duker JS, Waheed NK, Fujimoto JG, Maier A (2019)

Publication Language: English

Publication Type: Conference contribution, Abstract of a poster

Publication year: 2019

Pages Range: 3096

Conference Proceedings Title: Investigative Ophthalmology & Visual Science

Event location: Vancouver, B.C., Canada CA

URI: https://iovs.arvojournals.org/article.aspx?articleid=2746928&resultClick=1

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.

Authors with CRIS profile

Lennart Husvogt Lehrstuhl für Informatik 5 (Mustererkennung) Stefan Ploner Lehrstuhl für Informatik 5 (Mustererkennung) Julia Schottenhamml Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Related research project(s)

Joint Iterative Reconstruction and Motion Compensation for Optical Coherence Tomography Angiography (Joint Reco & MoCo for OCT(A)) Aug. 1, 2017 - July 31, 2019

Involved external institutions

Massachusetts Institute of Technology (MIT) US United States (USA) (US) Tufts Medical Center US United States (USA) (US)

How to cite

APA:

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.

MLA:

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.

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