Investigative Ophthalmology & Visual Science Cover Image for Volume 64, Issue 8
June 2023
Volume 64, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2023
Noise-free one-cardiac-cycle OCT videos for local assessment of retinal tissue deformation
Author Affiliations & Notes
  • Emmanuelle Richer
    Hopital Maisonneuve-Rosemont Centre de Recherche, Montreal, Quebec, Canada
    Biomedical engineering, Polytechnique Montreal, Montreal, Quebec, Canada
  • Marisse Masis
    Hopital Maisonneuve-Rosemont Centre de Recherche, Montreal, Quebec, Canada
    Department of Ophtalmology, Universite de Montreal Faculte de Medecine, Montreal, Quebec, Canada
  • Farida Cheriet
    Biomedical engineering, Polytechnique Montreal, Montreal, Quebec, Canada
  • Mark Lesk
    Hopital Maisonneuve-Rosemont Centre de Recherche, Montreal, Quebec, Canada
    Department of Ophtalmology, Universite de Montreal Faculte de Medecine, Montreal, Quebec, Canada
  • Santiago Costantino
    Hopital Maisonneuve-Rosemont Centre de Recherche, Montreal, Quebec, Canada
    Department of Ophtalmology, Universite de Montreal Faculte de Medecine, Montreal, Quebec, Canada
  • Footnotes
    Commercial Relationships   Emmanuelle Richer None; Marisse Masis None; Farida Cheriet None; Mark Lesk None; Santiago Costantino None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2023, Vol.64, 2367. doi:
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    • Get Citation

      Emmanuelle Richer, Marisse Masis, Farida Cheriet, Mark Lesk, Santiago Costantino; Noise-free one-cardiac-cycle OCT videos for local assessment of retinal tissue deformation. Invest. Ophthalmol. Vis. Sci. 2023;64(8):2367.

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

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Abstract

Purpose : The measurement and analysis of retinal tissue biomechanics is important for understanding the pathophysiology of glaucoma. The dynamics of anatomical changes in the eye can uncover information that is not available in static Optical Coherence Tomography (OCT) images. However, noise in OCT images hampers the detailed and accurate analysis of retinal tissue movement. Here, we propose a new stabilizing and denoising method to improve the quality of retinal OCT videos, which exploits temporal redundancy.

Methods : After spatial registration of the images, synchronization of the OCT acquisition with the measurement of the patient's pulse allows phase-wrapping each measurement using the heart frequency to average the frames of the video that correspond to the same instant in the cardiac cycle. This transforms a multi-cycle video into a single one-heart-cycle movie with reduced noise and enhanced visual clarity. A summary of this workflow is represented in figure 1.

Results : Using the proposed workflow on the OCT videos of 15 patients (30 seconds at 100Hz), the CNR, SNR and the Mattes Mutual Information (MMI), which measure signal quality and registration accuracy, all increased significantly (Wilcoxon test, p ≤ 0.005). The CNR and SNR were computed in the RPE, the choroid and the RNFL, and showed a fold-change increase of respectively 1.70, 2.01 and 1.70 for the CNR and 1.63, 1.82 and 1.61 for the SNR. The average MMI showed a fold-change improvement of 3.51. Figure 2 illustrates an example of the application of our workflow on one subject.

Conclusions : Videos of minimized noise allow local tissue displacement to be seen more clearly and analyzed more precisely than with standard image registration methods. Apart from the pulse signal hardware, all the necessary equipment is present in most ophthalmology clinics. As such, this technique has great potential for use in the clinical setting.

This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.

 

A: A temporal pulse signal, synchronized with the OCT acquisition, is used to separate all frames in the movie according to their position in the heart cycle. B: A-scan registration (*) is done on every bin separately. The registered frames are then averaged to create denoised images.

A: A temporal pulse signal, synchronized with the OCT acquisition, is used to separate all frames in the movie according to their position in the heart cycle. B: A-scan registration (*) is done on every bin separately. The registered frames are then averaged to create denoised images.

 

A: B-scan before treatment. The RPE, RNFL and the choroid, as well as the noise ROI used for the computation of the CNR and SNR, are indicated. B: B-scan on the same subject after our workflow.

A: B-scan before treatment. The RPE, RNFL and the choroid, as well as the noise ROI used for the computation of the CNR and SNR, are indicated. B: B-scan on the same subject after our workflow.

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