Enhanced Visualization of Subtle Outer Retinal Pathology by En Face Optical Coherence Tomography and Correlation with Multi-Modal Imaging

PLoS One. 2016 Dec 13;11(12):e0168275. doi: 10.1371/journal.pone.0168275. eCollection 2016.

Abstract

Purpose: To present en face optical coherence tomography (OCT) images generated by graph-search theory algorithm-based custom software and examine correlation with other imaging modalities.

Methods: En face OCT images derived from high density OCT volumetric scans of 3 healthy subjects and 4 patients using a custom algorithm (graph-search theory) and commercial software (Heidelberg Eye Explorer software (Heidelberg Engineering)) were compared and correlated with near infrared reflectance, fundus autofluorescence, adaptive optics flood-illumination ophthalmoscopy (AO-FIO) and microperimetry.

Results: Commercial software was unable to generate accurate en face OCT images in eyes with retinal pigment epithelium (RPE) pathology due to segmentation error at the level of Bruch's membrane (BM). Accurate segmentation of the basal RPE and BM was achieved using custom software. The en face OCT images from eyes with isolated interdigitation or ellipsoid zone pathology were of similar quality between custom software and Heidelberg Eye Explorer software in the absence of any other significant outer retinal pathology. En face OCT images demonstrated angioid streaks, lesions of acute macular neuroretinopathy, hydroxychloroquine toxicity and Bietti crystalline deposits that correlated with other imaging modalities.

Conclusions: Graph-search theory algorithm helps to overcome the limitations of outer retinal segmentation inaccuracies in commercial software. En face OCT images can provide detailed topography of the reflectivity within a specific layer of the retina which correlates with other forms of fundus imaging. Our results highlight the need for standardization of image reflectivity to facilitate quantification of en face OCT images and longitudinal analysis.

MeSH terms

  • Adult
  • Algorithms
  • Bruch Membrane / pathology
  • Female
  • Fluorescein Angiography
  • Healthy Volunteers
  • Humans
  • Image Enhancement / methods*
  • Image Processing, Computer-Assisted
  • Middle Aged
  • Multimodal Imaging*
  • Ophthalmoscopy
  • Optics and Photonics
  • Retina / diagnostic imaging*
  • Retina / pathology
  • Retinal Pigment Epithelium / diagnostic imaging*
  • Retinal Pigment Epithelium / pathology
  • Software
  • Spectroscopy, Near-Infrared
  • Tomography, Optical Coherence*

Grants and funding

This study was supported by the Ophthalmic Research Institute of Australia (DMS, FKC); Global Ophthalmology Award Program Bayer (FKC); National Health & Medical Research Council (Early Career Fellowship; APP1054712, FKC; Equipment Grant: 37812900, FKC); Retina Australia (FKC, JDR, TL, TM); and Health Department of Western Australia (FKC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.