Abstract
BackgroundTo evaluate artifacts in macular ganglion cell inner plexiform layer (GCIPL) thickness measurement in eyes with retinal pathology using spectral-domain optical coherence tomography (SD OCT).MethodsRetrospective analysis of color-coded maps, infrared images and 128 horizontal B-scans (acquired in the macular ganglion cell inner plexiform layer scans), using the Cirrus HD-OCT (Carl Zeiss Meditec, Dublin, CA). The study population included 105 eyes with various macular conditions compared to 30 eyes of 30 age-matched healthy volunteers. The overall frequency of image artifacts and the relative frequency of artifacts were stratified by macular disease.ResultsScan errors and artifacts were found in 55.1% of the 13,440 B-scans in eyes with macular pathology and 26.8% of the 3840 scans in normal eyes. Segmentation errors were the most common scan error in both groups, with more common involvement of both segmentation borders in diseased eyes and anterior segmentation border in normal eyes.ConclusionSegmentation errors and artifacts in SD OCT GCA are common in conditions involving the macula. These findings should be considered when assessing macular GCIPL thickness and careful assessment of scans is suggested.
Highlights
To evaluate artifacts in macular ganglion cell inner plexiform layer (GCIPL) thickness measurement in eyes with retinal pathology using spectral-domain optical coherence tomography (SD OCT)
Overall frequency of scan line artifacts A total of 13,440 scans from 105 OCT macular cube scans of 105 eyes were reviewed
Artifacts were mostly observed in B scans of patients with retinitis pigmentosa (RP) 95.5% followed by neovascular age-related macular degeneration (AMD) 64.47% and least in patients with epiretinal membrane (ERM) 19.7%
Summary
To evaluate artifacts in macular ganglion cell inner plexiform layer (GCIPL) thickness measurement in eyes with retinal pathology using spectral-domain optical coherence tomography (SD OCT). The onboard Cirrus® OCT GCA algorithm mode uses segmentation software for automated detection of the outer boundary of the retinal nerve fiber layer (RNFL) and the outer boundary of the inner plexiform layer (IPL) and provides measurements of ganglion cell inner plexiform layer (GCIPL) thickness, thereby allowing in vivo quantitative measurement of inner retinal layer thickness It has emerged as a useful method for research and clinical practice, with applications spanning the evaluation of glaucomatous damage and progression, to indicating retinal neurodegeneration and being a biomarker for structural changes overtime [4,5,6,7,8]. It is beneficial to identify those scenarios in which artifacts and errors may occur, endangering accurate quantification and diagnosis in patients with conditions that impact the architecture of the macula
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