Controlling for Artifacts in Widefield Optical Coherence Tomography Angiography Measurements of Non-Perfusion Area.
Lucas R De PrettoEric M MoultA Yasin AlibhaiOscar M Carrasco-ZevallosSiyu ChenByungKun LeeAndre J WitkinCaroline R BaumalElias ReichelAnderson Zanardi de FreitasJay S DukerNadia K WaheedJames G FujimotoPublished in: Scientific reports (2019)
The recent clinical adoption of optical coherence tomography (OCT) angiography (OCTA) has enabled non-invasive, volumetric visualization of ocular vasculature at micron-scale resolutions. Initially limited to 3 mm × 3 mm and 6 mm × 6 mm fields-of-view (FOV), commercial OCTA systems now offer 12 mm × 12 mm, or larger, imaging fields. While larger FOVs promise a more complete visualization of retinal disease, they also introduce new challenges to the accurate and reliable interpretation of OCTA data. In particular, because of vignetting, wide-field imaging increases occurrence of low-OCT-signal artifacts, which leads to thresholding and/or segmentation artifacts, complicating OCTA analysis. This study presents theoretical and case-based descriptions of the causes and effects of low-OCT-signal artifacts. Through these descriptions, we demonstrate that OCTA data interpretation can be ambiguous if performed without consulting corresponding OCT data. Furthermore, using wide-field non-perfusion analysis in diabetic retinopathy as a model widefield OCTA usage-case, we show how qualitative and quantitative analysis can be confounded by low-OCT-signal artifacts. Based on these results, we suggest methods and best-practices for preventing and managing low-OCT-signal artifacts, thereby reducing errors in OCTA quantitative analysis of non-perfusion and improving reproducibility. These methods promise to be especially important for longitudinal studies detecting progression and response to therapy.
Keyphrases
- optical coherence tomography
- diabetic retinopathy
- optic nerve
- high resolution
- electronic health record
- image quality
- big data
- cone beam
- primary care
- healthcare
- risk assessment
- data analysis
- emergency department
- computed tomography
- machine learning
- cross sectional
- magnetic resonance
- fluorescence imaging
- bone marrow
- mass spectrometry
- quality improvement
- photodynamic therapy
- convolutional neural network