Signal Normalization Reduces Image Appearance Disparity Among Multiple Optical Coherence Tomography Devices

Chieh Li Chen, Hiroshi Ishikawa, Gadi Wollstein, Richard A. Bilonick, Larry Kagemann, Joel S Schuman

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Purpose
    To assess the effect of the previously reported optical coherence tomography (OCT) signal normalization method on reducing the discrepancies in image appearance among spectral-domain OCT (SD-OCT) devices.

    Methods
    Healthy eyes and eyes with various retinal pathologies were scanned at the macular region using similar volumetric scan patterns with at least two out of three SD-OCT devices at the same visit (Cirrus HD-OCT, Zeiss, Dublin, CA; RTVue, Optovue, Fremont, CA; and Spectralis, Heidelberg Engineering, Heidelberg, Germany). All the images were processed with the signal normalization. A set of images formed a questionnaire with 24 pairs of cross-sectional images from each eye with any combination of the three SD-OCT devices either both pre- or postsignal normalization. Observers were asked to evaluate the similarity of the two displayed images based on the image appearance. The effects on reducing the differences in image appearance before and after processing were analyzed.

    Results
    Twenty-nine researchers familiar with OCT images participated in the survey. Image similarity was significantly improved after signal normalization for all three combinations (P ≤ 0.009) as Cirrus and RTVue combination became the most similar pair, followed by Cirrus and Spectralis, and RTVue and Spectralis.

    Conclusions
    The signal normalization successfully minimized the disparities in the image appearance among multiple SD-OCT devices, allowing clinical interpretation and comparison of OCT images regardless of the device differences.

    Translational Relevance
    The signal normalization would enable direct OCT images comparisons without concerning about device differences and broaden OCT usage by enabling long-term follow-ups and data sharing.
    Original languageEnglish (US)
    JournalTranslational Vision Science and Technology
    DOIs
    StatePublished - 2017

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