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Signal averaging improves signal-to-noise in OCT images: But which approach works best, and when?

Bernhard BaumannConrad W MerkleRainer A LeitgebMarco AugustinAndreas WartakMichael PircherChristoph K Hitzenberger
Published in: Biomedical optics express (2019)
The high acquisition speed of state-of-the-art optical coherence tomography (OCT) enables massive signal-to-noise ratio (SNR) improvements by signal averaging. Here, we investigate the performance of two commonly used approaches for OCT signal averaging. We present the theoretical SNR performance of (a) computing the average of OCT magnitude data and (b) averaging the complex phasors, and substantiate our findings with simulations and experimentally acquired OCT data. We show that the achieved SNR performance strongly depends on both the SNR of the input signals and the number of averaged signals when the signal bias caused by the noise floor is not accounted for. Therefore we also explore the SNR for the two averaging approaches after correcting for the noise bias and, provided that the phases of the phasors are accurately aligned prior to averaging, then find that complex phasor averaging always leads to higher SNR than magnitude averaging.
Keyphrases
  • optical coherence tomography
  • diabetic retinopathy
  • air pollution
  • optic nerve
  • electronic health record
  • big data
  • deep learning
  • machine learning