  Purpose: To develop a deep learning approach to de-noise optical coherence
tomography (OCT) B-scans of the optic nerve head (ONH).
  Methods: Volume scans consisting of 97 horizontal B-scans were acquired
through the center of the ONH using a commercial OCT device (Spectralis) for
both eyes of 20 subjects. For each eye, single-frame (without signal
averaging), and multi-frame (75x signal averaging) volume scans were obtained.
A custom deep learning network was then designed and trained with 2,328 "clean
B-scans" (multi-frame B-scans), and their corresponding "noisy B-scans" (clean
B-scans + gaussian noise) to de-noise the single-frame B-scans. The performance
of the de-noising algorithm was assessed qualitatively, and quantitatively on
1,552 B-scans using the signal to noise ratio (SNR), contrast to noise ratio
(CNR), and mean structural similarity index metrics (MSSIM).
  Results: The proposed algorithm successfully denoised unseen single-frame OCT
B-scans. The denoised B-scans were qualitatively similar to their corresponding
multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean SNR
increased from $4.02 \pm 0.68$ dB (single-frame) to $8.14 \pm 1.03$ dB
(denoised). For all the ONH tissues, the mean CNR increased from $3.50 \pm
0.56$ (single-frame) to $7.63 \pm 1.81$ (denoised). The MSSIM increased from
$0.13 \pm 0.02$ (single frame) to $0.65 \pm 0.03$ (denoised) when compared with
the corresponding multi-frame B-scans.
  Conclusions: Our deep learning algorithm can denoise a single-frame OCT
B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior
quality OCT B-scans with reduced scanning times and minimal patient discomfort.
