  Optical coherence tomography (OCT) can perform non-invasive high-resolution
three-dimensional (3D) imaging and has been widely used in biomedical fields,
while it is inevitably affected by coherence speckle noise which degrades OCT
imaging performance and restricts its applications. Here we present a novel
speckle-free OCT imaging strategy, named toward-ground-truth OCT (tGT-OCT),
that utilizes unsupervised 3D deep-learning processing and leverages OCT 3D
imaging features to achieve speckle-free OCT imaging. Specifically, our
proposed tGT-OCT utilizes an unsupervised 3D-convolution deep-learning network
trained using random 3D volumetric data to distinguish and separate speckle
from real structures in 3D imaging volumetric space; moreover, tGT-OCT
effectively further reduces speckle noise and reveals structures that would
otherwise be obscured by speckle noise while preserving spatial resolution.
Results derived from different samples demonstrated the high-quality
speckle-free 3D imaging performance of tGT-OCT and its advancement beyond the
previous state-of-the-art.
