  Optical coherence tomography angiography (OCTA) is a novel and clinically
promising imaging modality to image retinal and sub-retinal vasculature. Based
on repeated optical coherence tomography (OCT) scans, intensity changes are
observed over time and used to compute OCTA image data. OCTA data are prone to
noise and artifacts caused by variations in flow speed and patient movement. We
propose a novel iterative maximum a posteriori signal recovery algorithm in
order to generate OCTA volumes with reduced noise and increased image quality.
This algorithm is based on previous work on probabilistic OCTA signal models
and maximum likelihood estimates. Reconstruction results using total variation
minimization and wavelet shrinkage for regularization were compared against an
OCTA ground truth volume, merged from six co-registered single OCTA volumes.
The results show a significant improvement in peak signal-to-noise ratio and
structural similarity. The presented algorithm brings together OCTA image
generation and Bayesian statistics and can be developed into new OCTA image
generation and denoising algorithms.
