Maximum a posteriori signal recovery for optical coherence tomography angiography image generation and denoising

TL;DR

This paper introduces a novel iterative maximum a posteriori algorithm for improving OCTA image quality by reducing noise and artifacts, leveraging probabilistic models and Bayesian statistics.

Contribution

It presents a new Bayesian-based iterative algorithm for OCTA image denoising and generation, combining probabilistic models with regularization techniques.

Findings

Significant improvement in peak signal-to-noise ratio

Enhanced structural similarity in OCTA images

Effective noise reduction and artifact suppression

Abstract

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.