1. Maximum a posteriori signal recovery for optical coherence tomography angiography image generation and denoising
- Author
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A. Yasin Alibhai, Siyu Chen, Daniel Stromer, Lennart Husvogt, Andreas Maier, Nadia K. Waheed, Eric M. Moult, Stefan B. Ploner, James G. Fujimoto, and Julia Schottenhamml
- Subjects
FOS: Computer and information sciences ,Computer science ,Image quality ,Noise reduction ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,Image processing ,02 engineering and technology ,01 natural sciences ,Article ,010309 optics ,Optical coherence tomography ,0103 physical sciences ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Medical imaging ,Maximum a posteriori estimation ,FOS: Electrical engineering, electronic engineering, information engineering ,Ground truth ,medicine.diagnostic_test ,business.industry ,Image and Video Processing (eess.IV) ,Probabilistic logic ,Pattern recognition ,Electrical Engineering and Systems Science - Image and Video Processing ,Atomic and Molecular Physics, and Optics ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,Biotechnology - 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., Comment: 14 pages, 4 figures, to be published in Biomedical Optics Express
- Published
- 2020