OCT Angiography
OCT angiography extends standard OCT by acquiring repeated B-scans at the same location and computing the decorrelation of the complex OCT signal between successive scans. Moving red blood cells cause temporal fluctuations that differ from static tissue, enabling label-free visualization of retinal vasculature. The contrast mechanism uses amplitude decorrelation (SSADA), phase variance, or complex-signal algorithms. Key limitations include motion artifacts, projection artifacts from superficial vessels, and limited field of view.
Decorrelation Contrast
Speckle
ssada
SPECTROMETER
Forward-Model Signal Chain
Each primitive represents a physical operation in the measurement process. Arrows show signal flow left to right.
P(low-coherence) → Σ(interference) → D(g, η₁)
Benchmark Variants & Leaderboards
OCTA
OCT Angiography
P(low-coherence) → Σ(interference) → D(g, η₁)
Standard Leaderboard (Top 10)
| # | Method | Score | PSNR (dB) | SSIM | Trust | Source |
|---|---|---|---|---|---|---|
| 🥇 | ScoreOCT | 0.869 | 37.95 | 0.973 | ✓ Certified | Wei et al., ECCV 2025 |
| 🥈 | DiffusionOCT | 0.860 | 37.52 | 0.970 | ✓ Certified | Zhang et al., NeurIPS 2024 |
| 🥉 | SpeckleFormer | 0.846 | 36.85 | 0.964 | ✓ Certified | Devalla et al., ECCV 2024 |
| 4 | RetinalFormer | 0.836 | 36.35 | 0.960 | ✓ Certified | Chen et al., ICCV 2024 |
| 5 | OCT-ViT | 0.831 | 36.12 | 0.958 | ✓ Certified | Tian et al., ICCV 2024 |
| 6 | OCTA-Net | 0.798 | 34.6 | 0.942 | ✓ Certified | Hybrid U-Net+Transformer, 2023 |
| 7 | U-Net-OCT | 0.782 | 33.85 | 0.935 | ✓ Certified | U-Net variant |
| 8 | Speckle-DenoiseNet | 0.764 | 33.1 | 0.925 | ✓ Certified | Devalla et al., BOE 2019 |
| 9 | NLM-OCT | 0.688 | 30.2 | 0.870 | ✓ Certified | Non-local means variant |
| 10 | BM4D | 0.663 | 29.3 | 0.850 | ✓ Certified | Maggioni et al., IEEE TIP 2013 |
Showing top 10 of 13 methods. View all →
Mismatch Parameters (3) click to expand
| Name | Symbol | Description | Nominal | Perturbed |
|---|---|---|---|---|
| inter_bscan_time | ΔT | Inter-B-scan time error (ms) | 0 | 0.5 |
| bulk_motion | Δv_b | Bulk motion artifact (mm/s) | 0 | 0.2 |
| decorrelation_threshold | ΔD_th | Decorrelation threshold error | 0.5 | 0.55 |
Reconstruction Triad Diagnostics
The three diagnostic gates (G1, G2, G3) characterize how reconstruction quality degrades under different error sources. Each bar shows the relative attribution.
Model: decorrelation contrast — Mismatch modes: bulk motion, projection artifact, shadow artifact, saccade artifact
Noise: speckle — Typical SNR: 12.0–35.0 dB
Requires: interscan time, decorrelation threshold, layer segmentation, bulk motion correction
Modality Deep Dive
Principle
OCT Angiography detects blood flow non-invasively by comparing repeated OCT B-scans at the same location. Moving red blood cells cause temporal fluctuations in the OCT signal (amplitude and/or phase), while static tissue remains constant. Decorrelation, variance, or differential analysis between repeated scans produces a motion-contrast image revealing the vasculature without the need for injectable contrast agents.
How to Build the System
Use a high-speed OCT system (≥70 kHz A-scan rate, swept-source preferred) capable of repeated B-scans at the same location. Acquire 2-4 repeated B-scans at each position with inter-scan time of 3-10 ms. An eye-tracking system is essential for ophthalmic OCTA to correct microsaccades. Process with split-spectrum amplitude-decorrelation (SSADA), optical microangiography (OMAG), or phase-variance algorithms.
Common Reconstruction Algorithms
- SSADA (Split-Spectrum Amplitude-Decorrelation Angiography)
- OMAG (Optical Micro-Angiography, complex signal differential)
- Phase-variance OCTA
- Deep-learning OCTA denoising and vessel segmentation
- Projection artifact removal algorithms
Common Mistakes
- Bulk tissue motion producing decorrelation artifacts (false flow signals)
- Projection artifacts where superficial vessel shadows appear in deeper layers
- Shadow artifacts beneath large vessels causing false flow voids
- Insufficient inter-scan interval for detecting slow capillary flow
- Motion artifacts from blinks or microsaccades corrupting OCTA volumes
How to Avoid Mistakes
- Apply bulk motion correction (axial and lateral registration) before decorrelation analysis
- Use projection artifact removal algorithms (slab subtraction or OMAG-based)
- Increase number of repeated B-scans to improve SNR and reduce shadow impact
- Optimize inter-scan time: shorter for fast flow, longer for slow capillary flow
- Use active eye tracking and discard frames with large motion; average multiple volumes
Forward-Model Mismatch Cases
- The widefield fallback applies static spatial blur, but OCTA detects blood flow by comparing repeated OCT B-scans — the temporal decorrelation between scans caused by moving red blood cells is not modeled
- OCTA is fundamentally a motion-contrast technique (flow signal = decorrelation or variance between repeated measurements) — the widefield static model has no temporal dimension and cannot detect or distinguish flowing from static tissue
How to Correct the Mismatch
- Use the OCTA operator that models repeated OCT measurements at the same location: static tissue produces correlated signals while flowing blood produces decorrelated signals between repeated scans
- Extract flow maps using SSADA (split-spectrum amplitude decorrelation) or OMAG (optical microangiography) that require multiple temporally separated OCT measurements as input
Experimental Setup
Zeiss PLEX Elite 9000 / Optovue AngioVue
840
68
6x6 mm
4
15
SSADA / OCTA ratio
Signal Chain Diagram
Key References
- Jia et al., 'Split-spectrum amplitude-decorrelation angiography (SSADA)', Opt. Express 20, 4710 (2012)
- Spaide et al., 'OCT Angiography', Prog. Retin. Eye Res. 64, 1 (2018)
Canonical Datasets
- OCTA-500 (Li et al., Scientific Data 2024)
- ROSE retinal OCTA vessel segmentation