Does Projection Artifact Removal Improve Visualization of Images in Optical Coherence Tomography Angiography?
Bibliographic record
Abstract
Purpose: This study assesses the frequency of projection artifacts in optical coherence tomography angiography (OCTA) en face images and compares images before and after applying a 3-dimensional projection artifact removal (3D-PAR) algorithm. Methods: This is a single-center, retrospective study that included consecutive patients with any underlying diagnosis who had OCTA obtained from January to March 2017. Patients with various retinal diseases and also healthy eyes were included. All participants underwent imaging with a scan area of 3 mm × 3 mm. The 4 default en face slabs were analyzed: superficial capillary plexus (SCP), deep capillary plexus (DCP), outer retina (OR), and choriocapillaris (CC). Images were qualitatively analyzed before and after 3D-PAR by 2 independent graders. Results: None of the SCP images had projection artifact before or after 3D-PAR. Scans of the DCP presented projection artifact in 96.5% of the cases. After 3D-PAR, 14.7% had a complete improvement of projection artifact, 56.5% had a partial improvement, 14.1% were worse, and 14.7% presented no change. In the OR, 2.9% had projection artifact, with a complete improvement after 3D-PAR in 40%, partial improvement in 20%, and no change in 40%. Projection artifact was initially present in 97.6% of the images in the CC. After 3D-PAR, there was a complete improvement in 72.9%, partial improvement in 26.5%, and no change in 0.6%. Choroidal neovascularization (CNV) was detected in 29 eyes (17.1%), and 3D-PAR improved detection of CNV in 12 cases (41.4%). Conclusions: OCTA with 3D-PAR technology minimizes the appearance of projection artifacts in the DCP and CC slabs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".