Evaluation of artifact reduction in optical coherence tomography angiography with real-time tracking and motion correction technology - PubMed (original) (raw)
. 2016 Sep 6;7(10):3905-3915.
doi: 10.1364/BOE.7.003905. eCollection 2016 Oct 1.
Affiliations
- PMID: 27867702
- PMCID: PMC5102549
- DOI: 10.1364/BOE.7.003905
Evaluation of artifact reduction in optical coherence tomography angiography with real-time tracking and motion correction technology
Acner Camino et al. Biomed Opt Express. 2016.
Abstract
Artifacts introduced by eye motion in optical coherence tomography angiography (OCTA) affect the interpretation of images and the quantification of parameters with clinical value. Eradication of such artifacts in OCTA remains a technical challenge. We developed an algorithm that recognizes five different types of motion artifacts and used it to evaluate the performance of three motion removal technologies. On en face maximum projection of flow images, the summed flow signal in each row and column and the correlation between neighboring rows and columns were calculated. Bright line artifacts were recognized by large summed flow signal. Drifts, distorted lines, and stretch artifacts exhibited abnormal correlation values. Residual lines were simultaneously a local maximum of summed flow and a local minimum of correlation. Tracking-assisted scanning integrated with motion correction technology (MCT) demonstrated higher performance than tracking or MCT alone in healthy and diabetic eyes.
Keywords: (100.2980) Image enhancement; (170.4470) Ophthalmology; (170.4500) Optical coherence tomography; (330.4150) Motion detection.
Figures
Fig. 1
Detection of motion artifacts in an x-fast or y-fast scan. (A) Bright line artifacts can be identified by setting a summed flow signal threshold. (B) En face angiogram obtained from a scan without tracking. (C) Drift artifacts can be identified by setting a correlation threshold. (D) En face angiogram obtained from a scan assisted by tracking.
Fig. 2
Automated recognition of artifacts in acquisitions processed by MCT or tracking with MCT. The correlation with neighboring lines and thresholds considered for distorted stripes and stretch artifacts are shown in (A). An example of distorted stripe (B1) and stretch artifact (B2) that correspond to (A) are represented in (B). The correlation and summed flow signal thresholds considered for residual lines are shown in (C). Columns of interest (D1-D6) in the identification of residual lines are represented in (D).
Fig. 3
X-fast scans of the same eye with (A1-B1) and without (A2-B2) tracking. Volumetric OCT images are represented in (A1-A2) and the corresponding en face OCTA of the superficial plexus in (B1-B2). Saccadic motion without manifestation of bright lines is indicated by black arrows in B1.
Fig. 4
Comparison of the average and standard deviation of artifact prevalence in x/y-fast scans without tracking, x/y-fast scans with tracking, MCT of scans acquired without tracking and MCT of tracking-assisted scans.
Fig. 5
Similarity of OCTA after MCT for a healthy participant without tracking (A1-A2) and with tracking (B1-B2). Regions enclosed by dashed lines are enlarged in A3, A4 and B3, B4 respectively. Arrows indicate zones where vasculature detail is lost in both MCT-only acquisitions.
Fig. 6
Benefits of tracking-assisted scanning with MCT, shown by two eyes with DR. Scans of the x-fast, y-fast and MCT are shown with tracking (A1-C1) and without tracking (A2-C2). Angiogram sections with data loss highlighted in A2-C2 are represented in D2-F2.
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