# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 4 Chronological Review of Retrospective Approaches
### [2015-2018]  
Although the first registration manuscript using SS-OCT mentioned in this bibliographic review appears in 2012 [71], these devices were not widely used until 2015. By then, an A-scan acquisition rate also raised up to 100 kHz, contributing to a reduction of the motion artefacts observed in the fast plane. **[PERSON] et al. **[81]**(**2016**) proposed a method to jointly do registration and motion correction between longitudinal data. A dataset of 26 healthy control subjects was used in this study. The method required complementary data (volumes acquired from the same subject at different times). A fundus projection image (i.e., an image obtained by averaging the intensity in the depth direction between certain layers) was created using each volume. Then, a set of points representing the vessels were extracted from these images. The correspondences between the sets of points were estimated using CPD [91]. CPD introduces non-rigid deformations, and the authors aimed to restrict the deformations to rigid plus a scaling component. Thus, a cost function was minimized, iterating between the point-based registration and a lasso regression. The optimization of this cost function was done interpreting it as a simple least squares point-based registration problem. Visual evaluation and average RMSE of the manually selected landmark points after registration were used as validation techniques. **[PERSON] et al. [82] (2016)** proposed an algorithm that combined segmentation and registration using orthogonal volumes. As pre-processing, retinal boundaries in each B-scan were segmented and used to produce an enhanced en face image of the retina. These en face projections were used for lateral registration in three steps. First, the saccadic eye motion was detected, and the saccade locations were used to split the volumes. This was done based on discontinuities in the en face projection detected by lower correlation with the surrounding regions. Second, the inter-saccadic intervals were stitched using orthogonal scans as reference and a locally affine model. Third, an orientation-aware optical flow algorithm for dense local registration was applied. This results in motion only in the fast-scan direction. Once the affine transformations and local displacements had been found for each saccade-free region of the enhanced en face image, they were applied to the A-scans in order to correct the (lateral) motion in the whole 3D volume. In the last stage of the algorithm, the segmented retinal surfaces of each volume were used to correct for motion in the axial direction (vertical shifts of the A-scans). For validation, the MI, RMSE and distance between layers were computed for the retinal volumes of 12 subjects. The differences between vascular trees and artificial data with known movements were also used for validation. **[PERSON] et al. [50] (2016)** proposed a novel method to correct the misalignment between consecutive B-scans to improve the accuracy in multi-modality retinal image registration. The method was motivated by the fact that pixels from static tissue yield small decorrelation values whereas pixels from the faster moving tissue yield high decorrelation values. Hence, decorrelations from overlapping scans were computed and used to create an en face decorrelation image. The B-scans affected by motion were aligned (translation) with their precedents using a iterative diamond search strategy [95]. Lastly, the corrected OCT en face image was registered to a color fundus image using a matching method based on the low-dimensional step pattern analysis [96]. Here, 28 pre-defined features were matched by Euclidean distance. Then, a random sample consensus [97] was used with an affine transformation to produce the final registration. In order to validate, the RMSE, the maximum absolute error, and the median error of 18 3D-OCT scans and the corresponding color fundus images were used. **[PERSON] et al. [83] (2016)** proposed a pairwise registration approach based on saliency and center bias constraints.