# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 4 Chronological Review of Retrospective Approaches
### [2012-2014]  
By the end of 2011, the attention given to the pre-processing started to grow as more complex algorithms for OCT data registration were developed over the following years, as depicted in Figure 8.  
Figure 7: Cumulative bar plot showing the number of reviewed publications that used OCT single, OCT complementary, and multimodal acquisitions for correcting the retinal motion. **[PERSON] et al. [73] (2013)** proposed a new deformable algorithm for intra-volume OCT registration based on the similarity between pairs of A-scans. All the images were first pre-processed by removing the background above the ILM and below the Bruch's membrane (BM) boundaries. The registration consists of three steps. First, the B-scans were registered using only translations in order to align data in the fovea. Next, using the retinal boundaries as landmarks, an affine transformation (translation and scaling) was applied to each pair of A-scans (from the reference and target images). Finally, a deformable registration that uses regularized one-dimensional radial basis functions [90] was applied, computing the sum of squared differences (SSD) between A-scans as the similarity measure. Fifteen volumes with manual segmentations of eight retinal layers were used to evaluate the registration performance. Two of the images were randomly chosen as atlases and registered to the other 13 images. With the estimated deformation field, atlas labels were transformed to each target space and the Dice similarity coefficient between each of the eight layers was reported. **[PERSON] et al. [77] (2014)** followed the previous conference publication and extended the work in a journal paper. A larger validation cohort consisting of OCT images of the right eyes from 45 subjects (19 healthy controls and 26 patients diagnosed with multiple sclerosis) was used. Intensity normalization using a linear contrast stretching and the segmentation of another extra layer (nine in total) were considered in the pre-processing. **[PERSON] et al. [75] (2013)** proposed a method to eliminate motion artifacts by creating a composite image. Multiple OCT volumes from a healthy 27-year-old volunteer were acquired sequentially. The method consisted in stitching together motion-free patches from each dataset. This approach also applied segmentation of the retinal layers, and used the vascular network on each layer to guide the registration. To ensure that the registration used only vascular information, binary masks were obtained. Similarly to [70; 74], it also proposes a two-step global-to-local approach. First, the FFT and correlation are used to register individual stripes from different volumes in a rigid manner. Then, spline-based registration was used to locally deform the vessels to show a continuous, smooth transition between different stripes. Visual inspection was used as validation technique. **[PERSON] et al. [78] (2014)** proposed an improvement of their registration algorithm [71]. They stated that, given the number of alterations that can affect the A-scans, such as vignetting, floaters or lens opacity, corresponding anatomical locations in two input volumes might have very different signal levels and A-scan signatures. Hence, in pre-processing, a bias field estimation based approach to correct illumination differences in the input volumes was employed. An intensity similarity measure using the pseudo Huber norm and a regularization scheme based on a pseudo L\({}_{0.5}\) norm was introduced. Figure 8: Cumulative bar plot showing the number of reviewed publications that applied enhancement, layer segmentation, and vessel segmentation as pre-processing in the registration correction. Similarly to [71] two orthogonal volumes were scanned, and the two-step approach for the registration was developed. In the first stage, only axial motion and axial tilt are coarsely corrected, assuming that the B-scans were rigid structures that can move only on the axial direction. This result was then used as the starting point for a second stage full optimization.