# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 5 Discussion
The latter depends on the quality of the automatically computed landmarks or regions, which can bias the results, but it is deterministic and faster. The unique morphology of the retinal shape or the expected thickness of the retinal layers have also been explored as retinal landmarks to validate the registration techniques. Techniques based on annotated structures are suitable to validate inter-volume registration. However, special care must be exercised when analysing intra-volume misalignments, as significant changes between B-scans are expected even within accurately registered data. These changes take place mainly between steep adjacent areas (e.g., optic disc) or if a pathology is present (e.g., macular hole). Known deformations in simulated data have also been used for validation purposes. One option is to apply specific transformations (e.g., translation, shearing, rotation) and verify if the registration recovers them accurately. However, to cover all the possible misregistrations is a challenging task, as it is complicated to accurately model retinal motion. Some authors proposed to learn these misregistrations from an unregistered OCT dataset, but this requires a large amount of data to ensure that the artefacts are representative. Finally, additional modalities that are considered motion-free, such as FP or SLO, can be used as a ground truth to correct retinal motion in OCT. A special case for validation would be the retinal trackers, which perform an online correction to improve the quality of the images during the acquisition. The goal of the motion correction in OCT volumes is not only to ensure that the images are accurately aligned, but also to understand the nature behind the motion. Analyzing the advantages and drawbacks of the approaches studied in this review, it can be concluded that the best approaches are those that combine the orthogonal volume acquisition, as they are able to correct the axial misalignments over the slow and the fast axis, with the multi-modality acquisition, as they can correct coronal misalignments. However, an approach that uses these techniques is less convenient from a practical point of view due to its higher complexity and longer acquisition time. An algorithm that requires only one OCT volume would be simpler, with the additional advantage of allowing the processingof previously acquired volumes. Most of the registration procedures used a pairwise approach. A common problem is the incorrect registration between B-scans that have not been affected by retinal motion. One possible solution is to include constraints that restrict the type and/or amount of the transformations. Even though non-rigid transformations may provide some flexibility to the registration, it can also cause severe deformations in the retinal structure that can be easily missed during the visual inspection. Therefore, as these algorithms do not use references, a strong validation is required to assess their performance. Some publicly-available registration frameworks have been made available to facilitate the research in medical image registration [114] as well as the comparison of different registration procedures. In general, the largest collection of image registration tools can be found in the Insight ToolKit (ITK) [115]. Other works, using and extending ITK, are Advanced Normalization Tools (ANTs) [44; 116; 117], elastix [43], plastimatch [118; 119], and Deformable registration via attribute matching and mutual-saliency weighting (DRAMMS) [120]. Moreover, there are a number of non-ITK-based implementations such as NiftyReg [121]. These frameworks include several optimization methods, multiresolution schemes, interpolators, transformation models, and cost functions, allowing for studying the effect that each component has in the registered image. These image registration tools may be a good starting point for those aiming to do retrospective OCT motion correction.