# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 5 Discussion
Retrospective procedures may involve single, multimodal, or complementary acquisitions. Multimodal acquisition refers to offline alignment of two or more imaging modalities. In complementary acquisitions, different protocols are used to acquire data at the same location. Single acquisition includes the corrections implemented when only one image or volume is available. An optimal procedure for correcting OCT data misalignment could be achieved including information on the underlying retinal motion model, that is, to use theoretical representations that integrate knowledge on the frequency, amplitude and direction of the retinal motion. Unfortunately, there are a few drawbacks that limit the application of this procedure in OCT imaging. First, it is necessary to include information about the acquisition settings. This might increase the model complexity substantially, as many configurations are possible (e.g., field of view, axial and lateral resolutions, acquisition rate, scan pattern, etc). In addition, unlike other types of body motion (e.g., respiratory, cardiac motion), retinal motion results from saccades and drifts, which vary on amplitude, direction, and frequency, making its combination difficult to predict. Moreover, retinal motion may differ significantly between individuals [12], hindering the development of a general model. Thus, the correction based on theoretical modelling has been on hold, although the continuous collection of large datasets using retinal trackers may allow the use of advanced learning models to predict the retinal motion in the near future. Currently, coronal and axial misalignments due to involuntary eye movements remain a major problem on OCT imaging [13; 14], affecting both its quality and reliability. Subtle structural changes on the retinal tissue due to the early manifestation of a pathology can be easily understood as a misregistration problem. Pre-processing techniques can be used to mitigate these issues. Even if a common approach does not exist, noise removal using filtering (e.g., median, Wiener, Gabor), downsampling to reduce the computational complexity, and normalization, are often used. Depending on the application, to take into account specific areas of interest can also be helpful to enhance the registration performance. Background removal, retinal layer segmentation, or vasculature segmentation have been reported in a number of studies so far. The background removal is important in those cases where the SNR is relatively low and may have a significant weight on the registration performance. Retinal layer segmentation has been extensively used to guide image registration in OCT volumes, specifically if their position and shape are of high interest for the research problem. However, a precise layer segmentation is far from a straightforward task, so most of the publications opted for segmenting only a few layers, usually the retinal boundaries and the RPE layer, as they have the highest reflectivity, making them easier to distinguish. A step commonly associated with layer segmentation is the flattening, which facilitates the segmentation by removing the retinal curvature. However, depending on the application, this curvature has to be recovered later. Main vessel segmentation is also often used, specifically if multi-modality data are available, such as OCT en face imaging and SLO or FP. In order to facilitate the vascular segmentation, it is common to apply spatial filters, such as Gabor or Laplacian of Gaussian. The first publication in OCT image registration dates to 1993 and, until 2005, only axial misalignments were taken into account. The first devices only captured a few B-scans, so the correction was only done within a cross-sectional image. Cross-correlation was by then the most popular metric, although it may not be sensitive enough to discriminate between the smallest structural changes among neighbouring OCT scans [33]. The fast image acquisition introduced by SD-OCT allowed for obtaining several B-scans conforming a volume.