# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 3 Methods
### Motion Correction Strategies
#### Input Data  
_Single acquisitions_: These acquisitions correspond either to a cross-sectional image or an OCT volume, as shown in Figure 3. The algorithms applied to this input data can be divided into two large groups: approaches that use landmarks of the image (feature-based registration [47]) and methodologies that process all the content in the image (intensity-based registration). To combine both groups is also possible. Feature-based registration depends on the location of the points of interest (e.g., vasculature, vessel intersections or bifurcations, retinal layers, or lesions), which are used as reference to correct the misalignment. While feature-based registration is mainly combined with manual feature annotation, automated algorithms could be found in the literature demonstrating a good performance. Automated intensity-based registration approaches take into account the information of the whole data. Manual intervention is not required, which usually reduces the operational time, although this may vary depending on the specific algorithm.  
Generic registration algorithms can be applied to any single OCT B-scan or volume, without using prior knowledge or additional information. Nevertheless, additional data and comprehension of the retinal motion may help in designing an efficient and robust algorithm to correct OCT data misalignment.  
_Complementary acquisitions_: The lack of reliable references in single-modality devices led to the investigation of complementary data acquisition, captured at the same region of interest as the main data. Within this procedure, two alternatives are possible. First, several B-scans or full 3D volumes can be collected at the exact same position and with the same fast plane. Second, an orthogonal raster scan can be acquired (either a few B-scans or a full 3D volume). The latter has gathered more attention by the OCT community over the last years. By having two orthogonal volumes, as in Figure 4, the axial misalignments seen in the slow plane of the reference volume can be corrected by the fast plane of the respective orthogonal volume.  
_Multimodal acquisitions_: Registration algorithms are also regularly used for finding an accurate correspondence between modalities that do not exhibit the same characteristics, as depicted in Figure 5. Depending on the acquisition nature, these complementary modalities, such as SLO imaging or Fundus Photography (FP), can be roughly considered motion artefact free when compared to OCT imaging. Hence, a number of studies have opted for using another imaging modality as a reference to correct OCT data. The main advantage of this approach is to have a reference to guide the registration. However, some commonly used modalities, such as SLO, can also be affected by retinal motion themselves [48].  
Figure 4: Schematic B-scans acquired orthogonally. The motion artefacts appearing on the slow plane in the reference are corrected using the information of the fast plane of the respective orthogonal volume.  
Figure 3: Correction of a single cross-section (**top**) and a single volume (**bottom**).