# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 1 Introduction  
Optical coherence tomography (OCT) was introduced as a new modality for cross-sectional imaging of biological tissue in 1991 [1] and, since then, it has become one of the most successful technologies in the history of biomedical optics [2]. OCT is nowadays used in a wide range of research fields, but it is in ophthalmic imaging where it finds its main application [3, 4]. Retinal imaging diagnostics has been completely revolutionized over the last decade by OCT, which has become the gold standard imaging modality [5, 6]. OCT is an interferometric technique that detects the magnitude of reflected or backscattered light from different depths. An OCT system probes the sample with low a temporal coherence light beam, letting the backscattered light interfere with a reference beam from the same light source. From the resulting interference signal, the reflectivity profile of the sample along the beam axis (A-scan) is derived. Cross-sectional images (B-scans) are generated by recording of multiple A-scans, while scanning the incident light beam transversely over the sample. Three-dimensional (3D) volumetric data are generated by acquiring sequential cross-sectional images in a raster pattern [4, 5]. The time needed for acquiring each dataset depends mainly on its spatial dimension and the imaging speed of the device, which is broadly given by the A-scan acquisition rate. Typical state-of-the-art research systems feature A-scan rates of up to several hundreds of kilohertz [7, 8, 9], usually limited by the camera read-out rate in Spectral Domain-OCT (SD-OCT), or the sweep rate of the light source in Swept Source-OCT (SS-OCT) [10]. In older systems that are based on Time Domain OCT (TD-OCT), the maximum velocity of the reference arm length change limited the A-scan rate which typically was below 2 kHz. Fast image acquisition in vivo is very important for sampling correctly large field-of-view (LFov) OCT volumes, as retinal motion cannot be avoided. Even when the subject fixates on a stationary object, the eye carries out involuntary small andrapid movements that may reach several arcminutes with a frequency between 10 and 100 Hz [11; 12]. These involuntary fixational eye movements are usually categorized as high frequency tremors, rapid microsaccades, or slow drifts, depending on their frequency and magnitude [11; 12]. In addition, axial eye motion (introduced for example by heart beat) may distort B-scan images and result in deformed 3D data of the retina. The degree of inter- and intra-frame OCT image distortion generated by involuntary eye movements, as well as potential signal attenuation (due to the fringe wash out in the case of SD-OCT), increases with the acquisition time. This compromises the detection and tracking of the progression of retinal diseases. Consequently, complementary retinal tracking hardware or post-processing software procedures are often used to correct the distorted data. A substantial number of studies using LFoV OCT volumes are published on a monthly basis. However, only a minority tackle motion correction, even though a minimal distortion can easily bias structural analysis, such as measurements of retinal layer thicknesses. Although some correction methods have been published over the last years, motion artefacts from axial movement, changes in fixation, and saccades remain a major problem in OCT imaging [13; 14]. To the best of our knowledge, only [PERSON] et al. in 2015 [15] and later in 2017 [16] provided a review on registration methodologies used in retinal OCT imaging. A succinct overview on registration approaches for speckle noise reduction, multimodality imaging, and motion correction is given in 2015, whereas, in 2017, an extended review comparing hardware-based versus software-based techniques is provided. While hardware approaches are well presented, only a few publications on software-based approaches were covered.