Optical coherence tomography (OCT) retinal volumes are prone to motion artifacts due to the movement of the eye during acquisition. Current retrospective motion correction algorithms are either computationally expensive or limited to pair-wise formulations, based on registration of consecutive slices (B-scans). This type of approach can lead to errors when individual B-scans contain artifacts or lack sufficient signal. Instead, we propose a framework, based on unsupervised deep learning, that corrects motion by aligning groups of consecutive B-scans. The network architecture is fully-convolutional and, thus, it can perform inference on the entire OCT volume, even though it was trained on groups of smaller size. Moreover, we improved performance by inferring in a multi-shot recurrent manner, which was further leveraged by a novel data augmentation technique. We used an exhaustive search algorithm (brute-force) to compare the proposed method against, both quantitatively and qualitatively based on visual assessment. In a dataset of 146 (training: 106, validation: 40) macula and optic disc volumes from 19 healthy subjects, our best performing configuration achieved 72% reduction in registration errors compared to the exhaustive search algorithm, with a computation time of 2.35 seconds. These results demonstrated that our framework has the potential to provide a fast and robust solution, based on deep learning registration, for the motion correction of OCT images.
Significance: Speckle has historically been considered a source of noise in coherent light imaging. However, a number of works in optical coherence tomography (OCT) imaging have shown that speckle patterns may contain relevant information regarding subresolution and structural properties of the tissues from which it is originated.
Aim: The objective of this work is to provide a comprehensive overview of the methods developed for retrieving speckle information in biomedical OCT applications.
Approach: PubMed and Scopus databases were used to perform a systematic review on studies published until December 9, 2021. From 146 screened studies, 40 were eligible for this review.
Results: The studies were clustered according to the nature of their analysis, namely static or dynamic, and all features were described and analyzed. The results show that features retrieved from speckle can be used successfully in different applications, such as classification and segmentation. However, the results also show that speckle analysis is highly application-dependant, and the best approach varies between applications.
Conclusions: Several of the reviewed analyses were only performed in a theoretical context or using phantoms, showing that signal-carrying speckle analysis in OCT imaging is still in its early stage, and further work is needed to validate its applicability and reproducibility in a clinical context.
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