Intensity Inhomogeneity Correction of SD-OCT Data Using Macular Flatspace

Images of the retina acquired using optical coherence tomography (OCT) often suffer from intensity inhomogeneity problems that degrade both the quality of the images and the performance of automated algorithms utilized to measure structural changes. This intensity variation has many causes, including off-axis acquisition, signal attenuation, multi-frame averaging, and vignetting, making it difficult to correct the data in a fundamental way. This paper presents a method for inhomogeneity correction by acting to reduce the variability of intensities within each layer. In particular, the N3 algorithm, which is popular in neuroimage analysis, is adapted to work for OCT data. N3 works by sharpening the intensity histogram, which reduces the variation of intensities within different classes. To apply it here, the data are first converted to a standardized space called macular flat space (MFS). MFS allows the intensities within each layer to be more easily normalized by removing the natural curvature of the retina. N3 is then run on the MFS data using a modified smoothing model, which improves the efficiency of the original algorithm. We show that our method more accurately corrects gain fields on synthetic OCT data when compared to running N3 on non-flattened data. It also reduces the overall variability of the intensities within each layer, without sacrificing contrast between layers, and improves the performance of registration between OCT images

Review on retrospective procedures to correct retinal motion artefacts in OCT imaging

Motion artefacts from involuntary changes in eye fixation remain a major imaging issue in optical coherence tomography (OCT). This paper reviews the state-of-the-art of retrospective procedures to correct retinal motion and axial eye motion artefacts in OCT imaging. Following an overview of motion induced artefacts and correction strategies, a chronological survey of retrospective approaches since the introduction of OCT until the current days is presented. Pre-processing, registration, and validation techniques are described. The review finishes by discussing the limitations of the current techniques and the challenges to be tackled in future developments

Deformable Image Registration in the Analysis of Multiple Sclerosis

In medical image analysis, image registration is the task of finding corresponding features in two or more images, and using them to solve for the transformation that best aligns the images. Knowing the alignment allows information, such as landmarks and functional metrics, to be easily transferred between images, and allows them to be analyzed together. This dissertation focuses on the development of deformable image registration techniques for the analysis of multiple sclerosis (MS), a neurodegenerative disease that damages the myelin sheath of nervous tissue. MS is known to affect the entire central nervous system (CNS), and can result in the loss of sensorimotor control, cognition, and vision. Hence, the four primary contributions of this dissertation are on the development and application of deformable image registration in the three areas of the CNS that are most currently studied for MS -- the spinal cord, the retina, and the brain. First, for spinal cord magnetic resonance imaging (MRI), an approach is presented that uses deformable registration to provide atlas priors for automatic topology-preserving segmentation of the spinal cord and cerebrospinal fluid. The method shows high accuracy and robustness when compared to manual raters, and allows spinal cord atrophy to be analyzed on large datasets without manual segmentations. Second, for spinal cord diffusion tensor imaging, a pipeline is presented that uses deformable registration to correct for susceptibility distortions in the images. The pipeline allows for accurate computation of spinal cord diffusion metrics, which are shown to be significantly correlated with clinical measures of sensorimotor function and disability levels. Third, for optical coherence tomography (OCT) of the retina, a deformable registration technique is presented that constrains the transformation to follow the OCT acquisition geometry. 3D voxel-based analysis using the algorithm found significant differences between healthy and MS cohorts in regions of the retina that is consistent with previous findings using 2D analysis. Lastly, for brain MRI, a multi-channel registration framework is presented that can use distance transforms and image synthesis to improve registration accuracy. Together, these techniques have enabled several types of analysis that were previously unavailable for the study of MS