Optic nerve head three-dimensional shape analysis

We present a method for optic nerve head (ONH) 3-D shape analysis from retinal optical coherence tomography (OCT). The possibility to noninvasively acquire in vivo high-resolution 3-D volumes of the ONH using spectral domain OCT drives the need to develop tools that quantify the shape of this structure and extract information for clinical applications. The presented method automatically generates a 3-D ONH model and then allows the computation of several 3-D parameters describing the ONH. The method starts with a high-resolution OCT volume scan as input. From this scan, the model-defining inner limiting membrane (ILM) as inner surface and the retinal pigment epithelium as outer surface are segmented, and the Bruch's membrane opening (BMO) as the model origin is detected. Based on the generated ONH model by triangulated 3-D surface reconstruction, different parameters (areas, volumes, annular surface ring, minimum distances) of different ONH regions can then be computed. Additionally, the bending energy (roughness) in the BMO region on the ILM surface and 3-D BMO-MRW surface area are computed. We show that our method is reliable and robust across a large variety of ONH topologies (specific to this structure) and present a first clinical application

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