Advances in Fourier-domain optical coherence tomography (Fd-OCT) permit visualization of three-dimensional morphology of in-vivo retinal structures in a way that promises to revolutionize clinical and experimental imaging of the retina. The relevance of these advances will be further increased by the recent introduction of several commercial Fd-OCT instruments that can be used in clinical practice. However, due to some inherent limitations of current Fd-OCT technology (e.g., lack of spectroscopic information, inability to measure fluorescent signals), it is important to co-register Fd-OCT data with images obtained by other clinical imaging modalities such as fundus cameras and fluorescence angiography to create a more complete interpretation and representation of structures imaged. The co-registration between different imaging platforms becomes even more important if small retinal changes are monitored for early detection and treatment. Despite advances in volume acquisition speed with FD-OCT, eye/head motion artifacts can be still seen on acquired data. Additionally high-sampling density, large field-of-view (FOV) Fd-OCT volumes may also be needed for comparison with conventional imaging. In standard Fd-OCT systems, higher sampling density and larger imaging FOV (with constant sampling densities) lead to longer acquisition time which further increases eye/head motion artifacts. To overcome those problems, we tested 3D stitching of multiple, smaller retinal volumes which can be acquired in a less time (reduction of motion artifacts) and/or when stitched create a larger FOV representation of the retina. Custom visualization software that makes possible manual co-registration and simultaneous visualization of volumetric Fd-OCT data sets is described. Volumetric visualizations of healthy retinas with corresponding fundus pictures are presented followed by examples of retinal volumes of high sampling density that are created from multiple "standard" Fd-OCT volumes.
Recent developments in Fourier domain—optical coherence tomography (Fd-OCT) have increased the acquisition speed of current ophthalmic Fd-OCT instruments sufficiently to allow the acquisition of volumetric data sets of human retinas in a clinical setting. The large size and three-dimensional (3D) nature of these data sets require that intelligent data processing, visualization, and analysis tools are used to take full advantage of the available information. Therefore, we have combined methods from volume visualization, and data analysis in support of better visualization and diagnosis of Fd-OCT retinal volumes. Custom-designed 3D visualization and analysis software is used to view retinal volumes reconstructed from registered B-scans. We use a support vector machine (SVM) to perform semiautomatic segmentation of retinal layers and structures for subsequent analysis including a comparison of measured layer thicknesses. We have modified the SVM to gracefully handle OCT speckle noise by treating it as a characteristic of the volumetric data. Our software has been tested successfully in clinical settings for its efficacy in assessing 3D retinal structures in healthy as well as diseased cases. Our tool facilitates diagnosis and treatment monitoring of retinal diseases.
The ability to obtain true three-dimensional (3D) morphology of the retinal structures is essential for future clinical and
experimental studies. It becomes especially critical if the measurements acquired with different instruments need to be
compared, or precise volumetric data are needed for monitoring and treatment of retinal disease. On the other hand, it is
well understood that optical coherence tomography (OCT) images are distorted by several factors. Only limited work has
been performed to eliminate these problems in ophthalmic retinal imaging, perhaps because they are less evident in the
more common 2D representation mode of time-domain OCT. With recent progress in imaging speed of Fourier domain -
OCT (Fd-OCT) techniques, however, 3D OCT imaging is more frequently being used, thereby exposing problems that
have been ignored previously. In this paper we propose possible solutions to minimize and compensate for artifacts
caused by subject eye and head motion, and distortions caused by the geometry of the scanning optics. The first is
corrected by cross-correlation based B-scan registration techniques; the second is corrected by incorporating the
geometry of the scanning beam into custom volume rendering software. Retinal volumes of optical nerve head (ONH)
and foveal regions of healthy volunteer, with and without corrections, are presented. Finally, some common factors that
may lead to increased distortions of the ophthalmic OCT image such as refractive error or position of the subject's head
are discussed.
KEYWORDS: Optical coherence tomography, Data acquisition, Diagnostics, Volume rendering, 3D image processing, Image segmentation, Retinal scanning, 3D acquisition, Data centers, Signal detection
We report on the development of quantitative, reproducible diagnostic observables for age-related macular degeneration
(AMD) based on high speed spectral domain optical coherence tomography (SDOCT). 3D SDOCT volumetric data sets
(512 x 1000 x 100 voxels) were collected (5.7 seconds acquisition time) in over 50 patients with age-related macular
degeneration and geographic atrophy using a state-of-the-art SDOCT scanner. Commercial and custom software utilities
were used for manual and semi-automated segmentation of photoreceptor layer thickness, total drusen volume, and
geographic atrophy cross-sectional area. In a preliminary test of reproducibility in segmentation of total drusen volume
and geographic atrophy surface area, inter-observer error was less than 5%. Extracted volume and surface area of AMD-related
drusen and geographic atrophy, respectively, may serve as useful observables for tracking disease state that were
not accessible without the rapid 3D volumetric imaging capability unique to retinal SDOCT.
KEYWORDS: Image segmentation, Optical coherence tomography, 3D image processing, Eye, 3D acquisition, Visualization, Retina, 3D metrology, Data acquisition, 3D visualizations
The acquisition speed of current FD-OCT (Fourier Domain - Optical Coherence Tomography) instruments allows rapid
screening of three-dimensional (3D) volumes of human retinas in clinical settings. To take advantage of this ability
requires software used by physicians to be capable of displaying and accessing volumetric data as well as supporting
post processing in order to access important quantitative information such as thickness maps and segmented volumes.
We describe our clinical FD-OCT system used to acquire 3D data from the human retina over the macula and optic
nerve head. B-scans are registered to remove motion artifacts and post-processed with customized 3D visualization and
analysis software. Our analysis software includes standard 3D visualization techniques along with a machine learning
support vector machine (SVM) algorithm that allows a user to semi-automatically segment different retinal structures
and layers. Our program makes possible measurements of the retinal layer thickness as well as volumes of structures of
interest, despite the presence of noise and structural deformations associated with retinal pathology. Our software has
been tested successfully in clinical settings for its efficacy in assessing 3D retinal structures in healthy as well as
diseased cases. Our tool facilitates diagnosis and treatment monitoring of retinal diseases.
We present a method that maps a complex surface geometry to an equally complicated, similar surface. One main objective of our effort is to develop technology for automatically transferring surface annotations from an atlas brain to a subject brain. While macroscopic regions of brain surfaces often correspond, the detailed surface geometry of corresponding areas can vary greatly. We have developed a method that simplifies a subject brain's surface forming an abstract yet spatially descriptive point cloud representation, which we can match to the abstract point cloud representation of the atlas brain using an approach that iteratively improves the correspondence of points. The generation of the point cloud from the original surface is based on surface smoothing, surface simplification, surface classification with respect to curvature estimates, and clustering of uniformly classified regions. Segment mapping is based on spatial partitioning, principal component analysis, rigid affine transformation, and warping based on the thin-plate spline (TPS) method. The result is a mapping between
topological components of the input surfaces allowing for transfer of annotations.
Best quadratic simplicial spline approximations can be computed, using quadratic Bernstein-Bezier basis functions, by identifying and bisecting simplicial elements with largest errors. Our method begins with an initial triangulation of the domain; a best quadratic spline approximation is computed; errors are computed for all simplices; and simplices of maximal error are subdivided. This process is repeated until a user-specified global error tolerance is met. The initial approximations for the unit square and cube are given by two quadratic triangles and five quadratic tetrahedra, respectively. Our more complex triangulation and approximation method that respects field discontinuities and geometrical features allows us to better approximate data. Data is visualized by using the hierarchy of increasingly better quadratic approximations generated by this process. Many visualization problems arise for quadratic elements. First tessellating quadratic elements with smaller linear ones and then rendering the smaller linear elements is one way to visualize quadratic elements. Our results show a significant reduction in the number of simplices required to approximate data sets when using quadratic elements as compared to using linear elements.
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