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Biomedical subjects

David H Laidlaw

Publications and source records attributed to David H Laidlaw.

9 recordsLinked to original sources
PubMed2006

Super-resolution registration using tissue-classified distance fields.

We present a method for registering the position and orientation of bones across multiple computed-tomography (CT) volumes of the same subject. The method is subvoxel accurate, can operate on multiple bones within a set of volumes, and registers bones that have features commensurate in size to the voxel dimension. First, a geometric object model is extracted from a reference volume image. We use then unsupervised tissue classification to generate from each volume to be registered a super-resolution distance field--a scalar field that specifies, at each point, the signed distance from the point to a material boundary. The distance fields and the geometric bone model are finally used to register an object through the sequence of CT images. In the case of multiobject structures, we infer a motion-directed hierarchy from the distance-field information that allows us to register objects that are not within each other's capture region. We describe a validation framework and evaluate the new technique in contrast with grey-value registration. Results on human wrist data show average accuracy improvements of 74% over grey-value registration. The method is of interest to any intrasubject, same-modality registration applications where subvoxel accuracy is desired.

Algorithms↗
PubMed2005

Anatomical analysis of an aye-aye brain (Daubentonia madagascariensis, primates: Prosimii) combining histology, structural magnetic resonance imaging, and diffusion-tensor imaging.

This report presents initial results of a multimodal analysis of tissue volume and microstructure in the brain of an aye-aye (Daubentonia madagascariensis). The left hemisphere of an aye-aye brain was scanned using T2-weighted structural magnetic resonance imaging (MRI) and diffusion-tensor imaging (DTI) prior to histological processing and staining for Nissl substance and myelinated fibers. The objectives of the experiment were to estimate the volume of gross brain regions for comparison with published data on other prosimians and to validate DTI data on fiber anisotropy with histological measurements of fiber spread. Measurements of brain structure volumes in the specimen are consistent with those reported in the literature: the aye-aye has a very large brain for its body size, a reduced volume of visual structures (V1 and LGN), and an increased volume of the olfactory lobe. This trade-off between visual and olfactory reliance is likely a reflection of the nocturnal extractive foraging behavior practiced by Daubentonia. Additionally, frontal cortex volume is large in the aye-aye, a feature that may also be related to its complex foraging behavior and sensorimotor demands. Analysis of DTI data in the anterior cingulum bundle demonstrates a strong correlation between fiber spread as measured from histological sections and fiber spread as measured from DTI. These results represent the first quantitative comparison of DTI data and fiber-stained histology in the brain.

Animals↗
PubMed2004

Visualization and analysis of white matter structural asymmetry in diffusion tensor MRI data.

This work presents a method that permits the characterization, quantification, and 3D visualization of white matter structural information contained within diffusion tensor MR imaging (DT-MRI) data. In this method, regions within the brain are defined as possessing linear, planar, or spherical diffusion. Visualization of this diffusion metric data is realized by generating streamtube and streamsurface models to represent regions of linear and planar diffusion. Quantification of differences in diffusion anisotropy between different regions of interest (ROIs) is then achieved by analyzing 2D barycentric histograms created from the complete distribution of diffusion metric values measured in each region. In four healthy volunteers, there was only a small degree of asymmetry (epsilon) in the number of linear, planar, or spherical diffusion voxels between the right and left hemispheres (epsilon approximately equal to +/- 2%). However, in a patient with a metastatic brain lesion there was marked asymmetry in both linear (epsilon approximately -10%) and planar (epsilon approximately equal to 5%) diffusion between comparable ipsilateral and contralateral regions, with a significant reduction in the number of linear diffusion voxels and an increase in the number of planar diffusion voxels in the tumor-bearing hemisphere. These results demonstrate the potential of this approach to characterize brain structure in both healthy and diseased subjects.

Adenocarcinoma↗
PubMed2004

Estimating joint contact areas and ligament lengths from bone kinematics and surfaces.

We present a novel method for modeling contact areas and ligament lengths in articulations. Our approach uses volume images generated by computed tomography and allows the in vivo and noninvasive study of articulations. In our method, bones are modeled both implicitly (scalar distance fields) and parametrically (manifold surfaces). Using this double representation, we compute interbone distances and estimate joint contact areas. Using the same types of representation, we model ligament paths; in our model, the ligaments are approximated by the shortest paths in a three-dimensional space with bone obstacles. We demonstrate the method by applying our contact area and ligament model to the distal radioulnar joints of a volunteer diagnosed with malunited distal radius fracture in one forearm. Our approach highlights focal changes in the articulation at the distal radioulnar joint (location and area of bone contact) and potential soft-tissue constraints (increased "length" of the distal ligaments and ligament-bone impingement in the injured forearm). Results suggest that the method could be useful in the study of normal and injured anatomy and kinematics of complex joints.

Algorithms↗
PubMed2002

Fitting manifold surfaces to three-dimensional point clouds.

We present a technique for fitting a smooth, locally parameterized surface model (called the manifold surface model) to unevenly scattered data describing an anatomical structure. These data are acquired from medical imaging modalities such as CT scans or MRI. The manifold surface is useful for problems which require analyzable or parametric surfaces fitted to data acquired from surfaces of arbitrary topology (e.g., entire bones). This surface modeling work is part of a larger project to model and analyze skeletal joints, in particular the complex of small bones within the wrist and hand. To demonstrate the suitability of this model we fit to several different bones in the hand, and to the same bone from multiple people.

Carpal Bones↗
PubMedDate not supplied

Interactive volume rendering of thin thread structures within multivalued scientific data sets.

We present a threads and halos representation for interactive volume rendering of vector-field structure and describe a number of additional components that combine to create effective visualizations of multivalued 3D scientific data. After filtering linear structures, such as flow lines, into a volume representation, we use a multilayer volume rendering approach to simultaneously display this derived volume along with other data values. We demonstrate the utility of threads and halos in clarifying depth relationships within dense renderings and we present results from two scientific applications: visualization of second-order tensor valued magnetic resonance imaging (MRI) data and simulated 3D fluid flow data. In both application areas, the interactivity of the visualizations proved to be important to the domain scientists. Finally, we describe a PC-based implementation of our framework along with domain specific transfer functions, including an exploratory data culling tool, that enable fast data exploration.

Algorithms↗
PubMedDate not supplied

Comparing 2D vector field visualization methods: a user study.

We present results from a user study that compared six visualization methods for two-dimensional vector data. Users performed three simple but representative tasks using visualizations from each method: 1) locating all critical points in an image, 2) identifying critical point types, and 3) advecting a particle. Visualization methods included two that used different spatial distributions of short arrow icons, two that used different distributions of integral curves, one that used wedges located to suggest flow lines, and line-integral convolution (LIC). Results show different strengths and weaknesses for each method. We found that users performed these tasks better with methods that: 1) showed the sign of vectors within the vector field, 2) visually represented integral curves, and 3) visually represented the locations of critical points. Expert user performance was not statistically different from nonexpert user performance. We used several methods to analyze the data including omnibus analysis of variance, pairwise t-tests, and graphical analysis using inferential confidence intervals. We concluded that using the inferential confidence intervals for displaying the overall pattern of results for each task measure and for performing subsequent pairwise comparisons of the condition means was the best method for analyzing the data in this study. These results provide quantitative support for some of the anecdotal evidence concerning visualization methods. The tasks and testing framework also provide a basis for comparing other visualization methods, for creating more effective methods and for defining additional tasks to further understand the tradeoffs among the methods. In the future, we also envision extending this work to more ambitious comparisons, such as evaluating two-dimensional vectors on two-dimensional surfaces embedded in three-dimensional space and defining analogous tasks for three-dimensional visualization methods.

Algorithms↗