BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

Anisotropic water diffusion in neural fibres such as nerve, white matter in spinal cord, or white matter in brain forms the basis for the utilization of diffusion tensor imaging (DTI) to track fibre pathways. The fact that water diffusion is sensitive to the underlying tissue microstructure provides a unique method of assessing the orientation and integrity of these neural fibres, which may be useful in assessing a number of neurological disorders. The purpose of this review is to characterize the relationship of nuclear magnetic resonance measurements of water diffusion and its anisotropy (i.e. directional dependence) with the underlying microstructure of neural fibres. The emphasis of the review will be on model neurological systems both in vitro and in vivo. A systematic discussion of the possible sources of anisotropy and their evaluation will be presented followed by an overview of various studies of restricted diffusion and compartmentation as they relate to anisotropy. Pertinent pathological models, developmental studies and theoretical analyses provide further insight into the basis of anisotropic diffusion and its potential utility in the nervous system.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/nbm.782

The basis of anisotropic water diffusion in the nervous system – a technical review

The state of the art of reconstruction of the axonal tracts in the central nervous system (CNS) using diffusion tensor imaging (DTI) is reviewed. This relatively new technique has generated much enthusiasm and high expectations because it presently is the only approach available to non-invasively study the three-dimensional architecture of white matter tracts. While there is no doubt that DTI fiber tracking is providing exciting new opportunities to study CNS anatomy, it is very important to understand its limitations. In this review we therefore assess the basic principles and the assumptions that need to be made for each step of the study, including both data acquisition and the elaborate fiber reconstruction algorithms. Special attention is paid to situations where complications may arise, and possible solutions are reviewed. Validation issues and potential future directions and improvements are also discussed.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/nbm.781

Fiber tracking: Principles and strategies - A technical review

Because of their wide use in molecular modeling, methods to compute molecular surfaces have received a lot of interest in recent years. However, most of the proposed algorithms compute the analytical representation of only the solvent-accessible surface. There are a few programs that compute the analytical representation of the solvent-excluded surface, but they often have problems handling singular cases of self-intersecting surfaces and tend to fail on large molecules (more than 10,000 atoms). We describe here a program called MSMS, which is shown to be fast and reliable in computing molecular surfaces. It relies on the use of the reduced surface that is briefly defined here and from which the solvent-accessible and solvent-excluded surfaces are computed. The four algorithms composing MSMS are described and their complexity is analyzed. Special attention is given to the handling of self-intersecting parts of the solvent-excluded surface called singularities. The program has been compared with Connolly's program PQMS [M.L. Connolly (1993) Journal of Molecular Graphics, Vol. 11, pp. 139-141] on a set of 709 molecules taken from the Brookhaven Data Base. MSMS was able to compute topologically correct surfaces for each molecule in the set. Moreover, the actual time spent to compute surfaces is in agreement with the theoretical complexity of the program, which is shown to be O[n log(n)] for n atoms. On a Hewlett-Packard 9000/735 workstation, MSMS takes 0.73 s to produce a triangulated solvent-excluded surface for crambin (1 crn, 46 residues, 327 atoms, 4772 triangles), 4.6 s for thermolysin (3tln, 316 residues, 2437 atoms, 26462 triangles), and 104.53 s for glutamine synthetase (2gls, 5676 residues, 43632 atoms, 476665 triangles).

Reduced surface: An efficient way to compute molecular surfaces

Two- and three-dimensional (3D) white matter atlases were created on the basis of high-spatial-resolution diffusion tensor magnetic resonance (MR) imaging and 3D tract reconstruction. The 3D trajectories of 17 prominent white matter tracts could be reconstructed and depicted. Tracts were superimposed on coregistered anatomic MR images to parcel the white matter. These parcellation maps were then compared with coregistered diffusion tensor imaging color maps to assign visible structures. The results showed (a) which anatomic structures can be identified on diffusion tensor images and (b) where these anatomic units are located at each section level and orientation. The atlas may prove useful for educational and clinical purposes. © RSNA, 2003

Fiber tract-based atlas of human white matter anatomy.

We describe work toward creating color rapid prototyping (RP) plaster models as visualization tools to support scientific research in diffusion-tensor (DT) MRI analysis. We currently give surgeons and neurologists virtual-reality (VR) applications to visualize different aspects of their brain data, but having physical representations of those virtual models allows them to review the data with a very robust, natural, and fast haptic interface: their own hands. Our initial results are encouraging, and end users are excited about the possibilities of this technique. For example, using these models in conjunction with digital models on the computer screen or VR environment provides a static frame of reference that helps keep users oriented during their analysis tasks.

/pdf/color-rapid-prototyping-for-diffusion-tensor-mri-4gtkam68o0.pdf

Color Rapid Prototyping for Diffusion-Tensor MRI Visualization

Material classification is a key stop in creating computer graphics models and images from volume data, We present a new algorithm for identifying the distribution of different material types in volumetric datasets such as those produced with Magnetic Resonance Imaging (NMI) or Computed Tomography (CT). The algorithm assumes that voxels can contain more than one material, e.g. both muscle and fat; we wish to compute the relative proportion of each material in the voxels. Other classification methods have utilized Gaussian probability density functions to model the distribution of values within a dataset. These Gaussian basis functions work well for voxels with unmixed materials, but do not work well where the materials are mixed together. We extend this approach by deriving non-Gaussian "mixture" basis functions. We treat a voxel as a volume, not as a single point. We use the distribution of values within each voxel-sized volume to identify materials within the voxel using a probabilistic approach. The technique reduces the classification artifacts that occur along boundaries between materials. The technique is useful for making higher quality geometric models and renderings from volume data, and has the potential to make more accurate volume measurements. It also classifies noisy, low-resolution data well.

Classification of Material Mixtures in Volume Data for Visualization and Modeling

This chapter provides an overview of the variables that have been considered in the controlled and semi-controlled experiments for studying phenomena in visualization. As all controlled and semi-controlled experiments have explicitly defined independent variables, dependent variables, extraneous variables, and operational variables, a survey of these variables allows us to gain a broad prospect of a major aspect of the design space for empirical studies in visualization.

A Survey of Variables Used in Empirical Studies for Visualization

We report empirical study results on the color encoding of ensemble scalar and orientation to visualize diffusion magnetic resonance imaging (DMRI) tubes. The experiment tested six scalar colormaps for average fractional anisotropy (FA) tasks (grayscale, blackbody, diverging, isoluminant-rainbow, extended-blackbody, and coolwarm) and four three-dimensional (3D) spherical colormaps for tract tracing tasks (uniform gray, absolute, eigenmaps, and Boy's surface embedding). We found that extended-blackbody, coolwarm, and blackbody remain the best three approaches for identifying ensemble average in 3D. Isoluminant-rainbow colormap led to the same ensemble mean accuracy as other colormaps. However, more than 50 percent of the answers consistently had higher estimates of the ensemble average, independent of the mean values. The number of hues, not luminance, influences ensemble estimates of mean values. For ensemble orientation-tracing tasks, we found that both Boy's surface embedding (greatest spatial resolution and contrast) and absolute colormaps (lowest spatial resolution and contrast) led to more accurate answers than the eigenmaps scheme (medium resolution and contrast), acting as the uncanny-valley phenomenon of visualization design in terms of accuracy. Absolute colormap broadly used in brain science is a good default spherical colormap. We could conclude from our study that human visual processing of a chunk of colors differs from that of single colors.

Measuring the Effects of Scalar and Spherical Colormaps on Ensembles of DMRI Tubes

We present and compare two low-dimensional visual representations, 2D point and 2D path, for studying tractography datasets. The goal is to facilitate the exploration of dense tractograms by reducing visual complexity both in static representations and during interaction. The proposed planar maps have several desirable properties, including visual clarity, easy tract-of-interest selection, and multiscale hierarchy. The 2D path representations convey the anatomical familiarity of 3D brain models and cross-sectional views. We demonstrate the utility of both types of representation in two interactive systems where the views and interactions of the standard 3D streamtube representation are linked to those of the planar representations. We also demonstrate a web interface that integrates precomputed neural-path representations into a geographical digital-maps framework with associated labels, metrics, statistics, and linkouts. We compare the two representations both anecdotally and quantitatively via expert input. Results indicate that the planar path representation is more intuitive and easier to use and learn. Similarly, users are faster and more accurate in selecting bundles using the path representation than the 2D point representation. Finally, expert feedback on the web interface suggests that it can be useful for collaboration as well as quick exploration of data.

David H. Laidlaw

Papers

Color Rapid Prototyping for Diffusion-Tensor MRI Visualization

Classification of Material Mixtures in Volume Data for Visualization and Modeling

A Survey of Variables Used in Empirical Studies for Visualization

Measuring the Effects of Scalar and Spherical Colormaps on Ensembles of DMRI Tubes

Exploring Brain Connectivity with Two-Dimensional Maps