Frameless 3D Volume Registration of MR Data Sets For Stereotactic Pallidotomy

1997 ◽  
pp. 29-36
Author(s):  
Richard M. Lehman ◽  
R. Kumar ◽  
R. S. Mezrich ◽  
S. Negin
Keyword(s):  
Data Sets ◽  
Volume Registration ◽  
3D Volume

scholarly journals Validation of a semiautomated volumetric approach for fetal neurosonography using 5DCNS+ in clinical data from > 1100 consecutive pregnancies

2020 ◽  
Vol 36 (12) ◽  
pp. 2989-2995
Author(s):  
Amrei Welp ◽  
Michael Gembicki ◽  
Achim Rody ◽  
Jan Weichert
Keyword(s):  
Fetal Brain ◽  
Sufficient Information ◽  
Data Sets ◽  
Maternal Characteristics ◽  
Additional Information ◽  
3D Data ◽  
Manual Adjustment ◽  
Detailed Assessment ◽  
3D Volume ◽  
Cns Anomalies

Abstract Objective The aim of this study was to evaluate the validity of a semiautomated volumetric approach (5DCNS+) for the detailed assessment of the fetal brain in a clinical setting. Methods Stored 3D volumes of > 1100 consecutive 2nd and 3rd trimester pregnancies (range 15–36 gestational weeks) were analyzed using a workflow-based volumetric approach 5DCNS+, enabling semiautomated reconstruction of diagnostic planes of the fetal central nervous system (CNS). All 3D data sets were examined for plane accuracy, the need for manual adjustment, and fetal-maternal characteristics affecting successful plane reconstruction. We also examined the potential of these standardized views to give additional information on proper gyration and sulci formation with advancing gestation. Results Based on our data, we were able to show that gestational age with an OR of 1.085 (95% CI 1.041–1.132) and maternal BMI with an OR of 1.022 (95% CI 1.041–1.054) only had a slight impact on the number of manual adjustments needed to reconstruct the complete volume, while maternal age and fetal position during acquisition (p = 0.260) did not have a significant effect. For the vast majority (958/1019; 94%) of volumes, using 5DCNS+ resulted in proper reconstruction of all nine diagnostic planes. In less than 1% (89/9171 planes) of volumes, the program failed to give sufficient information. 5DCNS+ was able to show the onset and changing appearance of CNS folding in a detailed and timely manner (lateral/parietooccipital sulcus formation seen in < 65% at 16–17 gestational weeks vs. 94.6% at 19 weeks). Conclusions The 5DCNS+ method provides a reliable algorithm to produce detailed, 3D volume–based assessments of fetal CNS integrity through a standardized reconstruction of the orthogonal diagnostic planes. The method further gives valid and reproducible information regarding ongoing cortical development retrieved from these volume sets that might aid in earlier in utero recognition of subtle structural CNS anomalies.

3D volume segmentation of MRA data sets using level sets

2004 ◽  
Vol 11 (4) ◽  
pp. 419-435 ◽  
Author(s):  
Aly A. Farag ◽  
Hossam Hassan ◽  
Robert Falk ◽  
Stephen G. Hushek
Keyword(s):  
Level Sets ◽  
Data Sets ◽  
Volume Segmentation ◽  
3D Volume

scholarly journals Tensor volume exploration using attribute space representatives

2020 ◽  
Author(s):  
Jochen Jankowai ◽  
Robin Skånberg ◽  
Daniel Jönsson ◽  
Anders Ynnerman ◽  
Ingrid Hotz
Keyword(s):  
Transfer Function ◽  
Volume Rendering ◽  
Tensor Field ◽  
Complex Geometry ◽  
Scalar Fields ◽  
Data Sets ◽  
Tensor Fields ◽  
Directional Information ◽  
3D Volume ◽  
Attribute Space

While volume rendering for scalar fields has been advanced into a powerful visualisation method, similar volumetric representations for tensor fields are still rare. The complexity of the data challenges not only the rendering but also the design of the transfer function. In this paper we propose an interface using glyph widgets to design a transfer function for the rendering of tensor data sets. Thereby the transfer function (TF) controls a volume rendering which represents sought after tensor-features and a texture that conveys directional information. The basis of the design interface is a two-dimensional projection of the attribute space. Characteristicrepresentatives in the form of glyphs support an intuitive navigation through the attribute space. We provide three different options to select the representatives: automatic selection based on attribute space clustering, uniform sampling of the attribute space, or manually selected representatives. In contrast to glyphs placed into the 3D volume, we use glyphs with complex geometry as widgets to control the shape and extent of the representatives. In the final rendering the glyphs with their assigned colors play a similar role as a legend in an atlas like representation. The method provides an overview of the tensor field in the 3D volume at the same time as it allows the user to explore the tensor field in an attribute space. We demonstrate the flexibility of our approach on tensor fields for selected data sets with very different characteristics.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

Computer-aided methods for 3-D visualization of serial sections and thick biological specimens

1992 ◽  
Vol 50 (2) ◽  
pp. 1060-1061
Author(s):  
Mark Ellisman ◽  
Maryann Martone ◽  
Gabriel Soto ◽  
Eleizer Masliah ◽  
David Hessler ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Three Dimensional ◽  
Neuritic Plaque ◽  
Dimensional Structure ◽  
Data Sets ◽  
Molecular Physiology ◽  
Research Activities ◽  
Computer Aided ◽  
Dimensional Reconstruction

Structurally-oriented biologists examine cells, tissues, organelles and macromolecules in order to gain insight into cellular and molecular physiology by relating structure to function. The understanding of these structures can be greatly enhanced by the use of techniques for the visualization and quantitative analysis of three-dimensional structure. Three projects from current research activities will be presented in order to illustrate both the present capabilities of computer aided techniques as well as their limitations and future possibilities.The first project concerns the three-dimensional reconstruction of the neuritic plaques found in the brains of patients with Alzheimer's disease. We have developed a software package “Synu” for investigation of 3D data sets which has been used in conjunction with laser confocal light microscopy to study the structure of the neuritic plaque. Tissue sections of autopsy samples from patients with Alzheimer's disease were double-labeled for tau, a cytoskeletal marker for abnormal neurites, and synaptophysin, a marker of presynaptic terminals.

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