KEYWORDS: Diffusion, Tissues, Data modeling, Diffusion tensor imaging, Diffusion weighted imaging, Received signal strength, 3D modeling, Signal to noise ratio, Image segmentation, Microscopy
One aim of this work is to investigate the feasibility of using a hierarchy of models to describe diffusion tensor
MRI data. Parsimonious model selection criteria are used to choose among different models of diffusion within
tissue. Second, based on this information, we assess whether we can perform simultaneous tissue segmentation
and classification. The proposed hierarchical framework used for parsimonious model selection is based on the
F-test, adapted from Snedecor.
Diffusion Magnetic Resonance Microscopy (MRM) provides near-microscopic resolution without relying on
a sample's optical transparency for image formation. Diffusion MRM is a noninvasive imaging technique for
quantitative analysis of intrinsic features of tissues. Thus, we propose using Diffusion MRM to characterize
normal tissue structure in adult zebrafish, and possibly subtle anatomical or structural differences between
normals and knockouts.
Both numerical phantoms and diffusion weighted image (DWI) data obtained from adult zebrafish are used
to test this model selection framework.
Interactive visualization of multi-dimensional biological images has revolutionized diagnostic and therapy planning. Extracting complementary anatomical and functional information from different imaging modalities provides a synergistic analysis capability for quantitative and qualitative evaluation of the objects under examination. We have been developing NIHmagic, a visualization tool for research and clinical use, on the SGI OnyxII Infinite Reality platform. Images are reconstructed into a 3D volume by volume rendering, a display technique that employs 3D texture mapping to provide a translucent appearance to the object. A stack of slices is rendered into a volume by an opacity mapping function, where the opacity is determined by the intensity of the voxel and its distance from the viewer. NIHmagic incorporates 3D visualization of time-sequenced images, manual registration of 2D slices, segmentation of anatomical structures, and color-coded re-mapping of intensities. Visualization of MIR, PET, CT, Ultrasound, and 3D reconstructed electron microscopy images has been accomplished using NIHmagic.
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