@inproceedings{Laidlaw:1998:VDT,
opteditor = {},
optpostscript = {},
optorganization = {},
author = {David H. Laidlaw and Eric T. Ahrens and David Kremers and Matthew J.
Avalos and Russell E. Jacobs and Carol Readhead},
optkey = {},
optannote = {},
optseries = {},
url = {http://dx.doi.org/10.1109/VISUAL.1998.745294},
address = IEEEAdr,
localfile = {papers/Laidlaw.1998.VDT.pdf},
optisbn = {},
publisher = IEEEPub,
optkeywords = {},
doi = {http://doi.ieeecomputersociety.org/10.1109/VISUAL.1998.745294},
optmonth = {},
citeseer = {http://citeseer.ist.psu.edu/laidlaw98visualizing.html},
optcrossref = {},
optwww = {},
booktitle = {Proceedings of the 1998 IEEE Conference on Visualization
(VIS'98)},
optvolume = {},
optnumber = {},
abstract = {Within biological systems water molecules undergo continuous
stochastic Brownian motion. The rate of this diffusion can give
clues to the structure of underlying tissues. In some tissues the
rate is anisotropic — faster in some directions than others.
Diffusion-rate images are second-order tensor fields and can be
calculated from diffusion-weighted magnetic resonance images. A 2D
diffusion tensor image (DTI) and an associated anatomical scalar
field, created during the tensor calculation, de.ne seven values
at each spatial location. Visually representing these images is a
challenge because they contain so many inter-related components.
We present two new methods for visually representing DTIs. The
first method displays an array of ellipsoids where the shape of
each ellipsoid represents one tensor value. The novel aspect of
this representation is that the ellipsoids are all normalized to
approximately the same size so that they can be displayed
simultaneously in context. The second method uses concepts from
oil painting to represent the seven-valued data with multiple
layers of varying brush strokes. Both methods successfully display
most or all of the information in DTIs and provide exploratory
methods for understanding them. The ellipsoid method has a simpler
interpretation and explanation than the painting-motivated method;
the painting-motivated method displays more of the information and
is easier to read quantitatively. We demonstrate the methods on
images of the mouse spinal cord. The visualizations show
significant differences between spinal cords from mice suffering
from Experimental Allergic Encephalomyelitis (EAE) and spinal
cords from wild-type mice. The differences are consistent with
differences shown histologically and suggest that our new
non-invasive imaging methodology and visualization of the results
could have early diagnostic value for neurodegenerative diseases.
},
title = {{V}isualizing {D}iffusion {T}ensor {I}mages of the {M}ouse {S}pinal
{C}ord},
year = {1998},
pages = {127--134},
}
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