Paul Thompson's Research Publications
Tensor Visualization of Brain Variability
SIGGRAPH Computer Graphics Conference, 2001, Los Angeles, CA
John Bacheller, Paul M. Thompson, Colin J. Holmes, Arthur W. Toga
1Laboratory of Neuro Imaging, Department of
Neurology, Division of
Brain Mapping, UCLA School of Medicine, Los Angeles, California
Description. No two brains are exactly alike, and
brain structure varies dramatically even among normal individuals. To visualize
such variability, our brain mapping research group at the UCLA Laboratory of
Neuro Imaging developed
novel supercomputing algorithms to compute and visualize
how brain structure varies across normal individuals,
based on a database of MRI scans from 20 subjects. In these
images, the ellipsoids represent the amount of anatomical
variation across subjects. Pink colors show greatest anatomical variation in the most uniquely
human brain areas, controlling language and logical reasoning. The blue spheres
in the middle represent brain areas with little interpersonal variability; these
control sensation and motor function. Ellipsoids are also most elongated along
directions in which variability is greatest. The bubble-like rendering plots a
probability field, indicating through clustering and varying levels of
transparency how likely it is to find a brain structure in each coordinate
location in a normal population.
Novelty. These tensor visualizations are novel, as they
draw upon high-resolution brain imaging, as well as tensor concepts from Gaussian field theory and
fluid dynamics, to help determine when a brain region in a new
subject is abnormally situated, or whether such a variation is normal. This helps
in the early diagnosis of Alzheimer's Disease and schizophrenia,
and in the evaluation of new treatment approaches. The application area is novel, as it
is the first time brain variations in an image database have been encoded for
disease detection in new subjects.
Computation. Novel mathematics and advanced
rendering power are both indispensable in visualizing such complex tensor datasets.
In addition to the medical applications, these tensor images are computed from mathematical mappings
with 0.9 trillion parameters and rendered on a Silicon Graphics RealityMonster
Supercomputer with thirty two MIPS R10000 CPUs, requiring 9 hours parallel CPU time. The navigation
and visualization of these high-dimensional datasets represents a key challenge in
biomedical computing today, and the resulting visualizations are
empowering new diagnostic applications in neuroscience and medicine.
More about Tensors
AN ALZHEIMER'S DISEASE BRAIN ATLAS
Disease-Specific Brain Atlases
other research areas
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Paul Thompson, Ph.D.
Assistant Professor of Neurology
Laboratory of Neuro Imaging and Division of Brain Mapping
710 Westwood Plaza, UCLA School of Medicine
Westwood, Los Angeles CA 90095-1761, USA.