Uncertainty Visualization in HARDI based on Ensembles of ODFs.
In this paper, we propose a new and accurate technique for uncertainty analysis and uncertainty visualization based on fiber orientation distribution function (ODF) glyphs, associated with high angular resolution diffusion imaging (HARDI). Our visualization applies volume rendering techniques to an ensemble of 3D ODF glyphs, which we callSIP functionsofdiffusion shapes, to capture their variability due to underlying uncertainty. This rendering elucidates the complex heteroscedastic structural variation in these shapes. Furthermore, we quantify the extent of this variation by measuring the fraction of the volume of these shapes, which is consistent across all noise levels, thecertain volume ratio. Our uncertainty analysis and visualization framework is then applied to synthetic data, as well as to HARDI human-brain data, to study the impact of various image acquisition parameters and background noise levels on the diffusion shapes.
SCI Institute ; University of Utah.
Fangxiang
F
Jiao
University of Utah.
Jeff M
JM
Phillips
SCI Institute ; University of Utah.
Yaniv
Y
Gur
SCI Institute ; University of Utah.
Chris R
CR
Johnson
DT-MRI; HARDI; Rank-k tensor decomp; Uncertainty