Tract Orientation and Angular Dispersion Deviation Indicator (TOADDI): A framework for single-subject analysis in diffusion tensor imaging
Cheng Guan Koay, Ping-Hong Yeh, John M. Ollinger, M. Okan, \.Irfano\u{g}lu, Carlo Pierpaoli, Peter J. Basser, Terrence R. Oakes, Gerard, Riedy

TL;DR
TOADDI is a novel framework for single-subject diffusion tensor imaging analysis that tests for significant differences in tract orientation and shape compared to a control group, aiding in clinical assessments.
Contribution
This work introduces TOADDI, a new geometrically based statistical framework for single-subject DTI analysis using elliptical cones of uncertainty.
Findings
TOADDI accurately detects tract deviations in TBI patients.
The framework effectively separates TBI patients from controls.
Application shows significant differences in the superior longitudinal fasciculus.
Abstract
The purpose of this work is to develop a framework for single-subject analysis of diffusion tensor imaging (DTI) data. This framework (termed TOADDI) is capable of testing whether an individual tract as represented by the major eigenvector of the diffusion tensor and its corresponding angular dispersion are significantly different from a group of tracts on a voxel-by-voxel basis. This work develops two complementary statistical tests based on the elliptical cone of uncertainty (COU), which is a model of uncertainty or dispersion of the major eigenvector of the diffusion tensor. The orientation deviation test examines whether the major eigenvector from a single subject is within the average elliptical COU formed by a collection of elliptical COUs. The shape deviation test is based on the two-tailed Wilcoxon-Mann-Whitney two-sample test between the normalized shape measures (area and…
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