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Part 1 Introduction

1 Tensor has three intrinsic properties, i.e., scale + shape + orientation 2 Glyph visualization:

(a) Spherical tensor with varying scale alone

(b) Tensors with gradually varying shape alone

(c) Tensors with gradually varying orientation alone Fig 1 Tensor three intrinsic properties

3 The task is to analyze a group of diffusion tensors.

There are two types of analysis &visualization (demo) approaches.

Euclidean Mean + 4th-order Covariance Tensor/Eigen-tensor + Invariant Gradients and Rotation Tangents decomposition

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Figure 2 Screenshot of Covariance-based Visualization

Our mean tensor glyph (encode each tensor property separately) and mean dODF glyph (directly model the diffusion PDF)

Figure 3 Our tensor variation glyph and dODF glyph

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Part 2 Ensemble Exploration

Name: Task: Visualization:

(1) What tensor shapes do you think are present in the ensemble? Mark them on the triangle.

(2) Is scale varying in this ensemble?

☐Yes ☐No ☐Don’t know

(3) Describe the orientation variations.

☐No variation ☐Yes, there is ☐ Don’t know

Could you also tell the major direction(s) that has/have large diffusion variations?

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(4) Is there a correlation between

☐No correlation

☐scale-orientation

☐scale-shape

☐shape-orientation

☐scale-shape-orientation

☐Don’t know

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Part 3 Open Questions

(1) How useful do you think is way to analyze a group of tensors in scale, shape, and orientation separately (i.e., derive the mean and quantify the variations)?

☐Very ☐Moderately ☐Slightly ☐Not at all

(2) How intuitive is our glyph design? Is

☐Very ☐Moderately ☐Slightly ☐Not at all

(3) How useful do you think the information provided by dODF?

☐Very ☐Moderately ☐Slightly ☐Not at all

(4) Which visualization do you prefer for analyzing the tensor ensemble?

Could you shortly state why?

☐Neither ☐Covariance Tensor ☐Our Method ☐Both

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(a)

(b) (c) (d)

(e)

(g) (h)

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1 2 3

4 5 6

1 2

3

y x z

y x z

x y

z x

y z

x y z

x y z

1 2

3 4

(i)

(j) (k) (l)

The comparison of visualization for three synthetic ensem-

bles:

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