Introduction to Diffusion MRI data

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Figure 1

Image 1 of 1: ‘Diffusion along different directions’

Diffusion along X, Y, and Z directions. The signal in the left/right oriented corpus callosum is lowest when measured along X, while the signal in the inferior/superior oriented corticospinal tract is lowest when measured along Z.

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Figure 2

Image 1 of 1: ‘DWI slice’

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Figure 3

Image 1 of 1: ‘Diffusion gradient sphere’

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Preprocessing dMRI data

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Figure 1

Image 1 of 1: ‘Preprocessing steps’

To illustrate what the preprocessing step may look like, here is an example preprocessing workflow from QSIPrep (Cieslak et al, 2020):

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Figure 2

Image 1 of 1: ‘b0 brainmask’

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Figure 3

Image 1 of 1: ‘Blip up and blip down pairs’

Opposite phase-encodings from two DWI

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Figure 4

Image 1 of 1: ‘Topup image’

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Figure 5

Image 1 of 1: ‘T1w brainmask’

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Figure 6

Image 1 of 1: ‘Transformed volumes’

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Local fiber orientation reconstruction

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Diffusion Tensor Imaging (DTI)

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Figure 1

Image 1 of 1: ‘Diffusion signal equation’

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Figure 2

Image 1 of 1: ‘Diffusivity matrix’

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Figure 3

Image 1 of 1: ‘Diffusion tensor’

Adapted from Jelison et al., 2004

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Figure 4

Image 1 of 1: ‘FA equation’

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Figure 5

Image 1 of 1: ‘FA plot’

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Figure 6

Image 1 of 1: ‘MD equation’

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Figure 7

Image 1 of 1: ‘MD plot’

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Figure 8

Image 1 of 1: ‘Axial and radial diffusivities’

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Figure 9

Image 1 of 1: ‘RGB FA map’

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Figure 10

Image 1 of 1: ‘Tensor visualization’

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Figure 11

Image 1 of 1: ‘DTI drawbacks’

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Figure 12

Image 1 of 1: ‘Axial diffusivity map’

Axial diffusivity map.

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Figure 13

Image 1 of 1: ‘Radial diffusivity map’

Radial diffusivity map.

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Constrained Spherical Deconvolution (CSD)

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Figure 1

Image 1 of 1: ‘Spherical deconvolution equation’

The basic equations of an SD method can be summarized as
Spherical deconvolution

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Figure 2

Image 1 of 1: ‘Fiber Response Function (FRF)’

Estimated response function

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Figure 3

Image 1 of 1: ‘CSD ODFs’

CSD ODFs.

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Figure 4

Image 1 of 1: ‘CSD peaks’

CSD Peaks.

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Figure 5

Image 1 of 1: ‘CSD peaks and fODFs’

CSD Peaks and ODFs.

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Figure 6

Image 1 of 1: ‘ODFs of differing crossing angles’

ODFs of different crossing angles.

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Tractography

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Local tractography

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Figure 1

Image 1 of 1: ‘Streamline propagation equation’

Streamline propagation is, in essence, a numerical analysis integration problem. The problem lies in finding a curve that joins a set of discrete local directions. As such, it takes the form of a differential equation problem of the form:
Streamline propagation differential equation

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Deterministic tractography

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Figure 1

Image 1 of 1: ‘FA’

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Figure 2

Image 1 of 1: ‘EuDX Determinsitic Tractography’

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Figure 3

Image 1 of 1: ‘Binary Stopping Criterion Tractography’

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Figure 4

Image 1 of 1: ‘FA Mapped Tractography’

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Probabilistic tractography

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Figure 1

Image 1 of 1: ‘GFA’

GFA

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Figure 2

Image 1 of 1: ‘PMF direction getter-derived probabilistic tractogram’

Streamlines representing white matter using probabilistic direction getter from PMF

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Figure 3

Image 1 of 1: ‘SH direction getter-derived probabilistic tractogram’

Streamlines representing white matter using probabilistic direction getter from SH

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Figure 4

Image 1 of 1: ‘CSD model peaks for tracking’

Peaks obtained from the CSD model for tracking purposes

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Figure 5

Image 1 of 1: ‘PMF SH direction getter-derived probabilistic tractogram’

Streamlines representing white matter using probabilistic direction getter from SH (peaks_from_model)

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