Detecting subtle changes in structure
Download
1 / 14

Detecting Subtle Changes in Structure - PowerPoint PPT Presentation


  • 130 Views
  • Uploaded on

Detecting Subtle Changes in Structure. Chris Rorden Diffusion Tensor Imaging Measuring white matter integrity Tractography and analysis. Diffusion weighted images measures random motion of water molecules. In ventricles, CSF is unconstrained, so high velocity diffusion

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Detecting Subtle Changes in Structure' - cleo


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Detecting subtle changes in structure
Detecting Subtle Changes in Structure

  • Chris Rorden

    • Diffusion Tensor Imaging

      • Measuring white matter integrity

      • Tractography and analysis.


Diffusion weighted imaging

Diffusion weighted images measures random motion of water molecules.

In ventricles, CSF is unconstrained, so high velocity diffusion

In brain tissue, water more constrained, so less diffusion.

Diffusion Weighted Imaging

DWI


Diffusion tensor imaging dti

DTI is an extension of DWI that allows us to measure direction of motion.

DTI allows us to measure both the velocity and preferred direction of diffusion

In gray matter, diffusion is isotropic (similar in all directions)

In white matter, diffusion is anisotropic (prefers motion along fibers).

Diffusion Tensor Imaging (DTI)


The amount of diffusion occurring in one pixel of a MR image is termed the Apparent Diffusion Coefficient (ADC) or Mean Diffusivity (MD).

The non-uniformity of diffusion with direction is usually described by the term Fractional Anisotropy (FA).

DTI

MD differs

FA differs


Raw dti data
Raw DTI data is termed the Apparent Diffusion Coefficient (ADC) or Mean Diffusivity (MD).

  • DTI scans apply gradients of different strengths (b-values) and directions (b-vectors).

  • These highlight different diffusion directions.

  • Simplest DTI is shown below: one b=0 image (no directionality), plus six images with b=1000 but each with a different b-vector.


Eddy current correction
Eddy current correction is termed the Apparent Diffusion Coefficient (ADC) or Mean Diffusivity (MD).

  • Gradients for DTI data cause spatial distortions.

  • Different directions have different distortions.

  • The B0 image has little distortion (it is a T2 weighted Image)

  • Eddy current correction aligns all directions to the b0 (reference) image.

  • Analogous to motion correction for fMRI data.


What is a tensor

A tensor is composed of three vectors. is termed the Apparent Diffusion Coefficient (ADC) or Mean Diffusivity (MD).

Think of a vector like an arrow in 3D space – it points in a direction and has a length.

The first vector is the longest – it points along the principle axis.

The second and third vectors are orthogonal to the first.

What is a tensor?

Sphere: V1=V2=V3

Football:

V1>V2

V1>V3

V3 = V2

???:

V1>V2>V3


MD is termed the Apparent Diffusion Coefficient (ADC) or Mean Diffusivity (MD).

DTI

V1 modulated by FA

Color shows principle tensor direction, brightness shows FA

FA


The crossing fibers problem
The crossing fibers problem is termed the Apparent Diffusion Coefficient (ADC) or Mean Diffusivity (MD).

Reality

  • Tensor (DTI) will have trouble distinguishing voxels with crossing fibers from isotropic regions.

Tensors

CSF (isotropic)

Crossing fibers


Tractography
Tractography is termed the Apparent Diffusion Coefficient (ADC) or Mean Diffusivity (MD).

  • Programs like medInria allow us to measure integrity of connections between different regions.


Statistics md and fa
Statistics – MD and FA is termed the Apparent Diffusion Coefficient (ADC) or Mean Diffusivity (MD).

  • The FA and MD maps from each individual can be normalized to standard space.

  • Standard voxelwise statistics applied (like VBM)

  • Allows us to infer differences in white matter integrity between groups.

Group mean images based on normalized data.


Statistics tensors
Statistics – tensors is termed the Apparent Diffusion Coefficient (ADC) or Mean Diffusivity (MD).

  • You can measure fiber strength connectivity brain regions and see if this differs between groups.

  • Traditionally, tensor maps are very difficult to normalize – we do not want to squish relative shape of tensor.

    • Recent advances are addressing this (DTITK)


Tbss tract based spatial statistics
TBSS - Tract-Based Spatial Statistics is termed the Apparent Diffusion Coefficient (ADC) or Mean Diffusivity (MD).

  • TBSS is a FSL approach to conduct between group comparisons of DTI data.

  • projects data onto group-mean tract skeleton, allowing voxelwise analysis

  • addresses alignment problems unsolved by nonlinear registration

  • Overview www.fmrib.ox.ac.uk/fsl/tbss/index.html

  • Tutorial www.fmrib.ox.ac.uk/fslcourse/lectures/practicals/fdt/index.htm


Sample application
Sample application is termed the Apparent Diffusion Coefficient (ADC) or Mean Diffusivity (MD).

  • Young adults vary in their ability to recollect information.

  • TBSS shows that Fornix integrity predicts this variability (but not variability in familiarity).

Rudebeck (2009) JoN


ad