diffusion tensor mri and fiber tacking presented by: eng. inas yassine. i.yassine@k-space.org

Diffusion Tensor MRI And Fiber Tacking

Presented By:

Eng. Inas Yassine.

i.yassine@k-space.org

204/18/23

Outline Nuclear Magnetic resonance (NMR)

Diffusion Tensor Imaging (DTI)

DTI-Based Connectivity Mapping

Research in DTI

Nuclear Magnetic Resonance

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Nuclear Magnetic Resonance Magnetic resonance phenomenon

Nucleus acts as a tiny bar magnet In the absence of external magnetic field, such tiny

magnets are arranged randomly Net magnetization at steady state = 0

In the presence of a strong magnet field, tiny magnets tend to align along the field Probability can be computed from Boltzmann ratio Nonzero net magnetization results

Precession frequency given by Larmor Eqn.

= B0

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Nuclear Magnetic Resonance Injection of Radio frequency pulse

Free Induction decay Rotating frame of reference T1 relaxation T2 relaxation

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Nuclear Magnetic Resonance MR gradients Slice Selection Multiple slices

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Nuclear Magnetic Resonance κ-space

Composed of sines and cosines Detail and frequency relation

Image Reconstruction

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MRI machines MRI Types

Conventional MRI Open MRI (Regular, Standup)

MRI Scans T1 and T2 Weighted Functional Diffusion HARP GRAPPA

Diffusion Tensor Imaging

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Diffusion Tensor Imaging (DTI) MR signal is attenuated as a result of diffusion

Brownian motion in the presence of gradients

Gradients are applied in different directions and attenuation is measured DTI reconstruction using linear system solution

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Diffusion Tensor Imaging (DTI) Mobility in a given direction is described by ADC The tissue diffusivity is described by the tensor D The diffusion equation

Diffusion is represented by a 33 tensor*

ADCbexpDxbexp)0(A)b(A

nAttenuatio3,2,1j,i

ijij

zzyzxz

yzyyxy

xzxyxx

DDD

DDD

DDD

D

3

2

1

00

00

00

diagD

*P. Basser and D. Jone, NMR in Biomedicine, 2002.

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Diffusion Tensor Imaging (DTI) Brain Tissue types

Cerebrospinal fluid (CSF) Gray matter (GM) White matter (WM) Mixing of tissues

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Diffusion Tensor Imaging (DTI) Invariant Anisotropy Indices

Fractional Anisotropy (FA)*

Relative Anisotropy (RA)*

Volume Ratio (VR)*

)(2

])()()[(323

22

21

23

22

21

FA

3

])()()[( 23

22

21

RA

3

321

VR

*D. LeBihan, NMR Biomedicine., 2002.

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Diffusion Tensor Imaging (DTI) Single Tensor estimation

Estimation of direction is severely affected in the presence of noise*

*X. Ma, Y. Kadah, S. LaConte, and X. Hu, Magnetic Resonance in Medicine, 2004.

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Source of noise in diffusion MRI Statistical Noise

Magnetic Filed unhomogeneity Eddy currents Thermal Noise background signals caused by precessing tissue magnetization.

Systematic Noise from a number of patient motion, such as respiration, vascular, and CSF

pulsations; receiver-coil or gradient-coil motion; aliasing; and data truncation

(Gibbs) artifacts

DTI-Based Connectivity Mapping

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DTI-Based Connectivity Mapping Nerve fibers represent cylindrical-shaped

physical spaces with membrane acting like a barrier DT shows diffusion preference along axon

Measuring the diffusing anisotropy, we can estimate the dominant direction of the nerve bundle passing through each voxel

*X. Ma, Y. Kadah, S. LaConte, and X. Hu, Magnetic Resonance in Medicine, 2004.

2004/18/23

DTI-Based Connectivity Mapping*

Line Propagation Algorithms. Global energy Minimization

Fast Marching Technique Simulated annealing approach

*S.. Mori et. al, NMR IN BIOMEDICINE, 2002.

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Limitations*

Inaccuracy of Single Tensor Estimation due to Intravoxel Orientational Heterogeneity (IVOH). Contribution from multiple tensors.

Limited signal to noise Ratio. There may not be a single predominant direction of water

diffusion. Afferent and efferent pathways of axonal fiber tracts cannot be

judged.

*S.. Mori et. al, NMR IN BIOMEDICINE, 2002.

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Problem Statement Partial voluming is common in practice Attenuation due to multi-component diffusion when

applying a gradient defined by the diffusion direction is given by,

It is required to estimate the component tensor and their partial volume ratios 13 unknown for a 2-component model without loss of generality. Nonlinear equations unlike 1-component case

Ii

iT

i xDxbxE

exp)(

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Orientation Distribution function

2404/18/23

Research in DTI To develop Image denoising Algorithms diffusion Tensor

MRI. To develop a technique that would allow multiple diffusion

components to be computed within a given voxel. To predict the behavior of this technique under different

conditions of SNR. Improve diffusion orientation distribution function. Probabilistic fiber tracking that satisfies the anatomical

conditions

Thank You!