diffusion tensor mri and fiber tacking presented by: eng. inas yassine. [email protected]
TRANSCRIPT
204/18/23
Outline Nuclear Magnetic resonance (NMR)
Diffusion Tensor Imaging (DTI)
DTI-Based Connectivity Mapping
Research in DTI
404/18/23
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 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
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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
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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.
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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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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