Basics of Diffusion Tensor Imaging and DtiStudio
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1 Basics of Diffusion Tensor Imaging and DtiStudio DTI Basics 1
2 DTI reveals White matter anatomy Gray matter White matter DTI uses water diffusion as a probe for white matter anatomy Isotropic diffusion Anisotropic diffusion 2
3 3D axonal structures can be reconstructed based on DTI What is Diffusion Imaging and How It Works RF 90 RF 90 G 3
4 Diffusion process: Concentration gradient How can we measure diffusion without perturbing the system? 4
5 Homogeneous magnetic field H O H H H O H O H B 0 / 2 e.g. B 0 = 9.4 T = 400 MHz 400 Inhomogeneous magnetic field B 0 / 2 H H O H O H H O H e.g. B 0 = 9.4 T = 400 MHz 5
6 Direction of Gradient Z X Y Z-Gradient X-Gradient Y-Gradient Strength of Gradient Z Opposite polarity +/- X Y Weak X- Gradient Strong X- Gradient Strong X- Gradient 6
7 A diagram to show the gradient application length Gx strength X Y Z how it affects spins 90 RF G Dephasing Rephasing B 0 / 2 7
8 If spins move Imperfect refocusing =Signal loss! Dephasing Rephasing Signal loss! 8
9 Signal Intensity 3/17/2012 Parameters that affect the result G D Small G Less signal loss Large G More signal loss Equation for the diffusion attenuation G ln S S G D = - bd D b-value 9
10 Signal Intensity 3/17/2012 Diffusion measurement by imaging 90 RF Imaging G b DWI and ADC 1 G/cm 6 G/cm 10 G/cm 13 G/cm b-value 10
11 Direction of the diffusion measurment Only the diffusion along a gradient direction can be measured Apparent Diffusion Constant (ADC) Map with Different Measurement Direction X Y Z 11
12 Anisotropic diffusion Free diffusion Isotropic diffusion Restricted diffusion Anisotropic diffusion Determination of diffusion ellipsoid z l 1 y l 2 x Measure diffusion along various directions (> 6) l 3 Calculate shape of the ellipsoid 12
13 Raw data of DTI S 0 S x S y S z S x+y S x+z S y+z DtiStudio Interface and DTI Data Processing 13
14 Initial data I/O window Viewing screen 14
15 Tensor calculation results 15
16 DTI-derived contrasts S 0 S x S y S z S x+y S x+z S y+z FA map Orientation map Relationships of DTI-derived maps Average STD Axial diffusivity Radial diffusivity Mean diffusivity Fractional anisotropy 16
17 Simplifications and assumptions in DTI Diffusion ellipsoid Non-tensor analysis 17
18 rotationmetric equivalent degree around x axis translation metric: mm translation score 3/17/2012 Artifact Detection and Correction Subject motion : rotationmetric : translationmetric # of DWI # of DWI 0 18
19 normalized Gx PE(Y) normalized Gy normalized Gz 3/17/2012 Uncorrectable motion problem a Eddy current distortion RO(X) b ex vs Gx, R = ey vs Gy, R = c d ez vs Gz, R = R = R = R = ex ey ez 19
20 Data corruption a b c d e f 150 g h i j k l QC Reporting Histogram of normalized absolute fitting errors before registration fitting error score 0.02 histogram eddyzmetrics a b noise 2 0 Scanner#1 Scanner#2 Scanner#3 Scanner#4 Histogram rotationmetrics equivalent degree around x axis Rotation Metric 20
21 Fiber Tracking Can axonal projections be reconstructed? At each voxel, average fiber orientation can be estimated Axonal projection reconstruction may be possible 21
22 Examples of the tracking Example of reconstruction 22
23 Fiber selection process #1 #1 #2 #3 #2 #3 Automated ROI definition and tracking 23
24 Tensor vs Non-tensor Deterministic vs Probabilistic 24
25 Pathology/ Functions/ 3/17/2012 Quantitative Data Analysis and MriStudio (DiffeoMap and RoiEditor) Scalarization is needed for image analysis Conversion to a number e.g.: FA, T2, size size 25
26 Coverage 3/17/2012 Location is the most difficult parameter to quantify Without identification of corresponding locations, quantification is meaningless Coverage and granularity of image quantification 100% Whole-brain analysis Atlas-based analysis Voxel-based analysis ROI-based analysis 1 1.5M Granularity 26
27 FA, T2 3/17/2012 Normalization of pathological brains original Normalized LDDMM: Michael I. Miller Voxel-based analysis Normalized Control Patient 27
28 Rationale of isotropic filtering Rationale of anatomical filtering: Atlas-based analysis 28
29 3D electronic brain atlas Automated segmentation of pathological brains 29
30 Automated and quantitative image analysis for population data A B DiffeoMap Interface A: Preproseccing B: Linear transformation C: LDDMM D: Transformation E: Landmark LDDMM 30
31 Choice of atlases Age-dependent 0 M 12 M 24 M Adult Multi-definition segmentation Probabilistic Structural Vascular territory Connectivity RoiEditor Interface A: ROI management B: Atlas Selection C: ROI drawing assist D: ROI list 31
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