Basics of Diffusion Tensor Imaging and DtiStudio DTI Basics 1
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
3D axonal structures can be reconstructed based on DTI What is Diffusion Imaging and How It Works RF 90 RF 90 G 3
Diffusion process: Concentration gradient How can we measure diffusion without perturbing the system? 4
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
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
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
If spins move Imperfect refocusing =Signal loss! Dephasing Rephasing Signal loss! 8
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 2 0 2 2 3 D = - bd D b-value 9
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
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
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
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
Initial data I/O window Viewing screen 14
Tensor calculation results 15
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
Simplifications and assumptions in DTI Diffusion ellipsoid Non-tensor analysis 17
rotationmetric equivalent degree around x axis translation metric: mm translation score 3/17/2012 Artifact Detection and Correction Subject motion 1 10 15 22 32 0.1 0.08 0.06 73088: rotationmetric 4 3.2 2.4 7 6 5 4 73088: translationmetric 8 6 0.04 1.6 3 4 0.02 0 0.8 0 2 1 2 5 10 15 20 25 30 # of DWI 0 5 10 15 20 25 30 # of DWI 0 18
normalized Gx PE(Y) normalized Gy normalized Gz 3/17/2012 Uncorrectable motion problem 6 11 15 28 a Eddy current distortion RO(X) 1 0.5 b ex vs Gx, R = 0.9993 1 0.5 ey vs Gy, R = 0.99532 c 1 0.5 d ez vs Gz, R = -0.99576 0 0 0-0.5-0.5 R = 0.9993 R = 0.9953 R =-0.9957-0.5-1 -0.1-0.05 0 0.05 0.1 ex -1-0.1-0.05 0 0.05 0.1 ey -1-0.1-0.05 0 0.05 0.1 ez 19
Data corruption 50 100 3 2 150 a b c d 50 100 50 100 150 1 0 3 2 e f 150 g h 1 50 100 150 20 50 1.5 150 i j k l 100 50 100 150 1 0.5 0 QC Reporting Histogram of normalized absolute fitting errors before registration fitting error score 0.02 histogram eddyzmetrics 1200 0.4 0.8 1.2 1.6 2 2.4 2.8 3.2 0.018 0.016 1000 0.014 800 0.012 0.01 600 0.008 400 200 a 0.006 0.004 0.002 b 0 0 1 2 3 4 noise 2 0 Scanner#1 Scanner#2 Scanner#3 Scanner#4 Histogram rotationmetrics equivalent degree around x axis 0 0.4 0.8 1.2 1.6 2 2.4 2.8 600 50 100 150 1.5 1 0.5 500 400 300 200 100 50 100 150 0 0 0 0.02 0.04 0.06 0.08 Rotation Metric 20
Fiber Tracking Can axonal projections be reconstructed? At each voxel, average fiber orientation can be estimated Axonal projection reconstruction may be possible 21
Examples of the tracking Example of reconstruction 22
Fiber selection process #1 #1 #2 #3 #2 #3 Automated ROI definition and tracking 23
Tensor vs Non-tensor Deterministic vs Probabilistic 24
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
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
FA, T2 3/17/2012 Normalization of pathological brains original Normalized LDDMM: Michael I. Miller Voxel-based analysis Normalized Control Patient 27
Rationale of isotropic filtering Rationale of anatomical filtering: Atlas-based analysis 28
3D electronic brain atlas Automated segmentation of pathological brains 29
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
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