DTI ModuleMNTP 2011

InstructorKwan-Jin Jung, Ph.D.

(Carnegie Mellon University)

Technical AssistantNidhi Kohli

(Carnegie Mellon University)

David Schaeffer (University of Georgia)

Lauren Libero (University of Alabama at Birmingham)

Sara Levens, Ph.D.(University of Pittsburgh)

LEARNING OBJECTIVES Effects of

Segmented sampling Motion correction Fiber orientation estimation method fMRI based ROIs vs. drawing ROIs

Anatomical separation of sensorimotor cortex

TERMINOLOGY Diffusion encoding gradient direction

Vector table (x, y, z components) Angular resolution

Diffusion-weighting (b-values) Duration & amplitude s/mm²

b0 = 0 s/mm² No diffusion gradient

METHOD OF ACQUISITION Segmented sampling

Complementary diffusion encoding directions

64 (A) - 10 min 64 (B) - 10 min 128 (A + B) - 20 min

Useful for special populations

MOTION CORRECTION How to correct:

1. Estimate the motion2. Rotate image and vector table accordingly

Intended Collected Head correction WRONG

Head & vector table correction

CORRECT

MOTION CORRECTION No correction

No vector rotation Interpolation

Estimates how much you rotate vector table

Based on distributed b0 images – “real motion”

Rota

tion

(deg

rees

)

Rota

tion

(deg

rees

)

TimeBEFORE

AFTER

6

3

0

-3

-6

Time

6

3

0

-3

-6

MOTION CORRECTION Simulation method

Collect two diffusion scans1. 6 direction scan (low b-value)

Why? – Fast (little time for motion) Edges of brain are clearly defined

2. 6 or more direction scan (higher b-value)

Assume no motion on scan 1, then simulate what higher b-value volume should look like

Low b-value (b=800

s/mm²) DWI(scan 1)

Assume no motion

Co-registervolumes

(estimating motion)

High b-value (b=2000

s/mm²) DWI(scan 2)

Find D (diffusion tensor)

S=S0e-

bD

Find S (simulated

high b-value)

S=S0e-

bD

Rotate vector table

FIBER ORIENTATION ESTIMATION METHODFiber/voxel Data Acquisition Analysis

Single fiber 6 – 12 directions Tensor

Multiple fibers > 25 directions (HARDI)

CSD (Q-ball, multi-tensor)

FIBER ORIENTATION ESTIMATION METHOD Tensor

Performs well for straight tracts (like motor) Performs poorly for crossing and branching

fibers (like Genu)

Constrained Spherical Deconvolution (CSD) Better for detecting branching and crossing

fibers (Tournier et al., 2007)

CSD VS. TENSOR

CSD Tensor0

20000

40000

60000

80000

100000

120000

Average Number of Tracts in Genu

N F

iber

s

GenuTensor

GenuCSD

DRAWING ROIS Manually draw ROIs Using fMRI

Collect fMRI data – find center of activation (x, y, z) Matrix transformation

Convert from fMRI coordinates into DWI native space

SEGMENTING SENSORIMOTOR Finger closing fMRI results as ROI Separation of sensory and motor areas

Clustering – fiber end-point distribution

Central Sulcus

SUMMARY Sampling schemes can be

advantageously altered for use with special populations

Simulation is a promising method for more accurate motion correction

CSD Fiber tracking is most appropriate for resolving fiber crossings

SUMMARY fMRI-based ROIs can be

used to track fibers from areas of activation

DTI can be used as a tool to segment brain areas that are not separable based on diffuse fMRI activation maps

ACKNOWLEDGMENTS Dr. Kwan-Jin Jung Nidhi Kohli MNTP Leaders: Dr. Eddy & Dr. Kim MTNP Trainees & Participants DTI Trainees 2009 & 2010 Funding:

NIH grants: R90DA023420 and T90DA022761

DISTRIBUTED B0

SCANNING PARAMETERS

MOTION CORRECTION

No correction

Interpolation

Simulation

Motion