statistical parametric mapping lecture 11 - chapter 13 head motion and correction textbook:...

33
Statistical Parametric Statistical Parametric Mapping Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook : Functional MRI an introduction to methods , Peter Jezzard, Paul Matthews, and Stephen Smith Many thanks to those that share their MRI slides online

Upload: lilian-blair

Post on 17-Jan-2016

217 views

Category:

Documents


0 download

TRANSCRIPT

Page 1: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

Statistical Parametric MappingStatistical Parametric Mapping

Lecture 11 - Chapter 13

Head motion and correction

Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul Matthews, and Stephen Smith

Many thanks to those that share their MRI slides online

Page 2: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

http://www.fmrib.ox.ac.uk/fsl/

Page 3: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

Head motion correction by MCFLIRT

• FSL software for Motion Correction using FMRIB’s Linear Image Registration Tool - FLIRT.

Page 4: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul
Page 5: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

1

43

2

Page 6: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

4x4 Affine transform matrix used with 3-D images

⎥⎥⎥⎥

⎢⎢⎢⎢

⎥⎥⎥⎥

⎢⎢⎢⎢

=

⎥⎥⎥⎥

⎢⎢⎢⎢

11000

34333231

24232221

14131211

1

'

'

'

z

y

x

mmmm

mmmm

mmmm

z

y

x

m14, m24, and m34 are x, y, and z translations

3 each rotations, scales, shears

Let R represent (x, y, z) column vectors and M the 4x4 transform matrixR’ = M R when you want to calculate primed location from unprimed oneR = M-1 R’ when you want to calculate unprimed location from primed one

Page 7: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

x '

y '

z'

1

⎢ ⎢ ⎢ ⎢

⎥ ⎥ ⎥ ⎥

=

1 0 0 Tx

0 1 0 Ty

0 0 1 Tz

0 0 0 1

⎢ ⎢ ⎢ ⎢

⎥ ⎥ ⎥ ⎥

x

y

z

1

⎢ ⎢ ⎢ ⎢

⎥ ⎥ ⎥ ⎥

4x4 translation matrix T (3 parameters)

x '

y '

z'

1

⎢ ⎢ ⎢ ⎢

⎥ ⎥ ⎥ ⎥

=

1 0 0 0

0 cos(φx ) −sin(φx ) 0

0 sin(φx ) cos(φx ) 0

0 0 0 1

⎢ ⎢ ⎢ ⎢

⎥ ⎥ ⎥ ⎥

x

y

z

1

⎢ ⎢ ⎢ ⎢

⎥ ⎥ ⎥ ⎥

4x4 scale matrix S (3 parameters)

x '

y '

z'

1

⎢ ⎢ ⎢ ⎢

⎥ ⎥ ⎥ ⎥

=

Sx 0 0 0

0 Sy 0 0

0 0 Sz 0

0 0 0 1

⎢ ⎢ ⎢ ⎢

⎥ ⎥ ⎥ ⎥

x

y

z

1

⎢ ⎢ ⎢ ⎢

⎥ ⎥ ⎥ ⎥

x '

y '

z'

1

⎢ ⎢ ⎢ ⎢

⎥ ⎥ ⎥ ⎥

=

cos(φy ) 0 −sin(φy ) 0

0 1 0 0

sin(φy ) 0 cos(φy ) 0

0 0 0 1

⎢ ⎢ ⎢ ⎢

⎥ ⎥ ⎥ ⎥

x

y

z

1

⎢ ⎢ ⎢ ⎢

⎥ ⎥ ⎥ ⎥

x '

y '

z'

1

⎢ ⎢ ⎢ ⎢

⎥ ⎥ ⎥ ⎥

=

cos(φz) −sin(φz) 0 0

sin(φz) cos(φz) 0 0

0 0 1 0

0 0 0 1

⎢ ⎢ ⎢ ⎢

⎥ ⎥ ⎥ ⎥

x

y

z

1

⎢ ⎢ ⎢ ⎢

⎥ ⎥ ⎥ ⎥

rotation matrix Rx for rotation about x-axis (1 parameter)

rotation matrix Ry for rotation about y-axis (1 parameter)

rotation matrix Rz for rotation about z-axis (1 parameter)

M= [S][Rx][Ry][Rz][T]

Order of matrices important. Above ordering does translations first to match origins, then rotations about new origin, and finally scaling of the aligned image. For MC of fMRI no scale, i.e. use only 3 translation and 3 rotation parameters.

Page 8: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul
Page 9: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

1

43

2

Page 10: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

Mango provides these three interpolation options when saving an image with transform applied.

Nearest neighbor leads to similar image histograms in transformed images but least accurate in terms of interpolation values. Sinc interpolation is considered to be the most accurate. Trilinear is intermediate in quality but relatively fast.

Page 11: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul
Page 12: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

1

43

2

Page 13: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

Motion Correction

Same Modality

Page 14: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul
Page 15: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

1

43

2

Page 16: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

1

3

2

Page 17: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul
Page 18: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

Talariach's Coordinate System

• AC-PC line• AC as origin• Bounding Box

– 136 x 172 x 118 mm

• Right-handed system

Z = +1 mm

Origin(AC)

Page 19: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

Averages of Anatomy & Function

Average MRI - 16 Subjects( 3-D Gradient Echo )

Average PET - 15 Subjects( FDG )

Page 20: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

Fitted AC-PC LineFitted AC-PC Line

CCCC TNTN SCSC CBCB

Manual SN with High-Resolution MRI

Mango has a plugin for fitting high resolution MRI to the Talairach standard.Mango has a plugin for fitting high resolution MRI to the Talairach standard.

Page 21: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

PET

SPECT MRI

CT

Multi-modality registration to a standard brain space (i.e. a particular brain atlas).

Page 22: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

Using FLIRT to fit a T1W MR image to the MNI305 3-D average brain template (template brain feature outline indicated by red lines).

Note the large rotation about the y-axis indicated in the left image.

Page 23: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

Atlas Based Registration of fMRI

MNI152 structural brain template is average of 152 3-D T1W images after affine registration.

MNI152structuralEPI

M1 M2

M= [M2][M1]

What is a 7 DOF transform?All three scales are identical to correct for possible differences in spatial calibrations.

Page 24: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

Global (affine) vs. Regional (warping) to target Global (affine) vs. Regional (warping) to target brainbrain

4x4 affine transform registration to target brain

High degree of freedom warp to register to target brain (~2x105 deformation vectors)

Target Brain

This could be stage 2 in fMRI alignment to atlas.

Page 25: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul
Page 26: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

1

43

2

Page 27: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

R’ = MR

For 6 DOFs M includes rotations about x, y, and z axes and translations along x, y, and z.

Total motion is calculated from distance = {(x’-x)2+ (y’-y)2 +(z’-z)2}1/2 calculated for each voxel within the brain.

1 2

Page 28: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul
Page 29: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

Figure 13.1. Motion correction in a finger tapping experiment. Fig 1a (left) shows the motion component ( z translation) exhibiting the strongest stimulus-correlated motion. The experimental “on” periods are shown at the bottom of the figure. Fig 1b (right) shows a set of superior axial images with no correction (top row), after realignment to template only (middle row) and following full correction including z-motion in regression analysis ( bottom row).

Full correction models signal loss due to movement.

Page 30: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

Figure 13.2. Motion correction in a visual stimulation experiment. Fig 2a (left) shows the motion component ( z translation) exhibiting the strongest stimulus-correlated motion. The experimental “on” periods are shown at the bottom. Fig 2b (right) shows a set of superior axial images with no correction (top row), after realignment only (middle row) and following full correction( bottom row)

Page 31: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

Figure 13.3. Two slices from a group activation map ( n=6) in schizophrenic patients before ( upper row) and after (lower row) group correction for subject motion.

Page 32: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

No MC

MC

visual

visual

auditory

auditory

Page 33: Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul

http://www.fmrib.ox.ac.uk/fsl/