Transcript Co-registration and Spatial Normalisation

Co-registration and Spatial
Normalisation
Martin Chadwick and Catherine Sebastian
Overview
fMRI time-series
Motion
correction
kernel
Design matrix
Smoothing
General Linear Model
(Co-registration and) Spatial
normalisation
Standard
template
Statistical Parametric Map
Parameter Estimates
Preprocessing Steps
• Realignment
– Motion correction: Adjust for movement between
slices
• Coregistration
– Overlay structural and functional images: Link
functional scans to anatomical scan
• Normalisation
– Warp images to fit to a standard template brain
• Smoothing
– To increase signal-to-noise ratio
• Extras (optional)
– Slice timing correction; unwarping
Co-registration
• Refers to any method for realigning images
– Realignment for motion correction (last week)
– Aligning or overlaying images from different
modalities
T2* EPI image
(low resolution)
T1 structural
MR image (high
resolution)
Why Co-register structural and
functional images?
• Can overlay functional activations onto an
individual’s own anatomy
• Can overlay group-level functional
activations onto an average structural
• Gives you a better spatial image for later
normalisation step, as warps derived from
the higher resolution structural image can
be applied to the functional image
Recap: realignment parameters
• Like motion correction (realignment), co-
registration makes use of 6 parameters…
Translation
X
Y
Z
Rotation
Pitch
Roll
Yaw
Differences between realignment
and co-registration
• Images may not be quite the same shape
(distortion of EPI images, especially in
phase encode direction)
• Structural and functional images do not
have the same signal intensity in the same
areas. Therefore there are additional steps
No direct voxel to
voxel match
The Normalised Mutual
Information Approach
• Different material will have different
intensities within a scan modality (e.g. air
will have a consistent brightness, and this
will differ from other materials such as
white matter).
• When looking between modalities, these
consistencies can be used to compare
images
An example
not
aligned
aligned
Joint histogram
shows little noise
More noise: hard to
define structures
with certainty
Additional points
• In some studies structurals are not taken
– it is possible to conduct fMRI analysis
without co-registering to a structural
• Sometimes when you co-register, you have
to reslice the data
- E.g. change image dimensions from 3x3x3 to
2x2x2, or change apparent direction of data
collection from axial to coronal
- Useful if two images have very different voxel sizes
- Often involves interpolation
- Often used with PET data
Co-registration in SPM
Co-registration
in SPM
Make selection
Explains each option
Template: image that
remains stationary
Image that is ‘jiggled
about’ to match template
Defaults used by SPM for
estimating the match,
including Normalised
Mutual Information
Run
Reslice options: choose
from the menu for each of
the three options (usually
just defaults)
Preprocessing Steps
• Realignment
– Motion correction: Adjust for movement between
slices
• Coregistration
– Overlay structural and functional images: Link
functional scans to anatomical scan
• Normalisation
– Warp images to fit to a standard template brain
• Smoothing
– To increase signal-to-noise ratio
• Extras (optional)
– Slice timing correction; unwarping
What is Normalisation?
Warps images from
different participants
onto a template brain
Matthew Brett
Why Normalise?
We can average the signal across participants, allowing us
to derive group statistics. This can allow us to:
• Improve the statistical power of the analysis
• Generalise findings to the population level
• Identify commonalities and differences between
groups (e.g. patient vs. healthy)
• Report results in standard co-ordinate system
(e.g. Talairach)
SPM: Spatial Normalisation
• SPM uses a voxel-intensity-based approach to
normalisation.
• adopts a two-stage procedure :
• Step 1: Linear transformation (12-parameter affine). This step
accounts for the major differences in head shape and position, but
there will be remaining smaller-scale differences.
• Step 2: Non-linear transformation (warping). The non-linear step is
designed to take care of the smaller-scale differences in brain
anatomy.
Alternatives – anatomy based approaches e.g. FreeSurfer
Step 1: Affine Transformation
• Determines the
•
optimum 12-parameter
affine transformation
to match the size and
position of the images
12 parameters = 3
translations and 3
rotations (rigid-body)
+ 3 shears and 3
zooms
Rotation
Shear
Translation
Zoom
Step 2: Non-linear Registration
• The model for defining nonlinear warps uses deformations consisting of a
linear combination of low-frequency periodic basis functions.
Over-fitting and Regularisation
Template
image
Non-linear
registration
using
regularisation.
Affine
registration
Non-linear
registration
without
regularisation.
Caveats
• Impossible to make a meaningful perfect structural match between subjects, due to
individual differences in anatomy
• Even if anatomy is well-matched, it does not guarantee that functionally homologous
areas are spatially aligned – we don’t know the extent to which individuals may vary
in their structure-function relationships.
• Pathology creates a particular problem here, as even relatively confined abnormalities
or lesions can cause mis-registration in widespread areas of the brain, due to the
global nature of the normalisation process. Need to bare this in mind in patient
studies.
Solution
• A partial solution for any remaining small-scale differences in anatomical or functional
location is offered by the next stage of pre-processing, where the images are spatially
smoothed.
Normalisation in SPM
Select Option
Select image to be matched to template
Select image(s) to be warped using the
sn.mat calculated from the Source Image
Select SPM template:
Structural – spm5\templates\T1.nii
Functional - spm5\templates\EPI.nii
Select voxel sizes for warped output images
Sources:
• Friston, K. J. Introduction: Experimental design and statistical
parametric mapping
http://www.fil.ion.ucl.ac.uk/spm/doc/intro/
• Ashburner & Friston “Rigid Body Registration” Chapter 2, Human
Brain Function, 2nd ed.;
http://www.fil.ion.ucl.ac.uk/spm/doc/books/hbf2/
• Ashburner & Friston “Spatial Normalization Using Basis Functions”
Chapter 3, Human Brain Function, 2nd ed.;
http://www.fil.ion.ucl.ac.uk/spm/doc/books/hbf2/
• Rik Henson’s Preprocessing Slides:
http://imaging.mrc-cbu.cam.ac.uk/imaging/ProcessingStream
• Previous MfD Slides