Coregistration and Normalisation
By Lieke de Boer & Julie Guerin
Presentation Overview
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•
•
•
•
•
Recap: Steps of Preprocessing
Coregistration
Normalisation
Smoothing
Summary
SPM Guidelines
Steps of Preprocessing
1.
2.
3.
4.
Realign & Unwarp
Coregister
Normalise
Smooth
Raw data
(fct)
Raw data
(str)
MNI
1
MNI
2
MNI
4
3
fct
str
MNI
4
is a blurry
version of
3
Coregistration
Normalisation
& Smoothing
Recap: 1. Realignment
• Aligning all fMRI scans to reference scan
– Linear transformations (translations and rotations)
MNI
Translation
Rotation
Recap: 1. Unwarping
• Non-linear transformations
• Adjusts for deformations in magnetic field
2. Coregistration
MNI
• Cross modal (structural/functional) realignment to the
same native space.
• Within-subjects
Structural
(high resolution)
Functional
(low resolution)
Coregistration: Step 1
Translation
Registration
– Similar to realignment
– Fitting source image (T1 structural)
to reference image (T2 functional)
Rotation
Coregistration: Step 2
Reslicing/Interpolation
•Estimating what value of intensity each voxel represents in a
functional image.
•Structural (high resolution; 1mm3) ≠ Functional (low resolution;
3mm3)
– Reslicing estimates the intensity of surrounding voxels in
functional scan so that functional voxels correspond with
structural voxels.
Coregistration: Step 2 (Continued)
Methods of Interpolation:
• Nearest Neighbour
• Linear Interpolation
z
c
?
• B-Spline Interpolation (Higher-Order Interpolation)
Coregistration: Forming a Joint Histogram
T1 (structural) histogram
intensity
# voxels
T2* (functional) histogram
intensity
Registered Joint Histogram
# voxels
UN-Registered Joint Histogram
3. Normalisation
fct
str
MNI
Coregistered images  Standardised space
Talairach Atlas
MNI Template
Why Normalise?
•
•
•
•
≠
Statistical power
Group analyses
Generalise findings/Representative
Cross-study comparisons (standard coordinate system)
Normalisation
• Aligning and warping to
standardised space
• Template fitting:
– Right/Left
– Anterior/Posterior
– Superior/Inferior
GOAL: voxel to voxel correspondence between brains of
many subjects
Normalisation: Optimisation Difficulties
• Aim: to find an identical fit between the subjects’ brain
and the template brain.
• Reality: difficult to match brains when size/shape are so
different
– not structurally or functionally homologous
– maximise similarity within reasonable limits/expectations.
Normalisation: Affine Transformation
Linear Transformations
• Translations across axes
• Rotations around axes
• Scaling and zooming axes
• Shearing or skewing, i.e. angle changes between pairs of axes
Normalisation: Refine with Warping
• Applies non-linear warping to images to match template.
• Apply deformations/displacements to move voxels from original
location to template location in multiple dimensions
Affine Registration
Non-linear Registration
Normalisation: Risk of Overfitting
• Warping is completely flexible and can therefore introduce
unrealistic deformations = overfitting.
Non-linear
registration
using
regularisation.
Non-linear
registration
without
regularisation.
Normalisation: Regularisation
• Rather have less good match: compromise between
reasonably good match & realistic deformation
– Uses Bayesian framework: probability function of determining
appropriate warp amounts.
• Regularisation sets limits to warp parameters, and ensures
voxels stay close to their neighbours.
Normalisation: Segmentation
• Different scanners, noise, artifacts, magnetic field
properties, etc. prevent data from being uniformly and
predictably adjusted to template.
Segmentation
• Tissue Probability Maps (TPM) from standard space help
predict tissue differentiation in subjects.
• Segmentation helps correctly identify which tissue type
the voxels of interest belong to.
Normalisation: Generative Model
• Each tissue type has Gaussian probability density function
for intensity.
• Goal is to get the best estimate of tissue probabilities.
• Generative Model (Bayesian) – fitting a Gaussian Mixture
Model (GMM) to the joint histogram
4. Gaussian Smoothing
• Smoothing: adjusts for any residual differences and
alignment errors.
– Reduces signal to noise ratio.
– Better spatial overlap
– Better normally distributes data; enables statistical analyses
• How: Convolution calculates a weighted average of
neighbouring voxels -- each voxel gets replaced by a
weighted average of itself and its neighbours.
Keep In Mind:
• fMRI analyses are not precise and are based on multiple
data adjustments that ultimately alter the raw data
substantially.
• However, relatively reliable and robust inferences can be
made when sample sizes are large enough, and when
appropriate statistical analyses are performed.
Summary
MNI
MNI
fct
str
MNI
1. Realign & Unwarp
2. Coregister
3. Normalise & 4. Smooth
4
4
is a blurry
version of
3
SPM
SPM
SPM
SPM
Data = Structural
file (batched, for all
subjects)
Tissue probability maps
= 3 files: white matter,
grey matter, CSF
(Default)
Masking image =
exclude regions
from spatial
normalization
(e.g. lesion)
Parameter File = Click
‘Dependency’ (bottom
right of same window)
Images to Write = Coregistered functionals
(same as in previous slide)
SPM
SPM
References
• http://www.ucl.ac.uk/stream/media/swatch?v=1d42446d1c34 (Ged
Ridgway’s preprocessing lecture)
• SPM videos: http://www.fil.ion.ucl.ac.uk/spm/course/video/
• SPM Homepage: http://www.fil.ion.ucl.ac.uk/spm/
• Suz Prejawa
• MfD Resources 2011-2012
And thanks to Ged!