Coregistration and Spatial Normalisation Ana Saraiva Britt Hoffland.
-
Upload
calvin-gibson -
Category
Documents
-
view
225 -
download
0
Transcript of Coregistration and Spatial Normalisation Ana Saraiva Britt Hoffland.
Coregistration and Spatial Normalisation
Ana SaraivaBritt Hoffland
OverviewOverview
Motioncorrection
Smoothing
kernel
(Co-registration and) Spatialnormalisation
Standardtemplate
fMRI time-series Statistical Parametric Map
General Linear Model
Design matrix
Parameter Estimates
• Co-registration
• Between modality co-registration
PET T1 MRI
Why is between-modality co-registration useful?
• Significant advantages in research and clinical settings
Principles of co-registration
Registration Transformation
6 Parameters for motion correction
Different for between-modality coregistration
• Shape• Signal intensities
EPI
T2 T1 Transm
PD PET
Between modality registration
• Manually (homologous landmarks)• I via templates• II mutual information
Via Templates
• 12 parameter affine transformations
• Templates conform to the same anatomical space
• Simultaneous registration
1. Affine Registration
• 12 parameter affine transform– 3 translations– 3 rotations– 3 zooms– 3 shears
• Fits overall shape and size
Algorithm simultaneously minimises Mean-squared difference between template and
source image Squared distance between parameters and their
expected values (regularisation)
However…
• Image MRI Template MRI
Scaling/shearing parametersRigid body transformation parameters
• Image PET Template PET
2. Segmentation
• Partition in GM, WM, CSF
Priors:
Image:
Brain/skullCSFWMGM
Registration of partitions
Grey and white matter partitions are registered using a rigid body transformation,
Simultaneously minimise sum of squared difference…
Between Modality Coregistration: II. Mutual Information
PET T1 MRI
Co-registration in SPM
Co-registration in SPM
Make selection
Explains each option
Template: image that remains stationaryImage that is ‘jiggled about’ to match templateDefaults used by SPM for estimating the match, including Normalised Mutual InformationReslice options: choose from the menu for each of the three options (usually just defaults)
Run
Spatial Normalisation
fMRI pre-processing sequence
• 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 spatial normalisation?
• Establishes a one-to-one correspondence between the brains of different individuals by matching each subject to a standard template
• Allows: – Signal averaging across subjects– Determination of what happens generically over individuals – Identify commonalities and differences between groups (e.g.
patients vs. healthy individuals)
• Advantages:– Activation sites can be reported according to their Euclidian
coordinates within a standard space (e.g. MNI or Tailarach & Tournoux, 1988)
– Increases statistical power
Methods of registering images1. Label-based
– Identifies homologous features (points, lines and surfaces) in the image and template and finds the transformations that best superimpose them
– Limitations: few identifiable features; features can be identified manually (time consuming & subjective)
2. Non-label based (aka intensity based)– Identifies a spatial transformation that optimizes some voxel-
similarity between a source and image measure by:• Minimising the sum of squared differences between the object and
template image • Maximising correlation coefficient between the images.
– Limitation: susceptible to poor starting estimates
Spatial Normalisation in SPM
• 2 steps involved in registering any pair of images:
1. Linear registration - 12-parameter affine transformation – accounts for major differences in head shape and position
2. Nonlinear registration – warping – accounts for smaller-scale anatomical differences
Priors/Constraints
• Both linear and non-linear registrations use prior knowledge of the variability of the head and size to determine constraints
• Priors/constraints are calculated using estimators such as the maximum a posteriori (MAP) or the minimum variance estimate (MVE)
Step 1 – Affine transformation (Linear)
• Aim: to fit the source image f to a template image g, using a 12-parameter affine transformation
• Performed automatically by minimizing squared distance between parameters and expected values
• 12 parameters = 3 translations and 3 rotations (rigid-body) + 3 shears and 3 zooms• Accounts for overall shape, size,
position and orientation
translation zoom
rotation sheer
Step 2 – Warping (non-linear)
• Corrects gross differences in head shapes that cannot be accounted for by the affine transformation
• Warps are modelled by linear combinations of smooth discrete cosine transform basis functions
• Uses relatively small number of parameters (approx. 1000)
Non-linear basis functions
Deformations are modelled with a linear combination of non-linear basis functions
TemplateAffine registration (linear)
Non-linear registration with regularisation
Non-linear registration without regularisation
Over-fitting• Regularisation – necessary so that nonlinear registration does not introduce unnecessary deformations
• Ensures voxels stay close to their neighbours
Limitations
• Difficult to attempt exact structural matches between subjects, due to individual anatomical differences
• Even if anatomical areas were exactly matched, it does not mean functionally homologous areas are matched too
• This is particularly problematic in patient studies with lesioned brains
• Solution: To correct gross differences followed by spatial smoothing of normalised images…
Normalisation in SPM
Calculates warps needed to get from your selected images – saves in sn.mat file
1. Select the image that will be matched to the template
2. Select image(s) to be warped using the sn.mat calculated from the Source Image
3. Select SPM template
4. Select voxel sizes for warped output images
References
• Ashburner & Friston – Spatial Normalisation Using Basis Functions, Chapter 3, Human Brain Function, 2nd Ed
• Ashburner & Friston – Nonlinear Spatial Normalisation Using Basis Functions, Human Brain Mapping, 1999
• Ashburner & Friston - Multimodal image coregistration and partitioning--a unified framework, Neuroimage, 1997
• MFD slides from previous years • http://www.fil.ion.ucl.ac.uk/spm/course/slides08-zurich/