spm使用培训课件02Ged_Spatial_Preprocessing

• Registration intuitively relies on the concept of aligning images to increase their similarity
– This needs to be mathematically formalised – We need practical way(s) of measuring similarity
– AKA sum-squared error, RMS error, etc. (monotonically related). AKA “least squares” – Assumes simple relationship between intensities – Optimal (only) if differences are i.i.d. Gaussian – Okay for fMRI realignment or sMRI-sMRI coreg
Manual reorientation
Image “headers” contain information that lets us map from voxel indices to “world” coordinates in mm
Modifying this mapping lets us reorient (and realign or coregister) the image(s)
• Using interpolation we can find the intensity at equivalent voxels
– (equivalent according to the current transformation parameter estimates)
Voxel similarity measures
– (Sinc interpolation is an alternative to B-spline)
Manual reorientation – Reslicing
Reoriented (1x1x3 mm voxel size)
Resliced (to 2 mm cubic)
Quantifying image alignment
Edge:
DS(x) ~ 70-90%
0.05 pixel shift (187 mm) 3 - 5% DS
Inside:
DS(x) ~ 10-20% 0.25 pixel shift (~1 mm) 3 - 5% DS
I src ( i , j , k ) I src ( i 1, j , k ) I src ( i , j 1, k )
Intra-modal similarity measures
• Mean squared error (minimise)
fMRI time-series Design matrix
Statistical Parametric Map
Motion Correction
Smoothing
General Linear Model
Spatial Normalisation
Parameter Estimates
Anatomical Reference
TR = repetition time time required to scan one volume
voxel
Terminology of fMRI
Scan Volume: Field of View (FOV), e.g. 192 mm
Axial slices
Slice thickness e.g., 3 mm
– AKA reslicing, regridding, transformation, and writing (as in normalise - write)
• Nearest neighbour gives the new voxel the value of the closest corresponding voxel in the source • Linear interpolation uses information from all immediate neighbours (2 in 1D, 4 in 2D, 8 in 3D) • NN and linear interp. correspond to zeroth and first order B-spline interpolation, higher orders use more information in the hope of improving results
Manual reorientation
Manual reorientation
Interpolation
Nearest (Bi)linear Neighbour
(Bi)linear Sinc
Interpolation
• Applying the transformation parameters, and resampling the data onto the same grid of voxels as the target image (usually)
• Rigid inter-modal coregistration
– Aligning structural and (mean) functional images – Multimodal structural registration, e.g. T1-T2
• Affine inter-subject registration
Option of second pass, reregistering to mean from first pass, improves results if first image is not perfectly representative of all others
How much motion is significant?
I ref ( i , j , k ) I ref ( i 1, j , k ) I ref ( i , j 1, k )
Pairs of voxel intensities
• Mean-squared difference • Correlation coefficient • Joint histogram measures
– Allowing more complex geometric transformations or warps leads to more flexible spatial normalisation
• Let’s look at an example of realignment next
– We will return to coregistration later...
Time series: A large number of images that are acquired in temporal order at a specific rate
t
Terminology of fMRI
subjects
sessions runs
single run
volume slices
– First stage of non-linear spatial normalisation – Approximate alignment of tissue probability maps
Other types of registration in SPM
• Non-linear spatial normalisation
• Correlation-coefficient (maximise)
– Slightly more general, e.g. T1-T1 inter-scanner – Invariant under affine transformation of intensities – AKA Normalised Cross-Correlation, Zero-NCC
– Registering different subjects to a standard template
• Unified segmentation and normalisation
– Warping standard-space tissue probability maps to a particular subject (can normalise using the inverse)
Preprocessing in SPM
• Realignment
– With non-linear unwarping for EPI fMRI
• • • • •
Slice-time correction Coregistration Normalisation Segmentation Smoothing
Automatic image registration
• Quantifying the quality of the alignment with a measure of image similarity allows computational estimation of transformation parameters • This is the basis of both realignment and coregistration in SPM
Terminology of fMRI
Structural (T1) images: - high resolution - to distinguish different types of tissue Functional (T2*) images: - lower spatial resolution - to relate changes in BOLD signal to an experimental manipulation
• High-dimensional warping
– Modelling small longitudinal deformations (e.g. AD)
• DARTEL
– Smooth large-deformation warps using flows – Normalisation to group’s average shape template
The many guises of affine registration
• Manual reorientation • Rigid intra-modal realignment
– Motion correction of fMRI time-series – Within-subject longitudinal registration of serial sMRI
Matrix Size e.g., 64 x 64
In-plane resolution 192 mm / 64 = 3 mm
3 mm
3 mm 3 mm
பைடு நூலகம்
Voxel Size
(volumetric pixel)
fMRI Time-series Movie
Overview of SPM Analysis
Spatial Preprocessing
Ged Ridgway University College London
Thanks to: John Ashburner, Klaas Enno Stephan, and the FIL Methods Group. Meike Grol, Chloe Hutton, Jesper Andersson, Floris de Lange, Miranda van Turennout, Lennart Verhagen, …