Data pre-processing framework in SPM. Bogdan Draganski
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1 Data pre-processing fraework in SPM Bogdan Draganski
2 Outline Why do we need pre-processing? Overview Structural MRI pre-processing fmri pre-processing
3 Why do we need pre-processing?
4 What do we want?
5 Reason - spatial noralisation Inter-subject averaging Increase sensitivity with ore subjects Fixed-effects analysis Extrapolate findings to the population as a whole Mixed-effects analysis Make results fro different studies coparable by aligning the to standard space e.g. The T&T convention, using the MNI teplate
6 Moveent
7 Distortions
8 Non-gaussian distribution
9 fmri tie-series kernel Design atrix Statistical Paraetric Map Motion correction Soothing General Linear Model (Co-registration and) Spatial noralisation Paraeter Estiates Standard teplate
10 Functional MRI Slice tiing correction Structural MRI Realignent Inhoogeneity correction Segentation Distortion correction (optional) Coregistration to smri Spatial noralisation Modulation Spatial noralisation (based on smri) Soothing Soothing
11 fmri tie-series Motion Correct Anatoical MRI Coregister Deforation Estiate Spatial Nor Spatially noralised Sooth Soothed Teplate Pre-processing Overview
12 MR Iages are corrupted by soothly varying intensity inhoogeneity caused by agnetic field iperfections and subject-field interactions Would ake intensity distribution spatially variable A sooth intensity correction can be odelled by a linear cobination of DCT (discrete cosine transfor) basis functions
13 Inhoogeneity correction Field inhoogeneity will disrupt intensity based segentation Bias correction required no correction Estiate T 1 GM WM
14 Tissue intensity distributions T1w MRI
15 P=0.95 P=0.95 P=0.95 P=0.95*0.95=0.90 P=0.05 P=0.95*0.05=0.05
16 Optiisation Find the best paraeters according to an objective function (iniised or axiised) Objective functions can often be related to a probabilistic odel (Bayes -> MAP -> ML -> LSQ) bjective function Global optiu (ost probable) Local optiu Local optiu Value of paraeter
17 Optiisation of ultiple paraeters Optiu Contours of a two-diensional objective function landscape
18 Segentation results Spatially noralised BrainWeb phantos (T1, T2, PD) Tissue probability aps of GM and WM Cocosco, Kollokian, Kwan & Evans. BrainWeb: Online Interface to a 3D MRI Siulated Brain Database. NeuroIage 5(4):S425 (1997)
19 Spatial noralisation Affine registration Non-linear registration
20 Spatial noralisation - Overfitting Without regularisation, the non-linear spatial noralisation can introduce unwanted deforation Teplate iage Non-linear registration using regularisation (error = 302.7) Affine registration (error = 472.1) Non-linear registration without regularisation (error = 287.3)
21 Spatial noralisation - Liitations Seek to atch functionally hoologous regions, but... No exact atch between structure and function Different cortices can have different folding patterns Challenging high-diensional optiisation Many local optia Coproise Correct relatively large-scale variability (sizes of structures) Sooth over finer-scale residual differences
22 Uses inforation fro tissue probability aps (TPMs) and the intensities of voxels in the iage to work out the probability of a voxel being GM, WM or CSF Old Segentation New Segentation
23 Modulation If soeone has atrophy, noralisation will stretch grey atter to ake brain atch healthy teplate This will reduce ability to detect differences Noralization will squeeze this region Noralization will stretch this region
24 Modulation Analogy: as we blow up a balloon, the surface becoes thinner. Likewise, as we expand a brain area it s volue is reduced. Without odulation Source Teplate Modulated
25 Modulation Multiplication of the warped (noralised) tissue intensities so that their regional or global volue is preserved Can detect differences in copletely registered areas Otherwise, we preserve concentrations, and are detecting esoscopic effects that reain after approxiate registration has reoved the acroscopic effects Flexible (not necessarily perfect ) registration ay not leave any such differences 1 1 Native intensity = tissue density Unodulated Modulated 2/3 1/3 1/3 2/3
26 Why would we deliberately blur the data? Iproves spatial overlap by blurring over inor anatoical differences and registration errors Averaging neighbouring voxels suppresses noise Increases sensitivity to effects of siilar scale to kernel (atched filter theore) Makes data ore norally distributed (central liit theore) Reduces the effective nuber of ultiple coparisons How is it ipleented? Convolution with a 3D Gaussian kernel, of specified full-width at half-axiu (FWHM) in
27 Soothing Soothing kernel - should atch the shape and size of the expected effect Benefits ore Gaussian distribution of the data Sooth out incorrect registration 4 8 RFT requires FWHM > 3 voxels 12
28 Global noralisation Shape is really a ultivariate concept Dependencies aong volues in different regions SPM is ass univariate Cobining voxel-wise inforation with global integrated tissue volue provides a coproise Above: (ii) is globally thicker, but locally thinner than (i) either of these effects ay be of interest to us. Below: The two cortices on the right both have equal volue Figures fro: Voxel-based orphoetry of the huan brain Mechelli et al, 2005
29 fmri pre-processing Slice tiing correction (optional) Realignent (Motion correction) Unwarping (Motion correction x B0 correction) Co-registration Link functional scans to anatoical scan Spatial noralisation (unified segentation) Fitting iages to a standard brain Soothing Increases signal-to-noise ratio and approxiates a Gaussian distribution
30 fmri tie-series Motion Correct Anatoical MRI Coregister Deforation Estiate Spatial Nor Spatially noralised Sooth Soothed Teplate Pre-processing Overview
31 Slice tiing (optional) TR: 2 sec Slice1 Slice11 Slice Slice TR:-1 TR:0 TR:1 TR:2 TR:3 TR:4 TR:5 0 TR:-1 TR:0 TR:1 TR:2 TR:3 TR:4 TR:5
32 Slice tiing (optional) Slice1 Slice11 Slice TR:-1 TR:0 TR:1 TR:2 TR:3 TR:4 TR:5
33 Realignent - otion correction Translation Z Rotation Yaw Roll X Pitch Y
34 Realignent - otion correction Rigid body transforations paraeterised by: Xtrans Translations Ytrans Zt rans Pitch about X axis cos() sin() 0 0 sin() cos() Roll about Y axis cos() 0 sin() sin() 0 cos() Yaw about Z axis cos() sin() 0 0 sin() cos() Miniizing the squared difference (error) between the iages Squared Error
35 Realignent otion correction
36 Unwarp Fieldap Raw EPI Undistorted EPI
37 Unwarp can estiate changes in distortion fro oveent Resulting field ap at each tie point Measured field ap Estiated change in field wrt change in pitch (x-axis) Estiated change in field wrt change in roll (y-axis) 0 = + + distortions in a reference iage (FieldMap) subject otion paraeters (that we obtain in realignent) change in deforation field with subject oveent (estiated via iteration)
38 Co-registration Noralized utual inforation functional and structural iages in the sae space
39 Co-registration T2 intensity T1 intensity T2 intensity T1 intensity
40 Spatial registration Registration of structural iages to a standard brain teplate (standard space) The obtained transforation (warping) paraeters can be applied on co-registered fmri data Iproved spatial noralization based on high resolution structural inforation
41 Soothing
42 Soothing
43 Thank you
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