FIL SPM Course Spatial Preprocessing
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1 Registration, noralisation, segentation, VBM Matthan Caan, BIC/CSCA suer school 2012 Neuroiage, 2012 slides copied/adjusted fro: FIL SPM Course Spatial Preprocessing 1. smri 2. DTI 3. fmri By John Ashburner & Ged Ridgway 4
2 Preprocessing overview Preprocessing overview fmri tie-series Anatoical MRI TPMs Input Output Segentation Transforation (seg_sn.at) Kernel REALIGN COREG SEGMENT NORM WRITE SMOOTH REALIGN COREG SEGMENT NORM WRITE SMOOTH ANALYSIS Motion corrected Mean functional (Headers changed) MNI Space ANALYSIS Contents 1. Registration basics 2. Motion and realignent 3. Inter-odal coregistration 4. Spatial noralisation 5. Unified segentation Special cases of affine registration * Manual reorientation * Rigid intra-odal realignent * Motion correction of fmri tie-series * Within-subject longitudinal registration of serial smri * Rigid inter-odal coregistration * Aligning structural and (ean) functional iages * Multiodal structural registration, e.g. T1-T2 * Affine inter-subject registration * First stage of non-linear spatial noralisation * Approxiate alignent of tissue probability aps
3 Affine transforations * Rigid rotations have six degrees of freedo (DF) * Three translations and a 3D rotation (e.g. 3 axis rots.) * Voxel-world appings (spacing) usually include three scaling DF (for a possible total of 9 DF) * General 3D affine transforations add three shears (12 DF total) * Affine transfor properties * Parallel lines reain parallel Degrees of freedo (DOF) FSL 10 rigid (6) Degrees of freedo (DOF) SPM affine (12): Noralise: affine + non-linear Select 0 non-linear iterations Other types of registration in SPM * Non-linear spatial noralisation * Registering different subjects to a standard teplate * Unified segentation and noralisation * Warping standard-space tissue probability aps to a particular subject (can noralise using the inverse) * High-diensional warping * Modelling sall longitudinal deforations (e.g. AD) * DARTEL * Sooth large-deforation warps using flows * Noralisation to group s average shape teplate 11
4 Voxel-to-world apping Transforation atrix * Affine transfor associated with each iage * Maps fro voxels (x=1..n x, y=1..n y, z=1..n z ) to soe world coordinate syste. e.g. scanner co-ordinates * Registering iage B (source) to iage A (target) will update B s voxel-to-world apping (!!!) * If B is aligned with C, the apping should applied to both! T Contains translation, rotation, scaling, stretching, shearing. In MATLAB: n=nifti( T1.nii ) % read nifti-header n.at % display transforation In FSL: fslhd or fslinfo Manual reorientation Manual reorientation Iage headers contain inforation that lets us ap fro voxel indices to world coordinates in Modifying this apping lets us reorient (and realign or coregister) the iage(s)
5 Transforation/reorientation atrix SPM display SPM coreg result Interpolation (voxel size 1x1x3 ) (Bi)linear FSL flirt transforation atrix Nearest Neighbour (Bi)linear (Bi)linear Sinc 17 Interpolation / Reslicing Reslicing * Applying the transforation paraeters, and resapling the data onto the sae grid of voxels as the target iage * AKA writing (as in noralise - write) * Nearest neighbour gives the new voxel the value of the closest corresponding voxel in the source * Linear interpolation uses inforation fro all iediate 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 ore inforation in the hope of iproving results * (Sinc interpolation is an alternative to B-spline) Reoriented (1x1x3 voxel size) Resliced (to 2 cubic)
6 Reslicing After coregistration (6 DOF), the transforation is updated in the header of the nifti-file and reslicing is not strictly needed. Reslicing affects the quality of your dataset, because of interpolation. Reslice as little as possible. Pay attention when spacing of source and target is different: source 3x3x7, target 1x1x1: source insufficiently large source 1x1x1, target 3x3x7: any details are lost source 3x3x7, target 3x3x7: any details are lost, because of thick slices. First reslice to 3x3x3 (isotropic) Quantifying iage alignent * Registration intuitively relies on the concept of aligning iages to increase their siilarity * This needs to be atheatically foralised * We need practical way(s) of easuring siilarity * Using interpolation we can find the intensity at equivalent voxels * (equivalent according to the current transforation paraeter estiates) Note: when reslicing label aps, use nearest neighbour interpolation. Otherwise your labels change. 21 Voxel siilarity easures Intra-odal siilarity easures 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 histogra easures I src ( i, j, k ) I src ( i 1, j, k ) I src ( i, j 1, k ) * Mean squared error (iniise) * AKA su-squared error, RMS error, etc. * Assues siple relationship between intensities * Optial (only) if differences are i.i.d. Gaussian * Okay for fmri realignent or smri-smri coreg * Correlation-coefficient (axiise) * AKA Noralised Cross-Correlation, Zero-NCC * Slightly ore general, e.g. T1-T1 inter-scanner * Invariant under affine transforation of intensities
7 Autoatic iage registration Optiisation * Quantifying the quality of the alignent with a easure of iage siilarity allows coputational estiation of transforation paraeters * This is the basis of both realignent and coregistration in SPM * Allowing ore coplex geoetric transforations or warps leads to ore flexible spatial noralisation * Autoating registration requires optiisation... * Find the best paraeters according to an objective function (iniised or axiised) * Optiise: translation, rotation, scaling, shearing * Challenges: local inia, noise Objective function Global optiu (ost probable) Local optiu Local optiu Value of paraeter optialisatie verplaatsing verplaatsing
8 stap voor stap rotatie rotatie 2 verplaatsing verplaatsing schaling 1 schaling 1 schaling 2
9 lokale inia? schaling 1 3 schaling 2 4 verplaatsing ruis vervoring? verplaatsing
10 Inter-odal coregistration Match iages fro sae subject but different odalities: anatoical localisation of single subject activations achieve ore precise spatial noralisation of functional iage using anatoical iage. Inter-odal siilarity easures * Coonly derived fro joint and arginal entropies * Entropies via probabilities, fro histogras * H(a) = -a P(a) log 2 P(a) * H(a,b) = -a P(a,b) log 2 P(a,b) * Miniise joint entropy H(a,b) * Maxiise utual Inforation * MI = H(a) + H(b) - H(a,b) * Maxiise noralised MI * NMI = (H(a) + H(b)) / H(a,b) Noralised Mutual Inforation Measure for copactness of joint histogra. Use for inter-odal registration. Also robust to issing data in one scan. Joint histogra T 1 T 1 T 2 T 2
11 FSL flirt: no histogra iage, but you can change nr. of bins Contents 1. Registration basics 2. Motion and realignent 3. Inter-odal coregistration 4. Spatial noralisation 5. Unified segentation 43
12 Spatial Noralisation Spatial Noralisation - Reasons * 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. using the MNI teplate Spatial Noralisation Procedure Spatial Noralisation Warping * Start with a 12 DF affine registration * 3 translations, 3 rotations 3 zoos, 3 shears * Fits overall shape and size * Refine the registration with non-linear deforations Deforations are odelled with a linear cobination of non-linear basis functions
13 Standard spaces Spatial Noralisation Teplates The Talairach Atlas The MNI/ICBM AVG152 Teplate T2 T1 Trans EPI PD PET sp8/teplates The MNI teplate follows the convention of T&T, but doesn t atch the particular brain Recoended reading: Masking in registration Spatial Noralisation Results Spatial noralisation can be weighted so that non-brain voxels do not influence the result. FSL: SPM: use BET coregistration is robust on non-brain areas noralisation: use the brain ask in MNI-space need a subject s brain ask? segent, su GM/WM/CSF-asks sp8/apriori/brainask.nii Affine registration Non-linear registration 51
14 Practical suggestions Viewing registration results / alignent Use dc2nii (ricron) to convert PARREC / DICOM to nifti. It preserves your scan orientation. Make a copy of your data before you start. SPM overwrites transforation in nifti-header. High intensity peaks and negative values distort histogra. Fix before registration. SPM check reg: checking aligned datasets of different size FSLview (also for windows): view 4D-datasets as a ovie anually adjust thresholds, colorap, transparancy Use both! If your scan s orientation inforation is lost, initialize to MNIspace using initniftitomni Contents 1. Registration basics 2. Motion and realignent 3. Inter-odal coregistration 4. Spatial noralisation 5. Unified segentation Unified segentation and noralisation * MRI iperfections ake noralisation harder * Noise, artefacts, partial volue effect * Intensity inhoogeneity or bias field * Differences between sequences * Noralising segented tissue aps should be ore robust and precise than using the original iages... * Tissue segentation benefits fro spatially-aligned prior tissue probability aps (fro other segentations) * This circularity otivates siultaneous segentation and noralisation in a unified odel
15 Modelling inhoogeneity * Intensity in T1-scan is globally inhoogeneous * A ultiplicative bias field is odelled as a linear cobination of basis functions. Tissue Probability Maps * Tissue probability aps (TPMs) are used as the prior, instead of the proportion of voxels in each class Corrupted iage Bias Field Corrected iage ICBM Tissue Probabilistic Atlases. These tissue probability aps are kindly provided by the International Consortiu for Brain Mapping, John C. Mazziotta and Arthur W. Toga. Deforing the Tissue Probability Maps * Tissue probability iages are warped to atch the subject * The inverse transfor warps to the TPMs
16 Exaple output SPM segentation: light cleanup ight be needed (specify in setup) No cleanup Light cleanup 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)
17 References * Friston et al. Spatial registration and noralisation of iages. Huan Brain Mapping 3: (1995). * Collignon et al. Autoated ulti-odality iage registration based on inforation theory. IPMI 95 pp (1995). * Ashburner et al. Incorporating prior knowledge into iage registration. NeuroIage 6: (1997). * Ashburner & Friston. Nonlinear spatial noralisation using basis functions. Huan Brain Mapping 7: (1999). * Thévenaz et al. Interpolation revisited. IEEE Trans. Med. Iaging 19: (2000). * Andersson et al. Modeling geoetric deforations in EPI tie series. Neuroiage 13: (2001). * Ashburner & Friston. Unified Segentation. NeuroIage 26: (2005). * Ashburner. A Fast Diffeoorphic Iage Registration Algorith. NeuroIage 38: (2007). Preprocessing overview fmri tie-series REALIGN Anatoical MRI COREG TPMs SEGMENT NORM WRITE Input Output Segentation Transforation (seg_sn.at) Kernel SMOOTH Motion corrected Mean functional (Headers changed) MNI Space ANALYSIS Preprocessing (fmri only) fmri tie-series TPMs Input Output Segentation Preprocessing overview fmri tie-series Anatoical MRI TPMs Input Output Segentation Mean functional Transforation (seg_sn.at) Kernel Transforation (seg_sn.at) Kernel REALIGN SEGMENT NORM WRITE SMOOTH REALIGN COREG SEGMENT NORM WRITE SMOOTH Motion corrected MNI Space ANALYSIS Motion corrected Mean functional (Headers changed) MNI Space ANALYSIS
18 Preprocessing with Dartel fmri tie-series Anatoical MRI TPMs... DARTEL CREATE TEMPLATE REALIGN COREG SEGMENT DARTEL NORM 2 MNI & SMOOTH w.a.caan@ac.nl Motion corrected Mean functional (Headers changed) ANALYSIS
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