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Unwarping. In order to assign an observed response to a particular brain structure, or cortical area, the data must conform to a known anatomical space.

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Presentation on theme: "Unwarping. In order to assign an observed response to a particular brain structure, or cortical area, the data must conform to a known anatomical space."— Presentation transcript:

1 Unwarping

2 In order to assign an observed response to a particular brain structure, or cortical area, the data must conform to a known anatomical space. In order to combine data from different scans from the same subject, or data from different subjects it is necessary that they conform to the same anatomical frame of reference. Voxel-based analyses assume that the data from a particular voxel all derive from the same part of the brain. Violations of this assumption will introduce artifactual changes in the voxel values that may obscure changes, or differences, of interest. E.g. if movement of the subject in the scanner pushes a voxel from an area of low to high signal, this may register as a false-positive activation. Assumptions of statistical tests in functional imaging

3 Pre-processing steps to cope with violations of these assumptions All scans must conform to the same anatomical frame of reference: Realign the data to 'undo' the effects of subject movement during the scanning session. Data must conform to a known anatomical space: After realignment the data are then transformed using linear or nonlinear warps into a standard anatomical space. Finally, the data are usually spatially smoothed before entering the analysis proper.

4 Pre-processing steps to cope with violations of these assumptions All scans must conform to the same anatomical frame of reference: Realign the data to 'undo' the effects of subject movement during the scanning session. !UNWARP! Data must conform to a known anatomical space: After realignment the data are then transformed using linear or nonlinear warps into a standard anatomical space. Finally, the data are usually spatially smoothed before entering the analysis proper.

5 Errors after realignment After realignment, there can be residual errors in images for a number of reasons. The residual variance can be dealt with by assuming that it is related to subject movement. One way is to account for subject movement in the design matrix of the analysis proper, by including the movement parameters estimated from re-alignment as covariates.

6 Covarying for movement-related errors after realignment However, this may remove activations of interest if they are correlated with movement t max =13.38 No correction t max =5.06 Correction by covariation

7 Problems with covariation Covariation using movement parameters assumes only rigid deformation of the image between scans. BUT: images are sampled according to gradients of the magnetic field B, in 3 image dimensions. ω = γB ω = resonant frequency B = magnetic field strength By applying a gradient field across B 0, B varies according to position. There will only be one position at which 1 H spins are precessing at a particular resonant frequency, so can assign resultant signal to this location. Signals are assigned in 3 image dimensions by applying field gradients across B 0 in 3 dimensions. y z B 0 +G z z y z B 0 +G z z B 0 B 0 B 0 B 0 -G x x x B 0 +G x x B 0 -G z z B 0 -G z z

8 Non-rigid deformation Knowing the location at which 1 H spins will precess at a particular frequency and thus where the signal comes from is dependent upon correctly assigning a particular field strength to a particular location. If the field B 0 is homogeneous, then the image is sampled according to a regular grid and voxels can be localised to the same bit of brain tissue over subsequent scans by realigning, this is because the same transformation is applied to all voxels between each scan. If there are inhomogeneities in B 0, then different deformations will occur at different points in the field over different scans, giving rise to non-rigid deformation. B 0 Expect field strength to be B 0 here, so H atoms with signal associated with resonant frequency ω 0 to be located here. In fact, because of inhomogeneity, they are here.

9 Field inhomogeneities Due to microscopic gradients or variations in magnetic field strengths that occur at interfaces of substances of different magnetic susceptibility. E.g., metallic material (ferromagnetic) and the human body (diamagnetic). Also occurs close to tissue-air and tissue-bone interfaces such as around frontal sinuses. Field inhomogeneities have the effect that locations on the image are deflected with respect to the real object. A deformation field indicates the directions and magnitudes of location deflections throughout the FOV with respect to the real object. A deformation field indicates the directions and magnitudes of location deflections throughout the FOV with respect to the real object. igl.stanford.edu/~torsten/ct-dsa.html

10 Movement-by-inhomogeneity interactions Field inhomogeneities change with the position of the object in the field, so there can be non-rigid, as well as rigid distortion over subsequent scans. Field inhomogeneities change with the position of the object in the field, so there can be non-rigid, as well as rigid distortion over subsequent scans. The movement-by-inhomogeneity interaction can be observed by changes in the deformation field over subsequent scans. The amount of distortion is proportional to the absolute value of the field inhomogeneity and the data acquisition time. EPI is particularly sensitive to the effects of magnetic field inhomogeneities because it has long TR

11 Controlling for movement-by-inhomogeneity interactions One solution is to explicitly measure field inhomogeneity by use of a field-map (available in the FieldMap SPM toolbox). A field map then has to be generated for each scan in the time-series. One solution is to explicitly measure field inhomogeneity by use of a field-map (available in the FieldMap SPM toolbox). A field map then has to be generated for each scan in the time-series. Measurement of field-maps is complicated by noise, and rapid loss of signal towards the edges of the object. In practice, rather than generating a statistical field map for every image in the EPI data set, can compute how the statistical maps are warped over subsequent scans and then unwarp the statistical map itself in order to make accurate identification of activated areas. Computing how the images are warped over subsequent scans requires knowing how the deformation fields change with displacement of the subject, i.e. the derivatives of B with respect to displacement of the subject.

12 Principles of UNWARP Given the derivative of the field with respect to subject movement, and the movement parameters estimated from realignment, can predict the non-rigid deformation in the scan series. In practice, we know the non-rigid deformation (in terms of extra variance after realignment) and the subject movement (movement parameters) so we can estimate the derivatives of the field B 0 with respect to subject movement – thus estimate how the field is warped over the time series and undo this using UNWARP.

13 Movements modelled in UNWARP Translations and rotations in plane perpendicular to B 0 will not affect B 0, so only need to model derivatives of B 0 with respect to rotations out of perpendicular plane, i.e. pitching and rolling. Translations and rotations in plane perpendicular to B 0 will not affect B 0, so only need to model derivatives of B 0 with respect to rotations out of perpendicular plane, i.e. pitching and rolling,. x B 0 B 0 y z In UNWARP there is a (default) option to re-estimate the movement parameters with each unwarp iteration. This is recommended by John Ashburner. It is computed via a series of iterations; 1. estimate movement parameters (), 2. estimate deformation fields, B 0, 3. re-estimate movement with new model of magnetic field B 0 In UNWARP there is a (default) option to re-estimate the movement parameters with each unwarp iteration. This is recommended by John Ashburner. It is computed via a series of iterations; 1. estimate movement parameters (, ), 2. estimate deformation fields, B 0, 3. re-estimate movement with new model of magnetic field B 0

14 Modelling changes in B 0 The field B 0, which changes as a function of displacement, can be modelled by the first two terms of a Taylor expansion The field B 0, which changes as a function of displacement,, can be modelled by the first two terms of a Taylor expansion B 0 () = B 0 () + [( δ B 0 / δ ( δ B 0 / δ ) ] B 0 (, ) = B 0 (, ) + [( δ B 0 / δ ) + ( δ B 0 / δ ) ] The static deformation field, Changes in the deformation field with Which is the same throughout subject movement. Estimated via iteration The time series.Procedure in UNWARP. Calculated using Fieldmap in SPM It is possible to model the next term in the Taylor expansion as well, i.e. the second derivative of B with respect to, but this is not necessary. It is possible to model the next term in the Taylor expansion as well, i.e. the second derivative of B with respect to,, but this is not necessary.

15 Applying the deformation field to the image Once the deformation field has been modelled over time, the time-variant field is applied to the image. effect of sampling a regular object over a curved surface. The image is therefore re-sampled assuming voxels, corresponding to the same bits of brain tissue, occur at different locations over time.

16 Advantages of incorporating this in pre- processing One could include the movement parameters as confounds in the statistical model of activations. However, this may remove activations of interest if they are correlated with the movement. t max =13.38 No correction t max =5.06 Correction by covariation t max =9.57 Correction by Unwarp

17 UNWARP: Benefits and Limitations Although for small movements a limited portion of the total variance is removed, the susceptibility-by-movement interaction effects are quite localised to "problem" areas. For a subset of voxels in e.g. frontal-medial and orbitofrontal cortices and parts of the temporal lobes the reduction can be quite dramatic (>90%). However, UNWARP only tackles one source of variance after re- alignment, and other errors may arise from: - Susceptibility-dropout-by-movement interaction: Field inhomogeneities can also cause signal loss due to through-plane dephasing (which will not be rephased by encoding gradients that are all in-plane). - Susceptibility-dropout-by-movement interaction: Field inhomogeneities can also cause signal loss due to through-plane dephasing (which will not be rephased by encoding gradients that are all in-plane). - Spin-history effects: The signal will depend on how much longitudinal magnetisation has recovered (through T1 relaxation) since it was last excited (short TRlow signal). If the subject moves in the direction of increasing slice number between one excitation and the next, then the effective TR will be longer (resulting in increasing signal intensity). - Spin-history effects: The signal will depend on how much longitudinal magnetisation has recovered (through T1 relaxation) since it was last excited (short TRlow signal). If the subject moves in the direction of increasing slice number between one excitation and the next, then the effective TR will be longer (resulting in increasing signal intensity). - Slice-to-vol effects: The rigid-body model that is used by most motion-correction (e.g. SPM) methods assume that any movement will occur between scans. However there is also movement within scans – leading to further apparent shape changes. - Slice-to-vol effects: The rigid-body model that is used by most motion-correction (e.g. SPM) methods assume that any movement will occur between scans. However there is also movement within scans – leading to further apparent shape changes.

18 Summary Movement-by-inhomogeneity interactions can be accommodated during realignment using unwarp in SPM5 WARNING!! UNWARP can be computationally intensive, and therefore take a long time!

19 Jezzard, P. and Clare, S Sources of distortion in functional MRI data. Human Brain Mapping, 8:80-85 Andersson JLR, Hutton C, Ashburner J, Turner R, Friston K (2001) Modelling geometric deformations in EPI time series. Neuroimage 13: John Ashburners slides Paul Tofts MRI Physics Course at the IoN (slides not yet on the web – TBA) References


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