Bibliography on Image Registration
Finn Årup
Nielsen
CIMBI
at
DTU Informatics
and
NRU
Rigshospitalet
Lyngby and Copenhagen, Denmark
$Revision: 1.115 $
$Date: 2008/07/02 12:26:15 $
Abstract:
Reference for image registration are collected.
The focus is on image registration for the human brain, particularly
for functional neuroimaging.
This includes geometrically unwarping of EPIs, intrasubject motion
correction, intersubject atlas registration, etc.
Pointers to image registration programs are given as well as a list
of brain templates.
This structured bibliography is part of a larger collection of
bibliographies see
http://www.imm.dtu.dk/~
fn/bib/Nielsen2001Bib/.
The bibliography is written in LATEX and BIBTeX and should be
available both as HTML and PostScript.
The bibliography is probably far from complete, but new references
are added whenever the author finds new material and has the time to
add them.
You can email the author if corrections are required or you have
found references that you fell ought to be included:
fn@imm.dtu.dk.
Acknowledgment goes to
Mark
Jenkinson,
Thomas E. Nichols
via SPM
Extensions, and funding was
providing through
European Union project
MAPAWAMO,
International Neuroimaging
Consortium
(INC) American HBM project,
THOR Center for
Neuroinformatics, the
Villum Kann Rasmussen
Foundation
and the Lundbeck Foundation.
- Image transformation
- Cost functions
- Spatial resampling
- Correction for geometric distortion.
- Motion alignment tools
- Coregistration tools
- Spatial normalization algorithms and software.
A star (``*'') indicates that a public program is available.
- Templates: Some of the standard human brains used to atlas
warping
- Animal templates
- Validation
co-registration, image co-registration, image matching, image
realignment, image registration, inter-subject registration, linear
registration, matching, motion
correction, multi-modal image matching, multimodality matching,
realignment, registration, registration techniques, resampling,
reslicing, rigid matching, robust registration, spatial resampling,
spatial interpolation, warping.
[Toga, 1998] is an edited volume about brain warping.
[Bro-Nielsen, 1996] is a Ph. D. thesis which summarizes
some of the methods in operation in 1996.
Another is [Maintz and Viergever, 1998].
A general image registration survey is found in [Brown, 1992].
Table 1:
Image transformations. Motion
models. Restrictions on the motion.
Category |
Subcategory |
Subsubcategory |
Description |
Reference |
Rigid |
|
|
Only rotation and translation |
|
Non-rigid |
Similarity |
|
Rigid body and global scaling |
|
-- |
Affine |
|
Rotation, translation and scaling |
|
-- |
Nonlinear |
Polynomial basis |
E.g., AIR |
[Ingvar et al., 1994] |
-- |
-- |
Cosine basis |
E.g., SPM |
|
-- |
-- |
Thin-plate splines |
|
[Bookstein, 1989,Evans et al., 1991,Evans et al., 1994] |
-- |
-- |
Elastic |
|
[Miller et al., 1993], e.g., FMG |
-- |
-- |
Fluid |
|
[D'Agostino et al., 2004] |
-- |
-- |
Nagel-Engelmann |
|
[Nagel and Enkelmann, 1986,Hermosillo et al., 2001] |
-- |
-- |
Piecewise affine |
E.g., Talairach |
|
-- |
-- |
Infinitesimal affine |
|
[Nielsen et al., 2002] |
|
Table 1 display the different types of
image transformations or ``motion
models''.
These can both be performed in 2D and 3D.
Linear transformation is only global scaling and rotation, --
no translation (when presented in the stadard formulation).
With the use of homogeneous coordinates translation can be made
with a matrix multiplication, thus rigid, similarity and affine
transformation can be made with a matrix multiplication.
Shear transformation can make a parallelogram from a rectangle.
Nonlinear warps can have a ``symmetric prior''
[Ashburner et al., 2000,Ashburner et al., 1999].
The transformation can be confined to a specific dimension, e.g.,
inplane realignment.
Table 2:
Cost functions: Discrepancy and similarity
measures. See also [Jenkinson et al., 2002, table 1].
Type |
Subtype |
Description |
Reference |
Point |
|
|
[Arun et al., 1987] |
Point |
External fiducial markers |
|
|
|
Internal landmarks |
E.g., ``head of caudate'' and
other matched with Procustes algorithm
(least squares) |
[Evans et al., 1994], Evans, 1991 |
-- |
Robust |
Robust alignment with Rayleigh-Bessel function |
[Schormann and Dabringhaus, 2001] |
Line |
|
|
|
Plane |
|
``Surface Matching Technique''??? |
[Pellizzari et al., 1989] |
Volume |
|
|
[Collins et al., 1994] |
-- |
Square distance |
`Least square' or
mismatch |
|
-- |
Normalized correlation |
|
|
-- |
Correlation coefficient |
|
|
-- |
|
|
|
-- |
Ratio image uniformity |
`Wood's criteria' |
[Woods et al., 1992] |
-- |
Correlation ratio |
An asymmetric measure:
|
[Roche et al., 1998b,Roche et al., 1998a] |
-- |
Joint entropy |
|
|
-- |
Mutual information |
Also
refered to as relative entropy |
[Collignon et al., 1995,Viola and Wells III, 1995,Wells III et al., 1996,Maes et al., 1997,Studholme et al., 1997] |
-- |
Normalized mutual information |
|
[Studholme et al., 1998] |
-- |
Entropy correlation coefficient |
|
[Maes et al., 1997] |
-- |
With segmentation and a priori volumes |
|
[Ashburner et al., 1997] |
-- |
Mutual information to probabilistic tissue class labels |
|
[D'Agostino et al., 2004] |
|
Table 2 shows the cost functions associated with
image registration.
There are several variation of the cost functions:
- Rebinning in mutual information, e.g., 64 [Freire and Mangin, 2001a], or
the use of fuzzy membership, smoothing of joint histogram, also
called the ``grey level cooccurrence matrix'' (GLCM).
- Apodization with weighting of the cost function near the edges
of the image to avoid local minima [Jenkinson et al., 2002].
- Multigrid optimization where the image registration parameters
are first determined on a low-resolution image with large voxel
size. The parameters on this first level is used as initial values
of the parameters on the next finer level, see, e.g.,
[Maes et al., 1999]
- Excluding (mask) or weighting voxels differently,
e.g., to spatial
normalize patients with local lesions [Brett et al., 2001].
This functionality is available in the spatial normalization
procedure of the SPM2 and FSL package in
spm_normalise and flirt,
respectively, see Table 7.
Table 3 shows resampling and interpolation methods.
Further references for this step are
[Thévenaz et al., 2000,Meijering et al., 2001].
VTK implements affine, ``grid'' and thin-plate spline
transformations with nearest neighboor, trilinear or tricubic
interpolation on meshes, regular sampled, structure and unstructured
grids
http://www.kitware.com,
[Gobbi and Peters, 2003].
In Matlab 3D spatial resampling is implemented in the ``interp3.m''
function with nearest neighbor, linear, cubic and spline interpolation
methods.
Unwarping of EPI can be approached as an multi-modality
non-rigid image registration problem:
EPI scans can have geometric and intensity distortions and are to be
match with anatomical scans, e.g., a MRI T1 image
[Studholme et al., 1999,Studholme et al., 2000].
In [Kybic et al., 2000] the deformation field is modeled with splines.
[Andersson and Skare, 2002] describes an unwarping algorithm for
diffusion weighted EPI.
Other references for unwarping are
[Jezzard and Balaban, 1995,Munger et al., 2000].
An overview appears in [Hutton et al., 2002]
Table 4:
Correction for geometric distortion.
|
Motion correction
In motion correction the brain (and head) is typically regarded as a
rigid body where only rotation and translation in space are possible.
Introductions to this subject are [Cox, 1996,Brammer, 2001].
This type of registration can also be found under names such as
PET-PET registration, MRI-MRI registration
or MR/MR registration.
Some of the problems associated with motion correction are
- Interpolation errors when reslicing.
- `Movements at certain frequencies can interact with the physics
and temporal dynamics of the image acquisition protocol'
[Woods et al., 1999].
- In functional neuroimaging head movements can be correlated with
the paradigm [Hajnal et al., 1994,Bullmore et al., 1999].
This is also called task-related motion or stimulus
correlated motion. Even submillimeter movement can have an
influence [Field et al., 2000,Desmond and Atlas, 2000].
- Applying a non-robust motion correction on data with large
activations can produce spurious activations
[Freire and Mangin, 2001a,Freire and Mangin, 2001b].
This problem becomes more serious with larger MR scanner field
strengths (e.g., 3T compared with 1T) as well as larger activation
with addition of contrast agents such as MION.
Contour-based methods should be less sensitive to the confound
[Biswal and Hyde, 1997].
A robust algorithm is also describe by [Hsu et al., 2001].
- Differencies in the field of view among the images cause the
cost function to have many local mimima
[Jenkinson et al., 2002].
- Within scan motion can produce complex confounds that
separate slice-timing and realignment procedures cannot fully
correct and 4D algorithms are required
[Bannister et al., 2002].
A visualization method for the motion artifacts are described in
[Lacey et al., 1999,Thacker et al., 1999], see also
http://www.tina-vision.net/tina4/tina_tk_fmrimotion.html.
Tools for motion correction of 3D functional neuroimages are presented
in table 5.
Other motion correction methods are described in
[Minoshima et al., 1992,Snyder, 1996,Hill et al., 1994].
Motion correction for list-mode
PET is possible with optical
tracking
systems, e.g., with the POLARIS system
[Watabe et al., 2004].
A real-time system with real-time image-based motion detection during
fMRI scan and subsequent adjustment of slice position is described in [Thesen et al., 2000].
[Ardekani et al., 2001] compared 4 algorithms.
Given the range of noise and misalignments imposed the results tended
to show the following order (with the most accurate first): SPM99,
AFNI98, TRU, AIR.
The motion parameters (and derived parameters) can be included as
nuisance parameters in modeling, e.g., in columns of a design matrix
of a general linear
model
[Friston et al., 1996,Lund et al., 2005,Brett, 2005,Johnstone et al., 2005].
This can have large impact on the summary image obtained by
statistical tests [Lund et al., 2005].
[Grootoonk et al., 2000] find that interpolation errors
account for the residuals and suggest using sinusoids as the
transformation between the movement and the design variables.
An application for EEG-fMRI data with patients with epilepsy
is described in [Lemieux et al., 2007].
This approach included ``scan nulling''.
In MRI motion correction is usually performed for fMRI, but it might
have some utility for structural (anatomical) MRI (sMRI/aMRI) scans as well
[Kochunov et al., 2006].
Table 5:
Motion realignment tools.
A star `*' indicates that the tool is readily available on the
Internet.
|
|
|
Name |
Description |
Reference |
|
|
|
AFNI * |
Squared distance cost function implemented by the imreg and 2dImReg programs for 2D registration and 3dvolreg for 3D registration |
[Cox, 1996], http://afni.nimh.nih.gov/afni/AFNI_Help/imreg.html |
AIR * |
|
AIR 3 [Woods et al., 1998a],
AIR 5: http://bishopw.loni.ucla.edu/AIR5/ |
DART |
An algorithm that operates in the
Fourier domain (k-space) |
[Maas et al., 1997] |
Flirt * |
Motion correction using
Flirt (McFlirt)
Multiresolution optimization with apodization |
[Jenkinson et al., 2002,Jenkinson and Smith, 2001,Jenkinson and Smith, 2000,Bannister and Jenkinson, 2001]
http://www.fmrib.ox.ac.uk/fsl/flirt/ |
INRIAlign * |
Robust cost function |
[Freire et al., 2002,Freire and Mangin, 2001a],
http://www-sop.inria.fr/epidaure/software/INRIAlign/index.html |
Reg * |
Rigid-body or affine intramodal registration
software by Philippe Thévenaz |
[Thévenaz and Unser, 1998,Thévenaz et al., 1995,Unser et al., 1993]
http://bigwww.epfl.ch/thevenaz/registration/ |
RS |
``Registration software'' written as an AVS module with
brain surface segmentation and PET-PET and PET-MRI registration |
[Alpert et al., 1996] |
SPM * |
Implemented in the spm_realign.m function |
[Friston et al., 1995] |
TRU * |
(Seems to be the same as Thévenez' ``reg'') |
|
Coregistration
Coregistration or multimodality image registration is more
complicated than motion alignment since the gray-levels of the tissue
types in the different image modality, say PET and MRI, may not
correspond to each other.
Early voxel-intensity based algorithms are described in
[Woods et al., 1993,Ardekani et al., 1995,Andersson et al., 1995].
Table 6 displays coregistration tools.
Note that most image registration software that include some form of
the mutual information will be able to do co-registration.
Table 6:
Coregistration tools.
A star `*' denotes that the tool is easy available.
|
|
|
|
Name |
Transform |
Description |
Reference |
|
|
|
|
AIR * |
|
alignlinear in AIR3.0 |
[Woods et al., 1993]
http://www.loni.ucla.edu/NCRR/Software/AIR.html |
AMIR |
|
|
[Ardekani et al., 1995] |
CBA |
|
Commercial program from Applied Medical Imaging |
http://www.appmed.se |
Flirt * |
|
|
[Jenkinson et al., 2002,Jenkinson and Smith, 2001,Jenkinson and Smith, 2000]
http://www.fmrib.ox.ac.uk/fsl/flirt/ |
IIO |
Rigid |
``Interative Image overlay''. Manual
alignment. |
[Willendrup et al., 2004] |
IPS |
Rotation/translation |
``Interactive Point
Selection''. Semi-automated landmark-based with least-squares
optimization, applied for neuroreceptor studies. Part of the
MARS (Multiple Algorithms for Registration of Scans) package. |
[Willendrup et al., 2002a,Willendrup et al., 2002b,Willendrup et al., 2004],
http://www.nru.dk/people/willend/mars/ |
MATCH |
Non-linear |
|
[Hermosillo et al., 2002,Chef d'Hotel et al., 2002,Hermosillo et al., 2001]. Used for co-registration in, e.g., [Fize et al., 2003] |
MIPAV * |
Linear, thin plate spline |
Landmark-based least-squares
fitteing |
[Arun et al., 1987],
http://mipav.cit.nih.gov/ |
MIRIT |
|
Commercial coregistration program based on mutual
information |
[Maes et al., 1997],
http://bilbo.esat.kuleuven.ac.be/web-pages/downloads/Mirit/Mirit.html |
MPI (?) |
|
Interactive tool |
[Pietrzyk et al., 1994] |
MRIWarp * |
Non-linear |
General registration with mutual
information and correlation coefficient (and least squares) cost
function |
[Kjems et al., 1999a,Kjems, 1998,Kjems et al., 1999b]
http://hendrix.imm.dtu.dk/software/mriwarp/ |
RS |
|
``Registration software'' written as an AVS module with
brain surface segmentation and PET-PET and PET-MRI registration |
[Alpert et al., 1996] |
RView8 |
Rigid |
(mmvreg/rview) |
http://noodle.med.yale.edu/~ cs/software/software.html |
SPM * |
|
Both mutual information registration and registration
based on WM/GM/CSF segmented images are implemented (in SPM99).
SPM2 incorporates a number of different cost functions related
to mutual information
(The ``Coregister'' button and the spm_coreg.m function) |
[Ashburner and Friston, 1997,Ashburner et al., 1997,Collignon et al., 1995,Wells III et al., 1996,Maes et al., 1997,Studholme et al., 1998],
http://www.fil.ion.ucl.ac.uk/spm/ |
IPS, IIO, AIR 5.0 and SPM99 are compared on MRI to FDG-PET and
altanserin-PET coregistration in [Willendrup et al., 2004].
SPM99 and AIR are found to perform between on simulated FDG-PET-to-MRI
co-registration than the manual methods of IPS and IIO.
With the altanserin radiotracer, where there it finds little or no
5HT2A binding in cerebellum, the manual methods perform better.
Another comparison of co-registration algorithms appears in [Pfluger et al., 2000].
Spatial normalization
Discussion of the origins of spatial normalization appears in
[Fox, 1995].
Early reference to spatial normalization are
[Fox et al., 1985,Friston et al., 1989].
Other names are inter-subject brain image registration, intersubject registration, atlas warping,
...
In functional neuroimaging spatial normalization insures that the
functional results can be compared to the anatomy in multiple subject
studies.
In [Poldrack and Devlin, 2007] the issues of reporting the functional
activation with respect to the anatomy is discussed.
Table 7 lists tools for spatial
normalization, while further spatial normalization methods are described in
[Bajcsy et al., 1983,Bajcsy and Kovacic, 1989,Gee et al., 1993,Kosugi et al., 1993,Minoshima et al., 1994,Davatzikos, 1996,Christensen et al., 1997,Kochunov et al., 2000,Thévenaz and Unser, 2000].
[Andersson and Thurfjell, 1997] report a system for intra and
intersubject PET registration (perhaps it is used in the
CBA program?).
[Thompson et al., 1997] describe a fluid deformation for
cortical surfaces.
A method for ``inter-mouse'' warping is described in
[Falangola et al., 2005].
Talairach normalization has been found to result in a ``sulcal
variation zone'' of 1.5-2.0 centimeters measured against landmarks
[Steinmetz et al., 1990].
For the medial temporal lobe standard deviation on landmarks have been
found to be one or three millimeter, depending on optimal or
suboptimal parameters in non-linear basis-based spatial normalization
[Salmond et al., 2002], see also
[Ramsøy, 2007, appendix 3].
The problems associated with spatial normalization of the hippocampus
have been discussed in [Krishnan et al., 2006].
AFNI, SPM99 and ART have been
compared in [Ardekani et al., 2004].
The effect of different spatial normalization (affine AIR, MRIWarp) is
evaluated on functional O-15 positron emission tomograhy (PET) data in
[Kjems et al., 1999a] with canonical variate analysis, and the
study finds that the non-linear MRIWarp procedure is superior to the affine.
An elastic warping is compared to and affine transformation and an
SPM96 registration in [Gee et al., 1997], and
it finds peak activation from an analysis of functional images higher
for the warping than for the affine procedure.
In [Davatzikos et al., 2001b] MR-MR SPM96, PET-PET SPM95,
MR-MR SPM99 and STAR are compared and it is found the STAR results in
the lowest -values.
The influence of the template has been
investigated with the four choices using SPM99 for spatial
normalization of PET FDG images [Gisbert et al., 2003]:
One choice with the default H20 template provided by SPM and two
choices with a constructed FDG templates.
One FDG template was constructed from the subjects by averaging
spatial normalized FDG PET images that was normalized to the default
SPM template, and another FDG template that was constructed by
averaging FDG images whose deformation was estimated from MRI images.
The last choice did not construct an FDG template and instead warped
the subject PET-scans based on deformations estimated from the MRI
images.
A reported maximum -score ranged from 4.13 to 4.60.
Table 7:
Spatial normalization algorithms and software.
A star (``*'') indicates that a public program is available.
|
|
|
Name |
Description |
Reference |
|
|
|
AIR3 * |
|
[Woods et al., 1998b,Woods et al., 1999]
http://bishopw.loni.ucla.edu/AIR3/ |
ANIMAL |
Also called MNI_ANIMAL. Nonlinear registration.
First step is similar to AutoReg. Second step uses a deformation
field |
[Collins et al., 1995],
http://www.bic.mni.mcgill.ca/users/louis/MNI_ANIMAL_home/readme/readme.html |
ART |
Many-parameters algorithm |
[Ardekani, 2003,Ardekani et al., 2004] |
AutoReg |
Also called MNI_AutoReg. Linear transformation with a
cross-correlation cost function |
[Collins et al., 1994],
http://www.bic.mni.mcgill.ca/users/louis/MNI_AUTOREG_home/readme/ |
CBA |
Translation, scaling, rotation and second
transformation |
[Greitz et al., 1991,Ingvar et al., 1994] |
CHSN * |
``Convex Hull Spatial Normalization'' |
[Lancaster et al., 1999,Downs et al., 1994]
http://ric.uthscsa.edu/projects/chsn/chsn.html |
DARTEL * |
Diffeomorphic image registration |
[Ashburner, 2007],
ftp://ftp.fil.ion.ucl.ac.uk/spm/spm5_updates |
FMG |
Elastic |
[Schormann and Zilles, 1998,Schormann et al., 1996],
Email Thorsten Schormann. |
HAMMER * |
Elastic |
[Shen and Davatzikos, 2002,Shen and Davatzikos, 2003,Davatzikos et al., 2001a],
https://www.rad.upenn.edu/sbia/software/index.html#hammer |
HBA (*) |
``Human Brain Atlas''. Linear and nonlinear image
registration and template |
[Roland et al., 1994]
http://www.dhbr.neuro.ki.se/Hba/ |
LIPSIA (*) |
Linear and nonlinear normalization in
the LIPSIA package |
[Lohmann et al., 2001,Thirion, 1998] |
MRIWarp * |
Non-linear warp |
[Kjems et al., 1999a,Kjems, 1998,Kjems et al., 1999b]
http://hendrix.imm.dtu.dk/software/mriwarp/ |
SN |
9-parameter affine transformation |
[Lancaster et al., 1995]
http://ric.uthscsa.edu/projects/spatialnormalization.html |
SPM * |
Default is a
basis function
in SPM99. SPM2 includes functionality to
weight/mask voxels. |
[Friston et al., 1995,Ashburner and Friston, 1996,Ashburner and Friston, 1999],
http://www.fil.ion.ucl.ac.uk/spm/ |
STAR |
Elastic warping |
[Davatzikos, 1997] |
Brain templates
A large part of the spatial normalization algorithms require a
target to match to: a template -- aka. ``anatomical
textbook'',
cf. [Miller et al., 1993]).
A number of the templates for the human brain is listed in
table 8.
Further templates/brain atlases are pointed to in
[Toga and Thompson, 2000].
There is a discrepancy between the Talairach and the MNI templates,
and a piecewise affine transformation between the two has been
suggested [Brett, 2002].
This does not fully compensate
[Chau and McIntosh, 2005,Lancaster et al., 2007,Lancaster et al., 2006].
According to John Ashburner an O-15 H2O template can be used to
normalize FDG PET image without ``disastrous'' results
SPM mailing list
2002-01-21.
Table 8:
Templates: Some of the standard human brains used in
stereotaxic alignment.
|
|
|
|
|
Name |
Age |
Modality |
Description |
Reference |
|
|
|
|
|
colin27
|
Adult |
T1 |
MNI single subject (Colin Holmes). Also used in
BrainWeb and the default template in
SPM96. (Approximately?) in the same space as MNI305
Also distributed with MRIcro as ch2. |
[Holmes et al., 1998], SPM99 spm_templates.man.
http://www.mrc-cbu.cam.ac.uk/Imaging/Common/downloads/Colin/. |
MNI |
Adult |
T1, T2, PD, EPI, PET, SPECT |
Name for the MNI*
templates |
|
MNI152 |
Adult |
T1, T2, PD |
Standard templates in SPM99, distributed volume are
smooth with 8mm FWHM in 2mm resolution |
SPM99 spm_templates.man |
MNI305 |
Adult |
T1 |
ICBM standard, also distributed in SPM99 |
SPM99
spm_templates.man, [Collins et al., 1994,Evans et al., 1993,Collins, 1994],
ftp://ftp.bic.mni.mcgill.ca/pub/avgbrain/ |
`Woods 1999' |
Adult |
T1, T2 EPI |
Based on ten subjects in Talairach scaled space |
[Woods et al., 1999] |
Visible Human |
Adult |
|
Brain from the Visible Human Project |
http://www.nlm.nih.gov/research/visible/visible_human.html |
VAPET |
Adult |
|
Used at the VA Medical Center, Minneapolis |
|
CBA |
|
Cryosections |
`Computerized brain atlas', Dept. Neuroradiology,
Karolinska Institute. Included in the CBA program
Also called ``Greitz space''. |
[Greitz et al., 1991,Seitz et al., 1990,Thurfjell et al., 1995] |
HBA |
|
|
`Human Brain Atlas' from Karolinska Institutet |
[Roland et al., 1994] |
ECHBA |
|
|
New HBA. Re-acquired HBA used in European Computerised Human
Brain Database |
[Schormann et al., 1999,Roland et al., 1999] |
`BIT' |
|
|
Warped single subject |
[Lancaster et al., 2001] |
EVA833 |
Elderly |
|
Based on 833 elderly subjects |
[Quinton et al., 1999] |
-- |
|
Ligand PET |
[carbonyl-11C]WAY-100635, [11C]raclopride |
[Meyer et al., 1999] |
-- |
Adults(?) |
PET L-DOPA |
Based on 12 subjects |
Andreas Meyer-Lindenberg,
SPM mailing list 2001-11-20 |
CCHMC |
Children |
T1 |
Template based on 148 children
age 5-18. |
http://www.irc.chmcc.org/chips.htm,
Marko Wilke,
http://www.irc.chmcc.org,
SPM mailing list 2001-12-17 |
PAN |
-- |
External measurements |
Preauricular-nasion
Used in EEG. Not a template. Coordinates defined on individual basis. |
|
SUIT |
Adult |
|
Cerebellum |
[Diedrichsen, 2006], http://www.bangor.ac.uk/~pss412/imaging/suit.htm |
Talairach |
(Elderly) |
Drawings |
Original Talairach images.
No MRI exists. |
[Talairach and Tournoux, 1988] |
Schmahmann |
Adult |
Drawings, JPG, (T1) |
Book with
images of cerebellum from colin27 |
[Holmes et al., 1998,Schmahmann et al., 2000,Schmahmann et al., 1999,Schmahmann et al., 1996,Makris et al., 1996] |
[Horsley and Clarke, 1908] describe a stereotaxic space for the
macaque defined from measurements on Macaca mulatta (Macacus rhesus)
and a few cases of Macaca fascicularis (Macacus cynomolgus).
Table 9:
Animal templates. See
http://www.kopfinstruments.com/Atlas/
for a list of animal brain atlases.
Name |
Species |
Modality |
Description |
Reference |
B2K |
Baboon |
T1 MPRAGE, O15-Water PET |
|
[Black et al., 2001b],
http://www.nil.wustl.edu/labs/kevin/ni/b2k/ |
N2K |
Macaca Nemestrina (pig-tailed macaque) |
T1, PET |
|
[Black et al., 2001a],
http://www.nil.wustl.edu/labs/kevin/ni/n2k/p1.htm |
`Pig space' |
Pig
(Göttingen
minipigTM) |
MRI |
|
[Andersen et al., 2001],
SPM Mailing list,
2001-8-2 |
Ratlas |
Rat |
MRI |
|
[Schweinhardt et al., 2003],
http://mr.imaging-ks.nu/expmr.htm |
(Rat) |
Rat |
|
|
[Schwarz et al., 2006] |
Template Atlas |
Macaca fascicularis |
Drawings |
Bicommisural
coordinate system with zero at anterior commissure |
http://www.elsevier.com/homepage/sah/pbm/ |
|
[Erwin et al., 1999] describes a functional atlas for the
monkey lateral geniculate nucleus with respect to directions in visual
space.
This is available as ``Atlas of a Rhesus Lateral Geniculate Nucleus
(LGN)'' from
http://soma.npa.uiuc.edu/labs/malpeli/atlas/.
From `Template Atlas' (TA) to [Szabo and Cowan, 1984] (SC)
These transformations were taken from http://www.elsevier.com/homepage/sah/pbm/atlas/Tempindex.html.
Table 10:
Validation
Type |
Description |
Reference |
Spatial normalization |
HBA, SPM(96) and ``linear'' compared on
PET |
[Sugiura et al., 1997], [Sugiura et al., 1999]? |
MRI/PET coregistration |
AIR and SPM(96) compared |
[Kiebel et al., 1997b,Kiebel et al., 1997a] |
CT, MR, PET coregistration |
Internet-based blinded evaluation of 8 algorithms |
[West et al., 1997],
http://www.vuse.vanderbilt.edu/~ image/registration/ |
Spatial normalization |
Comparison of an affine (AIR), a
polynomial (AIR), an cosine (SPM) and a elastic deformation
(FMG) |
[Crivello et al., 2002] |
Spatial normalization |
|
[Hellier et al., 2001,Hellier et al., 2002,Hellier et al., 2003] |
|
A list of validation studies are available in table 10.
A comparison of early image registration algorithms appears in
[Strother et al., 1994].
In ``The Retrospective Registration Evaluation Project''
[West et al., 1997,Fitzpatrick et al., 1998] a
number of algorithms for
CT-MR and PET-MR image registration has been evaluated and the
results are available on the Internet from
http://www.vuse.vanderbilt.edu/~ image/registration/
Uses of spatial normalization in image-guided neurosurgery (IGNS):
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- AFNI
- Motion correction
| Comparison and evaluations
- AIR
- Methods
| Motion correction
| Coregistration
| Comparison and evaluations
| Validation and comparison
- AMIR
- Coregistration
- anatomical textbook
- Brain templates
- ANIMAL
- Comparison and evaluations
- ART
- Comparison and evaluations
| Comparison and evaluations
- atlas warping
- Spatial normalization
- AutoReg
- Comparison and evaluations
- B2K
- Animal brain templates
- baboon
- Animal brain templates
- BrainWeb
- Brain templates
- CBA (program)
- Coregistration
| Spatial normalization
| Comparison and evaluations
| Brain templates
| Brain templates
- cerebellum
- Brain templates
- CHSN
- Comparison and evaluations
- colin27
- Brain templates
- coregistration
- Coregistration
- DART
- Motion correction
- DARTEL
- Comparison and evaluations
- EPI
- Geometric unwarping of EPI
- epilepsy
- Motion correction
- flirt
- Methods
| Motion correction
| Motion correction
| Coregistration
- FMG
- Methods
| Comparison and evaluations
| Validation and comparison
- FSL
- Methods
- FUGUE
- Geometric unwarping of EPI
- general linear model
- Motion correction
- Greitz space
- Brain templates
- HAMMER
- Comparison and evaluations
- HBA
- Comparison and evaluations
- IIO
- Coregistration
- INRIAlign
- Motion correction
- interpolation
- Motion correction
- IPS
- Coregistration
- landmark
- Coregistration
- least square
- Methods
- LIPSIA
- Comparison and evaluations
- list-mode PET
- Motion correction
- masking
- Methods
| Comparison and evaluations
- MATCH
- Coregistration
- McFlirt
- Motion correction
- MIPAV
- Coregistration
- MIRIT
- Coregistration
- motion correction
- Motion correction
- motion model
- Methods
- MPI
- Coregistration
- MRIcro
- Brain templates
- MRIWarp
- Coregistration
| Comparison and evaluations
- mutual information
- Methods
- N2K
- Animal brain templates
- optical tracking
- Motion correction
- PAN
- Brain templates
- PET
- list-mode
- Motion correction
- PET-PET registration
- Motion correction
- pig space
- Animal brain templates
- POLARIS
- Motion correction
- PRELUDE
- Geometric unwarping of EPI
- priors
- symmetric
- Methods
- Procustes
- Methods
- rat
- Animal brain templates
| Animal brain templates
- Ratlas
- Animal brain templates
- reg
- Motion correction
- relative entropy
- Methods
- resampling
- Methods
- RS
- Motion correction
| Coregistration
- RView8
- Coregistration
- scan nulling
- Motion correction
- SN
- Comparison and evaluations
- spatial normalization
- Spatial normalization
| Validation and comparison
- SPM
- Motion correction
| Coregistration
| Comparison and evaluations
- SPM2
- Methods
| Comparison and evaluations
- SPM96
- Brain templates
- SPM99
- Comparison and evaluations
- STAR
- Comparison and evaluations
- SUIT
- Brain templates
- template
- influence
- Comparison and evaluations
- transformation
- Methods
- TRU
- Motion correction
- Unwarp
- Geometric unwarping of EPI
- VAPET
- Brain templates
- Visible Human
- Brain templates
- VTK
- Methods
- warping
- Spatial normalization
Finn Årup Nielsen
2010-04-23