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 $


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 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:

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.


List of Tables

  1. Image transformation
  2. Cost functions
  3. Spatial resampling
  4. Correction for geometric distortion.
  5. Motion alignment tools
  6. Coregistration tools
  7. Spatial normalization algorithms and software. A star (``*'') indicates that a public program is available.
  8. Templates: Some of the standard human brains used to atlas warping
  9. Animal templates
  10. 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.

General references

[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]
Plane   ``Surface Matching Technique''??? [Pellizzari et al., 1989]
Volume     [Collins et al., 1994]
-- Square distance `Least square' or $ L^2$ mismatch  
-- Normalized correlation    
-- Correlation coefficient    
-- Ratio image uniformity `Wood's criteria' [Woods et al., 1992]
-- Correlation ratio An asymmetric measure: $ \eta({\bf y}\vert{\bf x}) =
\frac{{\sf V}
{\sf E}[{\bf y}\vert{\bf x}]
{\sf V}[{\bf y}]
}$ [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:

Table 3: Spatial resampling. Partially from
Name Description Reference
Nearest neighbor    
Trilinear Also called `linear'  
Windowed sinc Also called `truncated sinc' e.g., [Hill et al., 1994]
Mixed linear/windowed sinc    
Unwindowed sinc    
Chirp-z Fourier domain analogue of sinc interpolation [Woods et al., 1999,Rabiner et al., 1969]
Mixed linear/chirp-z    

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, [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.

Geometric unwarping of EPI

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.
Name Method and description Reference
Field-map undistortion (*) Undistortion by a field (phase) map [Cusack and Papadakis, 2002,Cusack et al., 2003],
FUGUE * `FMRIB's Utility for Geometrically Unwarping EPIs' Program for EPI unwarping included in FSL [Jenkinson, 2001],
PRELUDE * Utility program for FUGUE
Unwarp * Correction of movement-by-susceptibility induced variance [Andersson, 2001],, toolbox for SPM99. Integrated in SPM2.

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

A visualization method for the motion artifacts are described in [Lacey et al., 1999,Thacker et al., 1999], see also

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],
AIR *   AIR 3 [Woods et al., 1998a], AIR 5:
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]
INRIAlign * Robust cost function [Freire et al., 2002,Freire and Mangin, 2001a],
Reg * Rigid-body or affine intramodal registration software by Philippe Thévenaz [Thévenaz and Unser, 1998,Thévenaz et al., 1995,Unser et al., 1993]
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 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]
AMIR     [Ardekani et al., 1995]
CBA   Commercial program from Applied Medical Imaging
Flirt *     [Jenkinson et al., 2002,Jenkinson and Smith, 2001,Jenkinson and Smith, 2000]
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],
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],
MIRIT   Commercial coregistration program based on mutual information [Maes et al., 1997],
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]
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) 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],

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].

Comparison and evaluations

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 $ P$-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 $ z$-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]
ANIMAL Also called MNI_ANIMAL. Nonlinear registration. First step is similar to AutoReg. Second step uses a deformation field [Collins et al., 1995],
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],
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]
DARTEL * Diffeomorphic image registration [Ashburner, 2007],
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],
HBA (*) ``Human Brain Atlas''. Linear and nonlinear image registration and template [Roland et al., 1994]
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]
SN 9-parameter affine transformation [Lancaster et al., 1995]
SPM * Default is a $ 7\times8\times7$ basis function in SPM99. SPM2 includes functionality to weight/mask voxels. [Friston et al., 1995,Ashburner and Friston, 1996,Ashburner and Friston, 1999],
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
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
MNI305 Adult T1 ICBM standard, also distributed in SPM99 SPM99, [Collins et al., 1994,Evans et al., 1993,Collins, 1994],
`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
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., Marko Wilke,, 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],
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]

Animal brain templates

[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 for a list of animal brain atlases.
Name Species Modality Description Reference
B2K Baboon T1 MPRAGE, O15-Water PET   [Black et al., 2001b],
N2K Macaca Nemestrina (pig-tailed macaque) T1, PET   [Black et al., 2001a],
`Pig space' Pig (Göttingen minipigTM) MRI   [Andersen et al., 2001], SPM Mailing list, 2001-8-2
Ratlas Rat MRI   [Schweinhardt et al., 2003],
(Rat) Rat     [Schwarz et al., 2006]
Template Atlas Macaca fascicularis Drawings Bicommisural coordinate system with zero at anterior commissure

[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


From `Template Atlas' (TA) to [Szabo and Cowan, 1984] (SC)

AP$\displaystyle _{\text{SC}}$ $\displaystyle =$   AP$\displaystyle _{\text{TA}} + 17\text{mm},$ (1)
DV$\displaystyle _{\text{SC}}$ $\displaystyle =$   DV$\displaystyle _{\text{TA}} + 4\text{mm},$ (2)

and from `Template atlas' to [Shantha et al., 1968] (SMB)

AP$\displaystyle _{\text{SMB}}$ $\displaystyle =$   AP$\displaystyle _{\text{TA}} + 17\text{mm},$ (3)
DV$\displaystyle _{\text{SMB}}$ $\displaystyle =$   DV$\displaystyle _{\text{TA}} + 8\text{mm}.$ (4)

These transformations were taken from

Validation and comparison

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], 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 image/registration/


Image-guided neurosurgery

Uses of spatial normalization in image-guided neurosurgery (IGNS): [Nowinski et al., 2000,Nowinski et al., 1998]. [St-Jean et al., 1998] use a deformable version of the Schaltenbrand and Wahren atlas for the basal ganglia and thalamus. Database construction: [Finnis et al., 2000].

Morphometric analysis

Bookstein, 1996, Biometrics, biomathematics and the morphometric synthesis

Unclassified references


Alpert et al., 1996
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Improved methods for image registration.
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Andersen et al., 2001
Andersen, F., Rodell, A. B., Danielsen, E. H., Gjedde, A., and Cumming, P. (2001).
Automatic registration of 3D-MR volumetric pig brain data into stereotactic standard coordinates (pig space).
NeuroImage, 13(6):S1295.

Andersson, 2001
Andersson, J. L. R. (2001).
Modeling geometric deformations in EPI time series.
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Andersson and Skare, 2002
Andersson, J. L. R. and Skare, S. (2002).
A model-based method for retrospective correction of geometric distortions in diffusion-weighted EPI.
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Andersson et al., 1995
Andersson, J. L. R., Sundin, A., and Valind, S. (1995).
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Andersson and Thurfjell, 1997
Andersson, J. L. R. and Thurfjell, L. (1997).
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Ardekani et al., 1995
Ardekani, B., Braun, M., Hutton, B. F., Kanno, I., and Iida, H. (1995).
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Ardekani, 2003
Ardekani, B. A. (2003).
An improved method for intersubject registration in 3D volumetric brain MRI.
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Ardekani et al., 2001
Ardekani, B. A., Bachman, A. H., and Helpern, J. A. (2001).
A quantitative comparison of motion detection algorithms in fMRI.
Magnetic Resonance Imaging, 19(7):959-963. PMID: 11595367. Comparison of four motion realignment algorithms: TRU, SPM99, AFNI98 and AIR. All could provide subvoxel precision and most submillimeter precision. Within the range of noise and initial misalignment impose the results showed that SPM99 and AFNI98 tended to be the best followed by TRU and finally AIR.

Ardekani et al., 2004
Ardekani, B. A., Bachman, A. H., Strother, S. C., Fujibayashi, Y., and Yonekura, Y. (2004).
Impact of inter-subject image registration on group analysis of fMRI data.
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Arun et al., 1987
Arun, K. S., Huang, T. S., and Blostein, S. D. (1987).
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Ashburner, 2007
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A fast diffeomorphic image registration algorithm.
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Ashburner et al., 1999
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Ashburner et al., 2000
Ashburner, J., Andersson, J. L. R., and Friston, K. J. (2000).
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Ashburner and Friston, 1996
Ashburner, J. and Friston, K. J. (1996).
Fully three-dimensional nonlinear spatial normalization.
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Ashburner and Friston, 1997
Ashburner, J. and Friston, K. J. (1997).
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Ashburner and Friston, 1999
Ashburner, J. and Friston, K. J. (1999).
Nonlinear spatial normalization using basis functions.
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Special Issue: Proceedings of the Brainmap '98 Workshop. Describes construction of T1 and T2 MRI brain atlases in Talairach space based on ten subjects. The T1 template was constructed with an initial affine intersubject transformation, a nonlinear 495-parameter intersubject warping, a rigid body atlas transformation to Talairach space, and finally a scaling to fit the extent of the Talairach atlas. The T2 EPI template was constructed by intermodality registration with the T1 images. Also describes motion alignment.

Woods et al., 1998a
Woods, R. P., Grafton, S. T., Holmes, C. J., Cherry, S. R., and Mazziotta, J. C. (1998a).
Automated image registration. I general methods and intrasubject, intramodality validation.
Journal of Computer Assisted Tomography, 22(1):139-152. PMID: 9448779.

Woods et al., 1998b
Woods, R. P., Grafton, S. T., Watson, J. D. G., Sicotte, N. L., and Mazziotta, J. C. (1998b).
Automated image registration. II. intersubject validation of linear and nonlinear models.
Journal of Computer Assisted Tomography, 22(1):153-165. PMID: 9448780.

Woods et al., 1993
Woods, R. P., Mazziotta, J. C., and Cherry, S. R. (1993).
MRI-PET registration with automated algorithm.
Journal of Computer Assisted Tomography, 17(4):536-546.


Motion correction | Comparison and evaluations
Methods | Motion correction | Coregistration | Comparison and evaluations | Validation and comparison
anatomical textbook
Brain templates
Comparison and evaluations
Comparison and evaluations | Comparison and evaluations
atlas warping
Spatial normalization
Comparison and evaluations
Animal brain templates
Animal brain templates
Brain templates
CBA (program)
Coregistration | Spatial normalization | Comparison and evaluations | Brain templates | Brain templates
Brain templates
Comparison and evaluations
Brain templates
Motion correction
Comparison and evaluations
Geometric unwarping of EPI
Motion correction
Methods | Motion correction | Motion correction | Coregistration
Methods | Comparison and evaluations | Validation and comparison
Geometric unwarping of EPI
general linear model
Motion correction
Greitz space
Brain templates
Comparison and evaluations
Comparison and evaluations
Motion correction
Motion correction
least square
Comparison and evaluations
list-mode PET
Motion correction
Methods | Comparison and evaluations
Motion correction
motion correction
Motion correction
motion model
Brain templates
Coregistration | Comparison and evaluations
mutual information
Animal brain templates
optical tracking
Motion correction
Brain templates
Motion correction
PET-PET registration
Motion correction
pig space
Animal brain templates
Motion correction
Geometric unwarping of EPI
Animal brain templates | Animal brain templates
Animal brain templates
Motion correction
relative entropy
Motion correction | Coregistration
scan nulling
Motion correction
Comparison and evaluations
spatial normalization
Spatial normalization | Validation and comparison
Motion correction | Coregistration | Comparison and evaluations
Methods | Comparison and evaluations
Brain templates
Comparison and evaluations
Comparison and evaluations
Brain templates
Comparison and evaluations
Motion correction
Geometric unwarping of EPI
Brain templates
Visible Human
Brain templates
Spatial normalization

Finn Årup Nielsen 2010-04-23