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MNI Space and Talairach Space 转换
发起人:chmarka  回复数:11  浏览数:16996  最后更新:2012-6-13 12:37:50 by xufenghu1

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2010-6-21 22:10:25
chmarka





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MNI Space and Talairach Space 转换

MNI Space and Talairach Space 转换方法

1.用xjview 调用spm.mat文件,通过数据库查询出定位结果。

2. 用mni2tal.m在matlab下运行,把mni 坐标转为talairach 坐标。

说明如下:

% rotn  = spm_matrix([0 0 0 0.05]); % similar to Rx(eye(3),-0.05), DLW
0031 M2T.rotn  = [      1         0         0         0;
0032                    0    0.9988    0.0500         0;
0033                    0   -0.0500    0.9988         0;
0034                    0         0         0    1.0000 ];
0035
0036
0037 % upz   = spm_matrix([0 0 0 0 0 0 0.99 0.97 0.92]);
0038 M2T.upZ   = [ 0.9900         0         0         0;
0039                    0    0.9700         0         0;
0040                    0         0    0.9200         0;
0041                    0         0         0    1.0000 ];
0042
0043
0044 % downz = spm_matrix([0 0 0 0 0 0 0.99 0.97 0.84]);
0045 M2T.downZ = [ 0.9900         0         0         0;
0046                    0    0.9700         0         0;
0047                    0         0    0.8400         0;
0048                    0         0         0    1.0000 ];
0049
0050 % from original mni2tal...
0051 %upT   = spm_matrix([0 0 0 0.05 0 0 0.99 0.97 0.92]);
0052 %downT = spm_matrix([0 0 0 0.05 0 0 0.99 0.97 0.84]);
0053
0054               
0055
0056 return
0057
0058
0059 % from http://www.mrc-cbu.cam.ac.uk/Imaging/mnispace.html
0060 %
0061 % Approach 2: a non-linear transform of MNI to Talairach
0062 %
0063 % An alternative is to use some sort of transformation that
0064 % may differ for different brain areas. One method might be
0065 % to do an automated non-linear match of the MNI to the
0066 % Talairach brain. For example, you could apply an SPM or
0067 % AIR warping algorithm. However, there are two problems
0068 % here. First, as we stated above, we do not have an MRI
0069 % image of the brain in the Talairach atlas, which was a
0070 % post-mortem specimen. Second, the automated non-linear
0071 % transforms produce quite complex equations relating the
0072 % two sets of coordinates.
0073 %
0074 % An alternative is to apply something like the transform
0075 % that Talairach and Tournoux designed; here different
0076 % linear transforms are applied to different brain regions.
0077 % This is the approach I describe below.
0078 %
0079 % To get a good match for both the temporal lobes and the
0080 % top of the brain, I used different zooms, in the Z (down/up)
0081 % direction, for the brain above the level of the AC/PC line,
0082 % and the brain below. The algorithm was:
0083 %
0084 % I assumed that the AC was in the correct position in the MNI
0085 % brain, and therefore that no translations were necessary;
0086 % Assumed that the MNI brain was in the correct orientation in
0087 % terms of rotation around the Y axis (roll) and the Z axis (yaw);
0088 % Using the SPM99b display tool, I compared the MNI brain to the
0089 % images in the Talairach atlas;
0090 %
0091 % Compared to the atlas, the MNI brain seemed tipped backwards,
0092 % so that the cerebellar / cerebral cortex line in the sagittal
0093 % view, at the AC, was too low. Similarly, the bottom of the
0094 % anterior part of the corpus collosum seemed too high. I
0095 % therefore applied a small (0.05 radian) pitch correction to
0096 % the MNI brain;
0097 %
0098 % Matching the top of the MNI brain to the top of the brain in
0099 % the atlas, required a zoom of 0.92 in Z. Similarly a Y zoom
0100 % of 0.97 was required as a best compromise in matching the front
0101 % and back of the MNI brain to the atlas. The left / right match
0102 % required a 0.99 zoom in X;
0103 %
0104 % The transform above provided a good match for the brain superior
0105 % to the AC/PC line, but a poor match below, with the temporal lobes
0106 % extending further downwards in the MNI brain than in the atlas. I
0107 % therefore derived a transform for the brain below the AC/PC line,
0108 % that was the same as the transform above, except with a Z zoom of
0109 % 0.84;
0110 %
0111 % This algorithm gave me the following transformations:
0112 %
0113 % Above the AC (Z >= 0):
0114 %
0115 % X'= 0.9900X
0116 %
0117 % Y'=  0.9688Y +0.0460Z
0118 %
0119 % Z'= -0.0485Y +0.9189Z
0120 %
0121 %
0122 % Below the AC (Z < 0):
0123 %
0124 % X'= 0.9900X
0125 %
0126 % Y'=  0.9688Y +0.0420Z
0127 %
0128 % Z'= -0.0485Y +0.8390Z
0129 %
0130 %
0131 % The matlab function mni2tal.m implements these transforms.
0132 % It returns estimated Talairach coordinates, from the
0133 % transformations above, for given points in the MNI brain.
0134 % To use it, save as mni2tal.m somewhere on your matlab path.
0135 %
0136 % So, taking our example point in the MNI brain, X = 10mm, Y = 12mm, Z = 14mm:
0137 %
0138 % With the mni2tal.m function above on your path, you could
0139 % type the following at the matlab prompt:
0140 %
0141 %
0142 % mni2tal([10 12 14])
0143 %
0144 % Which would give the following output (see above):
0145 %
0146 %
0147 % ans =
0148 %
0149 %     9.9000   12.2692   12.2821
0150 %
0151 %
0152 % which is, again, an estimate of the equivalent X, Y and Z
0153 % coordinates in the Talairach brain.
0154 %
0155 % The inverse function, tal2mni.m, gives MNI coordinates for
0156 % given Talairach coordinates, using the same algorithm.
0157 %
0158 % We could of course do a more complex transform to attempt
0159 % to make a closer match between the two brains. The approach
0160 % above is only intended to be preliminary. It does have the
0161 % advantage that it is very ****, and therefore the distortions
0162 % involved are easy to visualise, and unlikely to have dramatic
0163 % unexpected effects.
0164 %
0165 % Incidentally, if you use the above transform, and you want to
0166 % cite it, I suggest that you cite this web address. The transform
0167 % is also mentioned briefly in the following papers: Duncan, J.,
0168 % Seitz, R.J., Kolodny, J., Bor, D., Herzog, H., Ahmed, A., Newell, F.N.,
0169 % Emslie, H. "A neural basis for General Intelligence", Science (21 July
0170 % 2000), 289 (5478), 457-460; Calder, A.J., Lawrence, A.D. and
0171 % Young,A.W. "Neuropsychology of Fear and Loathing" Nature Reviews
0172 % Neuroscience (2001), Vol.2 No.5 352-363

 

 

2010-6-21 22:14:28
chmarka





副主任医生

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mnispace 转换软件

mnispace 转换软件

运行方法:解压后拷贝mni2tal.m到spm目录下,在matlab下运行mni2tal([x y z])则输出talairach 坐标x y z.

 

mni2tal.rar

2010-6-21 22:18:47
chmarka





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Approach 1: redo the affine transform
This very reasonable approach has been posted to the SPM mailing list in 1998 by Andreas Meyer-Lindenberg, of NIMH%26#46; The following is copied from the text of that message, with a small correction (kindly pointed out to me by Darren Gitelman on the SPM mailing list):"I have taken the SPM 95 PET template (which is reasonably "Talairach 88-compatible", as far as I know) and used the spatial normalization algorithm of SPM 96 with an affine transform to map it onto the SPM 96 template (which is "MNI compatible")%26#46; If you do this and disregard parameter values for the affine transform that are very small, you come up with the following formulae for translating between the two coordinate systems: To get from McGill [MNI] -SPM96-coordinates to Talairach 88-SPM 95 coordinates: X' = 0%26#46;88X-0%26#46;8 Y' = 0%26#46;97Y-3%26#46;32 Z' = 0%26#46;05Y+0%26#46;88Z-0%26#46;44" So, to get a best guess at where your MNI point would be on the Talairach atlas, using the method described above, you can use matlab%26#46; Place the following matlab function somewhere in your matlab path (save the following as aff_mni2tal%26#46;m): function outpoint = aff_mni2tal(inpoint)
2010-6-21 22:19:42
chmarka





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There are two problems with this approach:

 The first problem is very ****; we have no MRI scan of the brain in the Talairach atlas%26#46; The SPM95 brain was smaller than the MNI brain, but it is still not a perfect match for the Talairach atlas%26#46;

The second problem is the same as the problem with the orginal transform done by the MNI: the brains are a different shape%26#46; For example, because the temporal lobes are fatter in the MNI brain, the affine transform needs to squeeze these down, by multplying the Z coordinates by a factor of about 0%26#46;88%26#46; However, this results in the top of the brain being pulled rather too far down, so that it is about 6mm below the highest point on the Talairach atlas%26#46;

2010-6-22 8:01:25
chmarka





副主任医生

角  色:网委
发 帖 数:1324
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注册时间:2009-2-8

Approach 2:

a non-linear transform of MNI to Talairach
An alternative is to use some sort of transformation that may differ for different brain areas. One method might be to do an automated non-linear match of the MNI to the Talairach brain. For example, you could apply an SPM or AIR warping algorithm. However, there are two problems here. First, as we stated above, we do not have an MRI image of the brain in the Talairach atlas, which was a post-mortem specimen. Second, the automated non-linear transforms produce quite complex equations relating the two sets of coordinates.

An alternative is to apply something like the transform that Talairach and Tournoux designed; here different linear transforms are applied to different brain regions. This is the approach I describe below.

To get a good match for both the temporal lobes and the top of the brain, I used different zooms, in the Z (down/up) direction, for the brain above the level of the AC/PC line, and the brain below. The algorithm was:

I assumed that the AC was in the correct position in the MNI brain, and therefore that no translations were necessary;
Assumed that the MNI brain was in the correct orientation in terms of rotation around the Y axis (roll) and the Z axis (yaw);
Using the SPM99b display tool, I compared the MNI brain to the images in the Talairach atlas;
Compared to the atlas, the MNI brain seemed tipped backwards, so that the cerebellar / cerebral cortex line in the sagittal view, at the AC, was too low. Similarly, the bottom of the anterior part of the corpus collosum seemed too high. I therefore applied a small (0.05 radian) pitch correction to the MNI brain;
Matching the top of the MNI brain to the top of the brain in the atlas, required a zoom of 0.92 in Z. Similarly a Y zoom of 0.97 was required as a best compromise in matching the front and back of the MNI brain to the atlas. The left / right match required a 0.99 zoom in X;
The transform above provided a good match for the brain superior to the AC/PC line, but a poor match below, with the temporal lobes extending further downwards in the MNI brain than in the atlas. I therefore derived a transform for the brain below the AC/PC line, that was the same as the transform above, except with a Z zoom of 0.84;
This algorithm gave me the following transformations:

Above the AC (Z >= 0):

X'= 0.9900X

Y'= 0.9688Y +0.0460Z

Z'= -0.0485Y +0.9189Z

Below the AC (Z < 0):

X'= 0.9900X

Y'= 0.9688Y +0.0420Z

Z'= -0.0485Y +0.8390Z

2010-6-22 8:01:48
chmarka





副主任医生

角  色:网委
发 帖 数:1324
经 验 值:1612
注册时间:2009-2-8

The matlab function mni2tal.m implements these transforms. It returns estimated Talairach coordinates, from the transformations above, for given points in the MNI brain. To use it, save as mni2tal.m somewhere on your matlab path.

So, taking our example point in the MNI brain, X = 10mm, Y = 12mm, Z = 14mm:

With the mni2tal.m function above on your path, you could type the following at the matlab prompt:

mni2tal([10 12 14])

Which would give the following output (see above):

ans =

9.9000 12.2692 12.2821

 

which is, again, an estimate of the equivalent X, Y and Z coordinates in the Talairach brain.

The inverse function, tal2mni.m, gives MNI coordinates for given Talairach coordinates, using the same algorithm.

We could of course do a more complex transform to attempt to make a closer match between the two brains. The approach above is only intended to be preliminary. It does have the advantage that it is very ****, and therefore the distortions involved are easy to visualise, and unlikely to have dramatic unexpected effects.

Incidentally, if you use the above transform, and you want to cite it, I suggest that you cite this web address. The transform is also mentioned briefly in the following papers: Duncan, J., Seitz, R.J., Kolodny, J., Bor, D., Herzog, H., Ahmed, A., Newell, F.N., Emslie, H. "A neural basis for General Intelligence", Science (21 July 2000), 289 (5478), 457-460; Calder, A.J., Lawrence, A.D. and Young,A.W. "Neuropsychology of Fear and Loathing" Nature Reviews Neuroscience (2001), Vol.2 No.5 352-363

2010-6-22 8:02:11
chmarka





副主任医生

角  色:网委
发 帖 数:1324
经 验 值:1612
注册时间:2009-2-8

Other methods for locating your activation
Other methods that you can use to work out where your activation is are;

Use the SPM 99 and 96 overlay displays to show you the activations on the MNI brain. If you know your anatomy well, or can see the equivalent structures in the Talairach atlas, then you may know where your activation is. Unfortunately, outside the primary sesorimotor cortices, the relation of functional areas to sulcal anatomy can be very variable;
Use the Talairach atlas, and try by eye to take into account the difference in brain size (given that the differences are relatively small). Obviously this can be inaccurate, and it is very difficult to standardise across labs.


 
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