Say you have a statistic image that you created outside of SPM and
you want to overlay it on a structural image. The bare bones
solution is as follows.
Create a thresholded version of the image, where all
subthreshold voxels are set to NaN (See how to set NaNs below). Then...
Subject: Re: Your Message Sent on Fri, 19 Jan 2001 23:28:45 0800
From: John Ashburner <john@FIL.ION.UCL.AC.UK>
Date: Mon, 22 Jan 2001 11:40:24 +0000
To: SPM@JISCMAIL.AC.UK
I think it may take quite a bit of programming to get most of
the routines working with your data. One thing to get you
started would be to try an undocumented feature of spm_orthviews.m:
spm_orthviews('image',spm_get(1,'*.img','Select background image'));
spm_orthviews('addimage',1,spm_get(1,'*.img','select blobs image'));
Best regards,
John
Here's a different approach, which I understand will show the blobs in
a monotone color (instead of hot metal). Also, there are more bells
and whistles (it's less of a hack).
Subject: Re: overlaying resulting image
From: John Ashburner <john@fil.ion.ucl.ac.uk>
Date: Wed, 4 Oct 2000 10:37:01 +0100 (BST)
To: spm@mailbase.ac.uk, duann@salk.edu
 Would you please show me how to overlay a statistical map onto the brain
 template SPM uses. Let's say I have an analyzeformated statistical
 map obtained from other software. It was already normalized to the same
 coordinates as the template images SPM has. How can I overlay this image
 onto the SPM template just like the results shown in SPM convention.
The following is supposed to work. It uses a hidden undocumented
feature in SPM99, so it may contain bugs....
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
P1 = spm_get(1,'*.img','Specify background image');
P2 = spm_get(1,'*.img','Specify blobs image');
% Clear graphics window..
spm_clf
% Display background image..
h = spm_orthviews('Image', P1,[0.05 0.05 0.9 0.9]);
% Display blobs in red. Use [0 1 0] for green, [0 0 1] for blue
% [0.6 0 0.8] for purple etc..
spm_orthviews('AddColouredImage', h, P2,[1 0 0]);
% Update the display..
spm_orthviews('Redraw');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Best regards,
John
...to the top
In addition to setting NaN values to zero, this gem is also useful
as a basic skeleton for reading each slice, doing something to it,
and then writing it out.
Subject: Re: NaN values in beta values
From: John Ashburner <john@fil.ion.ucl.ac.uk>
Date: Mon, 23 Oct 2000 17:27:24 +0100 (BST)
To: spm@mailbase.ac.uk, steffejr@umdnj.edu
I can't think of any nice way of doing this with ImCalc, so I'm afraid
I'll have to show you a more labour intensive method that should be
modified slightly (as the filenames need changing) before pasting into
Matlab.
VI = spm_vol('original_beta.img');
VO = VI;
VO.fname = 'patched_beta.img';
VO = spm_create_image(VO);
for i=1:VI.dim(3),
img = spm_slice_vol(VI,spm_matrix([0 0 i]),VI.dim(1:2),0);
tmp = find(isnan(img));
img(tmp) = 0;
VO = spm_write_plane(VO,img,i);
end;
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Subject: Re: normalization of contrast images
From: John Ashburner
Date: Wed, 2 Aug 2000 14:37:38 +0100 (BST)
To: spm@mailbase.ac.uk, gpagnon@emory.edu
[...]
 And, while I am at it, does anybody know how to convert a mask that
 has zeros outside the brain to a mask that has NaN outside the brain?
 I tried ImCalc with something like 'i1(find(i1==0))=NaN', but it
 doesn't like it.
By default, ImCalc outputs the data as 16 bit integer with scalefactors.
There is no NaN representation for this, so the data would need to be
written as floating point or double precision floating point. I think
you can do this by typing something like:
P = spm_get(1,'*.img');
Q = 'output.img';
f = 'i1.*(i1./i1)';
flags = {0,0,spm_type('float'),1};
spm_imcalc_ui(P,Q,f,flags);
Best regards,
John
Note you can NaNout voxels below a threshold, say, 3:
 f = 'i1 + 0./(i1>3)'
Also note that this is a general way for making spm_imcalc write out
images with float or double precision; concisely
 f = 'sqrt(i1)'; % whatever you like
 spm_imcalc_ui('in.img','out.img',f,{[],[],spm_type('float')});
Using [],[] instead of 0,0 ensures that
default values will be used for those two flags.
...to the top
Subject: Re: tdistribution display (histogram) with spm99b
From: john@fil.ion.ucl.ac.uk (John Ashburner)
Date: Fri, 30 Jul 1999 14:06:20 +0100
To: spm@mailbase.ac.uk, jovicich@humc.edu
The attached program should produce the histograms you are after. To
call it, type:
V = spm_vol(spm_get(1,'*.img','Select image...'));
[n, x] = histvol(V, 100);
figure;
bar(x,n);
[...]
Regards,
John
The attached function is here histvol.m and
below
function [n, x]=histvol(V, nbins)
% Create Histogram of an image volume
% FORMAT [n, x]=histvol(V, nbins)
% V  mapped image volume (see spm_vol)
% nbins  number of bins to use.
% n  number of counts in each bin
% x  position of bin centres
%_______________________________________________________________________
% @(#)JohnsGems.html 1.42 05/02/02
if nargin==1, nbins = 256; end;
% determine range...
mx = Inf;
mn = Inf;
for p=1:V.dim(3),
img = spm_slice_vol(V,spm_matrix([0 0 p]),V.dim(1:2),1);
msk = find(isfinite(img));
mx = max([max(img(msk)) mx]);
mn = min([min(img(msk)) mn]);
end;
% compute histograms...
x = [mn:(mxmn+1)/nbins:mx];
n = zeros(size(x));
for p=1:V.dim(3),
img = spm_slice_vol(V,spm_matrix([0 0 p]),V.dim(1:2),1);
msk = find(isfinite(img));
n = n+hist(img(msk),x);
end;
return;
...to the top
Subject: Re: Mesh Plots of tmaps
From: John Ashburner <john@FIL.ION.UCL.AC.UK>
Date: Fri, 2 Feb 2001 11:14:20 +0000
To: SPM@JISCMAIL.AC.UK
 I am wishing to create Mesh plots in Matlab of the tmap produced
 from my SPM analysis. Being a somewhat Matlab virgin, I was wondering
 whether
 someone could possibly point me in the direction of exactly how to do this.

 Which mat file contains all tvalues in the statistic image?
The t values are stored in spmT_????.img files, where ???? refers to
the contrast number. You can read the values from these images into
Matlab something like:
pl = 30; % plane 30
fname = spm_get(1,'*.img','Name of t image');
V = spm_vol(fname);
M = spm_matrix([0 0 pl]);
img = spm_slice_vol(V,M,V.dim(1:2),1);
Displaying the values can be dome something like:
surf(img);
There are loads of commands for 3D visualisation in Matlab 5.x. You can
find out what these are by typing:
help graph3d
All the best,
John
...to the top
Subject: Re: raw data
From: John Ashburner <john@FIL.ION.UCL.AC.UK>
Date: Thu, 10 May 2001 14:33:19 +0100
To: SPM@JISCMAIL.AC.UK
> I have a very simple question. Where does one find and how does one go
> about extracting the raw data associated with a particular voxel?
I would write a very short script to do this. Try modifying and copypasting
the following....
* * * * * * * * * * * * * * * * * * * * * * * * * * * * 
V=spm_vol(spm_get(Inf,'*.img'));
x = 32;
y = 32;
z = 32;
dat = zeros(length(V),1);
for i=1:length(dat),
dat(i) = spm_sample_vol(V(i),x,y,z,0);
end;
* * * * * * * * * * * * * * * * * * * * * * * * * * * * 
The vector dat will contain the raw data from the voxel at 32,32,32.
Best regards,
John
...to the top
Subject: Re: ACPC positions
From: John Ashburner <john@fil.ion.ucl.ac.uk>
Date: Fri, 27 Oct 2000 15:00:05 +0100 (BST)
To: spm@mailbase.ac.uk, spm@fil.ion.ucl.ac.uk
[...]
The best that I can suggest is you try manually reorienting your
images via the <Display> button. Try different rotations and
translations until the image is displayed how you want it. The
attached Matlab function can then be used for reslicing the image(s)
in the transverse orientation, with 1mm isotropic resolution.
Best regards,
John
Attached file: reorient.m
Error in c matrix fixed, June 6, 2001 TEN
Subsequently, John offered modifications to use the native voxel size, which have been incorporated...
See also SPM5 Gem2 & Gem3
Subject: Re: reorient.m
From: John Ashburner <john@FIL.ION.UCL.AC.UK>
Date: Thu, 14 Dec 2000 17:04:38 +0000
To: SPM@JISCMAIL.AC.UK
 I would like to reorient some images in two ways,
 using the usefull function reorient() of John Ashburner.

  first is to keep the voxel size intact,
 but still reorienting in the transverse plane
Without testing the code, and without too much thought, I think
the modification to do this is involves something like changing from:
mat = spm_matrix([mn1]);
dim = (mat\[mx 1]')';
to something like:
vox = spm_imatrix(V.mat);
vox = vox(7:9);
mat = spm_matrix([0 0 0 0 0 0 vox])*spm_matrix([mn1]);
dim = (mat\[mx 1]')';

  second is to reorient in the coronal plane,
 with 1x1x1 mm resol.
I think this is involves something like changing from:
mat = spm_matrix([mn1]);
to something like:
mat = spm_matrix([0 0 0 pi/2])*spm_matrix([mn1]);
or maybe:
mat = spm_matrix([0 0 0 pi/2])*spm_matrix([mn1]);

 In the two cases, the final image should have no .mat file
 and be resliced using sinc ...
To reslice using sinc interpolation, you change from:
img = spm_slice_vol(V,M,dim(1:2),1);
to something like:
img = spm_slice_vol(V,M,dim(1:2),6);
I hopehese suggestions work.
Best regards,
John
If you want to set arbitrary voxel size, just set vox as
desired in the fix above, but then be sure to force dim to be an
integer.
For example, I wanted to increase the resolution of my images by a
factor of three, so I did
vox = spm_imatrix(V.mat);
vox = vox(7:9)/3;
mat = spm_matrix([0 0 0 0 0 0 vox])*spm_matrix([mn1]);
dim = ceil(mat\[mx 1]')');
...to the top
This is for soley reslicing images, but it has a nice description of
how to create a basic transformation matrix.
Subject: Re: how to write out a resampled volume with new user specified
voxel size
From: John Ashburner <john@FIL.ION.UCL.AC.UK>
Date: Thu, 4 Jan 2001 10:57:52 +0000
To: SPM@JISCMAIL.AC.UK
 I would like to batch resample some volume images to isotropic
 voxels. Is there an SPM99 command line instruction (or menu option) that
 would permit me to write out a resampled volume with a user specified voxel
 size dimensions? I know this core function is implicit in several
 modules. I considered using Coregister or Spatial Normalization modules
 but these require a target or template volume of the desired resolution
 which doesn't always exist in my application.
 Many thanks for any suggestions.
You could try the attached function that I've just scribbled together. It
hasn't been tested, so I hope it works.
Best regards,
John
The attached function is here reslice.m and
below
function reslice(PI,PO,dim,mat,hld)
% FORMAT reslice(PI,PO,dim,mat,hld)
% PI  input filename
% PO  output filename
% dim  1x3 matrix of image dimensions
% mat  4x4 affine transformation matrix mapping
% from vox to mm (for output image).
% To define M from vox and origin, then
% off = vox.*origin;
% M = [vox(1) 0 0 off(1)
% 0 vox(2) 0 off(2)
% 0 0 vox(3) off(3)
% 0 0 0 1];
%
% hld  interpolation method.
%___________________________________________________________________________
% @(#)JohnsGems.html 1.42 John Ashburner 05/02/02
VI = spm_vol(PI);
VO = VI;
VO.fname = deblank(PO);
VO.mat = mat;
VO.dim(1:3) = dim;
VO = spm_create_image(VO); end;
for x3 = 1:VO.dim(3),
M = inv(spm_matrix([0 0 x3 0 0 0 1 1 1])*inv(VO.mat)*VI.mat);
v = spm_slice_vol(VI,M,VO.dim(1:2),hld);
VO = spm_write_plane(VO,v,x3);
end;
...to the top
Subject: Re: rot+trans
From: John Ashburner <john@fil.ion.ucl.ac.uk>
Date: Fri, 6 Oct 2000 16:15:23 +0100 (BST)
To: spm@mailbase.ac.uk, pauna@creatis.insalyon.fr
 1 how can I find the transformations
 (rot+translations) from the transformation matrix?
The relative translations and rotations between a pair of images
is encoded in the .mat files of the images. So for images F.img and
G.img, you would find the transformation by:
% The mat file contents are loaded by:
MF = spm_get_space('F.img');
MG = spm_get_space('G.img');
% A rigid body mapping is derived by:
MR = MF/MG; % or MR = MG/MF;
% From this, a set of rotations and translations
% can be obtained via:
params = spm_imatrix(MR)
% See spm_matrix for an explanation of what these
% parameters mean
...to the top
Subject: Re: rendering SPM96
From: John Ashburner <john@FIL.ION.UCL.AC.UK>
Date: Tue, 20 Feb 2001 16:22:05 +0000
To: SPM@JISCMAIL.AC.UK
SPM96 shows voxels that are considered to be between 5mm in front of and 20mm
behind the surface that is displayed. This is the same as for the "old style"
rendering of SPM99.
To change this depth, try changing line 131 of spm_render.m from:
msk = find(xyz(3,:) < (z1+20) & xyz(3,:) > (z15));
to something like:
msk = find(xyz(3,:) < (z1+5) & xyz(3,:) > (z15));
I would rather see the rendering done on the brain of either the same
subject that the data were acquired, or on a nice smooth average brain
surface. Rendering can be done on a smooth average in SPM99, and there
is also the capability of doing the rendering on to the individual subjects
MR. The rendering in SPM96 is done onto a single subject brain, which can
be quite misleading as it is not the same subject that the data comes
from. Depths behind the surface are derived from the single subject brain,
which can cause problems as spatial normalisation is not exact. An activation
that could be on the brain surface may appear a few mm in front of or behind
the single subject brain surface.
Another option within SPM99 is to use the brain extraction feature to
identify the approximate brain surface of a segmented structural image.
This would be saved as surf_*.mat. A routine similar the one attached
can then be used to render the blobs on to this surface.
Best regards
John
Attached file: fancy_rendering.m
...to the top
OK, this mail is actually from Matthew Brett, but it references code
written by John.
Subject: Re: Scale factor
From: Matthew Brett <matthewbrett@YAHOO.COM>
Date: Tue, 22 May 2001 18:47:15 0700 (21:47 EDT)
To: SPM@JISCMAIL.AC.UK
Dear Masahiro,
> I would like to incorporate scaling factors to many header files.
> It would be nice if I can incorporate multiple scaling factors saved
> in a text file into multiple header files. Is there a way to do
> this?
For the same problem, I have used an utility written
by John Ashburner many eons ago, called dbedit:
http://www.mrccbu.cam.ac.uk/Imaging/dbedit.html
You need to script it somehow, obviously, but the line in the file
setting your scale factor might look like:
dbedit myfile.hdr dime.funused1=$myval
where your scale factor is in the variable $myval
I've also been playing with some perl functions to do this kind of
thing, written by Andrew Janke:
http://www.cmr.uq.edu.au/~rotor/software/
Best,
Matthew
dbedit tarball: dbedit.tar.gz
...to the top
OK, this mail isn't from John either, and it doesn't even reference
John's code, but it's useful info that I've needed on more than
one occation.
The difficulty is that the fMRI interface doesn't querry about
masking; this email spells out how to overcome this.
Subject: Re: explicit masking
From: Stefan Kiebel <skiebel@fil.ion.ucl.ac.uk>
Date: Tue, 27 Jun 2000 11:43:52 +0100
To: "Kevin J. Black" <kevin@npg.wustl.edu>, SPM <spm@mailbase.ac.uk>
Dear Kevin,
> Is it possible to instruct spm99 to search all voxels within a given
> mask image rather than all above a fixed or a %mean threshold?
Yes, with SPM99 it's possible to use several masking options.
To recap, there are 3 sorts of masks used in SPM99:
1. an analysis threshold
2. implicit masking
3. explicit masking
1: One can set this threshold for each image to Inf to switch off this
threshold.
2: If the image allows this, NaN at a voxel position masks this voxel
from the statistics, otherwise the mask value is zero (and the user
can choose, whether implicit masking should be used at all).
3: Use mask image file(s), where NaN (when image format allows this) or
a nonpositive value masks a voxel.
On top of this, SPM automatically removes any voxels with constant
values over time.
So what you want is an analysis, where one only applies an explicit
mask.
In SPM99 for PET, you can do this by going for the Full Monty and
choosing Inf for the implicit mask and no 0thresholding. Specify one
or more mask images. (You could also define a new model structure,
controlling the way SPM for PET asks questions).
With fMRI data/models, SPM99 is fully capable of doing explicit masking,
but the user interface for fMRI doesn't ask for it. One way to do this
type of masking anyway is to specify your model, choose 'estimate later'
and modify (in matlab) the resulting SPMcfg.mat file. (see spm_spm.m
lines 27  39 and 688  713).
1. Load the SPMcfg.mat file, set the xM.TH values all to Inf,
set xM.I to 0 (in case that you have an image format not
allowing NaN).
2. Set xM.VM to a vector of structures, where each structure
element is the output of spm_vol. For instance:
xM.VM = spm_vol('Maskimage');
3. Finally, save by
save SPMcfg xM append
> If so, does the program define a voxel to be used as one which has
> nonzero value in the named mask image?
Not nonzero, but any positive value and unequal NaN. Note that you can
specify more than one mask image, where the resulting mask is then the
intersection of all mask images.
Stefan
...to the top
Subject: Re: colour bar
From: John Ashburner <john@FIL.ION.UCL.AC.UK>
Date: Thu, 28 Mar 2002 11:23:16 +0000 (06:23 EST)
To: SPM@JISCMAIL.AC.UK
> I would like to use the same colour bar for two different VBM experiments..
> Please, does anyone know how can I do this?
If this is for the display of orthogonal views, then you can tweek the
values that the blobs are scaled to by using a little bit of extra
Matlab code. First of all, display an image, with superimposed blobs.
Then in Matlab, type:
global st
st.vols{1}.blobs{1}.mx
This will give the maximum intensity of the set of blobs. Then do the
same with the other set of results. Find the largest of both results
(suppose it is 5.6) and scale the blobs so they are displayed with
this maximum by:
global st
st.vols{1}.blobs{1}.mx = 5.6;
Best regards,
John
...to the top
Subject: Re: resample to lower resolution
From: John Ashburner <john@FIL.ION.UCL.AC.UK>
Date: Wed, 3 Jul 2002 12:35:22 +0100 (07:35 EDT)
To: SPM@JISCMAIL.AC.UK
> Is there a way to resample images of 512x512 spatial resolution down to
> 256x256 resolution?
If you copy and paste the following into Matlab, then it should do the job
for you. Note that I haven't fully tested the code, but it worked on the
one image I tried it on. Note that it only reduces the data using Fourier
transforms in two directions. Combining slices in the 3rd direction is
just by averaging.
Best regards,
John
%* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
% Select image to reduce
V = spm_vol(spm_get(1,'*.img','Select image'));
% Create new image header information
VO = V;
VO.fname = 'reduced.img';
VO.dim(1:3) = floor(VO.dim(1:3)/2);
VO.mat = V.mat*[2 0 0 0.5 ; 0 2 0 0.5 ; 0 0 2 0.5 ; 0 0 0 1];
% Write the header
VO = spm_create_image(VO);
% Work out which bits of the Fourier transform to retain
d1 = VO.dim(1);
d2 = VO.dim(2);
if rem(d1,2), r1a = 1:(d1+1)/2; r1b = [];
r1c = []; r1d = (d1*2(d13)/2):d1*2;
else, r1a = 1:d1/2; r1b = d1/2+1;
r1c = d1*2d1/2+1; r1d = (d1*2d1/2+2):d1*2;
end;
if rem(d2,2), r2a = 1:(d2+1)/2; r2b = [];
r2c = []; r2d = (d2*2(d23)/2):d2*2;
else, r2a = 1:d2/2; r2b = d2/2+1;
r2c = d2*2d2/2+1; r2d = (d2*2d2/2+2):d2*2;
end;
for i=1:VO.dim(3),
% Fourier transform of one slice
f = fft2(spm_slice_vol(V,spm_matrix([0 0 (i*21)]),VO.dim(1:2)*2,0));
% Throw away the unwanted region
f1 = [f(r1a,r2a) (f(r1a,r2b)+f(r1a,r2c))/2 f(r1a,r2d)
([f(r1b,r2a) (f(r1b,r2b)+f(r1b,r2c))/2 f(r1b,r2d)]+...
[f(r1c,r2a) (f(r1c,r2b)+f(r1c,r2c))/2 f(r1c,r2d)])/2
f(r1d,r2a) (f(r1d,r2b)+f(r1d,r2c))/2 f(r1d,r2d)]/4;
% Fourier transform of second slice
f = fft2(spm_slice_vol(V,spm_matrix([0 0 (i*2 )]),VO.dim(1:2)*2,0));
% Throw away the unwanted region
f2 = [f(r1a,r2a) (f(r1a,r2b)+f(r1a,r2c))/2 f(r1a,r2d)
([f(r1b,r2a) (f(r1b,r2b)+f(r1b,r2c))/2 f(r1b,r2d)]+...
[f(r1c,r2a) (f(r1c,r2b)+f(r1c,r2c))/2 f(r1c,r2d)])/2
f(r1d,r2a) (f(r1d,r2b)+f(r1d,r2c))/2 f(r1d,r2d)]/4;
% Create a simple average of two FTs and do an inverse FT
f = real(ifft2((f1+f2)))/2;
% Write the plane to disk
VO = spm_write_plane(VO,f,i);
end;
...to the top
Subject: Re: smoothed modulated image
From: John Ashburner <john@FIL.ION.UCL.AC.UK>
Date: Fri, 12 Apr 2002 13:45:01 +0000
To: SPM@JISCMAIL.AC.UK
> considering a smoothed modulated image, which is the right
> interpretation of the matrix: "each value of the matrix denotes the
> volume, measured in mm3, of gray matter within each voxel" or "each
> value of the matrix is proportional to the volume, measured in mm3,
> of gray matter within each voxel" or something else?
The contents of a modulated image are a voxel compression map
multiplied by tissue belonging probabilities (which range between zero
and one).
The units in the images are a bit tricky to explain easily (so I would
suggest you say that intensities are proportional). To find the
volume of tissue in a structure in one of the modulated images, you
sum the voxels for that structure and multiply by the product of the
voxel sizes of the modulated image.
The total volume of grey matter in the original image can be
determined by summing the voxels in the modulated, spatially
normalised image and multiplying by the voxel volume (product of voxel
size).
For example, try the following code for an original image and the same
image after spatial normalisation and modulation. Providing the
bounding box of the normalised image is big enough, then both should
give approximately the same answer.
V = spm_vol(spm_get(1,'*.img'))
tot = 0;
for i=1:V(1).dim(3),
img = spm_slice_vol(V(1),spm_matrix([0 0 i]),V(1).dim(1:2),0);
tot = tot + sum(img(:));
end;
voxvol = det(V(1).mat)/100^3; % volume of a voxel, in litres
tot = tot; % integral of voxel intensities
tot*voxvol
I hope the above makes sense.
All the best,
John
...to the top
OK, this isn't a John email, but rather a tip of my own that uses one
of John's functions. When
preparing a manuscript you often want to display a "blobs on brain"
image, where a reference image underlies a colored significance
image. You can do this within the SPM Results facility, but since you
never get a figure right the first time, I prefer to do it on the
command line.
The code snippet blow scriptizes the blobsonbrain figure. You'll
get a large orthgonal viewer in the graphics window, so it's then easy
to print (or grab a screen snapshot) to then create your figure.
% Make sure to first clear the graphics window
% Select images
Pbck = spm_get(1,'*.img','Select background image')
Psta = spm_get(1,'*.img','Select statistic image')
% Set the threshold value
Th = 4;
% Create a new image where all voxels below Th have value NaN
PstaTh = [spm_str_manip(Psta,'s') 'Th'];
spm_imcalc_ui(Psta,PstaTh,'i1+(0./(i1>=Th))',{[],[],spm_type('float')},Th);
% Display!
spm_orthviews('image',Pbck,[0.05 0.05 0.9 0.9]);
spm_orthviews('addimage',1,PstaTh)
% Possibly, set the crosshairs to your favorite location
spm_orthviews('reposition',[0 10 10])
This assumes that you just want to threshold your image based on a
single intensity threshold. To make it totally scripted, replace the
spm_get calls with hard assignments.
...to the top
A source of confusion is where the origin (the [0,0,0] location of an
image) is stored. When there is no associated .mat file, the origin
is read from the Analyze originator field. If this is zero it is
assumed to match the center of the image field of view. If there is a .mat file, then the origin is the first three values of
M\[0 0 0 1]'
where M is the transformation matrix in the .mat file.
One limitation is that the origin stored in the Analyze header is a
(short) integer, and so cannot represent an origin with fractional
values. To set the origin to specific, fractional value, use this
code snippet:
Orig = [ x y z ]; % Desired origin in units of voxels
P = spm_get(Inf,'*.img'); % matrix of file names
for i=1:size(P,1)
M = spm_get_space(deblank(P(i,:)));
R = M(1:3,1:3);
% Set origin
M(1:3,4) = R*Orig(:);
spm_get_space(deblank(P(i,:)),M);
end
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Pvalue images are difficult to visualize since "important" values are
small and clumped near zero. A log10 transformation makes for much
better visualization while still having interpretability (e.g. a value of 3
cooresponds to P=0.001).
This function, T2nltP, will create log10 Pvalue image based
on either a contrast number (which must be a T contrast) or a T
statistic image and the degrees of freedom.
(See also the equivalent SPM2 function.)
T2nltP.m
function T2nltP(a1,a2)
% Write image of log10 Pvalues for a T image
%
% FORMAT T2nltP(c)
% c Contrast number of a T constrast (assumes cwd is a SPM results dir)
%
% FORMAT T2nltP(Timg,df)
% Timg Filename of T image
% df Degrees of freedom
%
%
% As per SPM convention, T images are zero masked, and so zeros will have
% Pvalue NaN.
%
% @(#)T2nltP.m 1.2 T. Nichols 03/07/15
if nargin==1
c = a1;
load xCon
load SPM xX
if xCon(c).STAT ~= 'T', error('Not a T contrast'); end
Tnm = sprintf('spmT_%04d',c);
df = xX.erdf;
else
Tnm = a1;
df = a2;
end
Tvol = spm_vol(Tnm);
Pvol = Tvol;
Pvol.dim(4) = spm_type('float');
Pvol.fname = strrep(Tvol.fname,'spmT','spm_nltP');
if strcmp(Pvol.fname,Tvol.fname)
Pvol.fname = fullfile(spm_str_manip(Tvol.fname,'H'), ...
['nltP' spm_str_manip(Tvol.fname,'t')]);
end
Pvol = spm_create_image(Pvol);
for i=1:Pvol.dim(3),
img = spm_slice_vol(Tvol,spm_matrix([0 0 i]),Tvol.dim(1:2),0);
img(img==0) = NaN;
tmp = find(isfinite(img));
if ~isempty(tmp)
img(tmp) = log10(max(eps,1spm_Tcdf(img(tmp),df)));
end
Pvol = spm_write_plane(Pvol,img,i);
end;
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This is the famed script to modulate spatially normalized probability
images. For gray matter probability images, modulated images have
units of gray matter volume per voxel, instead of gray matter
concentration adjusted for differences in local brain size.
In John's own words, here's a better explaination.
Note that script below is SPM99 specific. In SPM2, it is done via spm_write_sn(V,prm,'modulate') (See spm_write_sn help fore more.)
Date: Thu, 27 Jul 2000 14:28:39 +0100 (BST)
Subject: Re: matlab question & VBM
From: John Ashburner <john@fil.ion.ucl.ac.uk>
To: spm@mailbase.ac.uk, x.chitnis@iop.kcl.ac.uk
 Following on from John Ashburner's recent reply, is there a matlab function
 that enables you to adjust spatially normalised images in order to preserve
 original tissue volume for VBM?
The function attached to this email will do this. Type the following bit of
code into Matlab to run it:
Mats = spm_get(Inf,'*_sn3d.mat','Select sn3d.mat files');
Images = spm_get(size(Mats,1),'*.img','Select images to modulate');
for i=1:size(Mats,1),
spm_preserve_quantity(deblank(Mats(i,:)),deblank(Images(i,:)));
end;
[...]
Best regards,
John
The attached script is here spm_preserve_quantity.m
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From: John Ashburner <john@FIL.ION.UCL.AC.UK>
Subject: Re: Help for constructing Template images
Date: Wed, 18 Dec 2002 16:22:59 +0000
To: SPM@JISCMAIL.AC.UK
> What are the advantages of customized template images
> in VBM analysis?
Customised templates are useful when:
1) The contrast in your MR images is not the same as the
contrast used to generate the existing templates. If
the contrast is different, then the mean squared cost
function is not optimal. However, for "optimised" VBM
this only really applies to the initial affine
registration that is incorporated into the initial
segmentation. Contrast differences are likely to have
a relatively small effect on the final results.
2) The demographics of your subject population differ
from those used to generate the existing templates and
prior probability images. For example, serious problems
can occur if your subjects have very large ventricles.
In these data, there would be CSF in regions where the
existing priors say CSF should not exist. This would
force some of the CSF to be classified as white matter,
seriously affecting the intensity distribution that
is used to model white matter. This then has negative
consequences for the whole of the segmentation.
> Can any one please explain the detailed steps to
> construct a customized template image (gray and white
> matter images) for VBM analysis?
The following script is one possible way of generating your
own template image. Note that it takes a while to run, and
does not save any intermediate images that could be useful
for quality control. Also, if it crashes at any point then
it is difficult to recover the work it has done so far.
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
make_template.m
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
You may also wish to do some manual editing of the images
afterwards  especially to remove extraskull CSF. When
everything has finished, simply smooth the images by 8mm
and call them templates and prior probability images.
You can modify the default priors for the segmentation step
in order that the customised ones are used. This can be done
either by changing spm_defaults.m, or by typing the following
in Matlab:
spm_defaults
global defaults
defaults.segment.estimate.priors = ...
spm_get(3,'*.IMAGE','Select GM,WM & CSF priors');
Note that this will be cleared if you reload the defaults. This
could be done when you start spm, reset the defaults or if the
optimised VBM script is run, as it calls spm_defaults.m.
Alternatively the optimised VBM script could be modified to
include the above.
Note that I have only tried the script with three images, so
I don't have a good feel for how robust it is likely to be.
>
> Please let me know the number of subjects required to
> construct one?
Its hard to say, but more is best. The 8mm smoothing means
that you can get away with slightly fewer than otherwise.
Best regards,
John
Note: The make_template is an updated version of what was originally
posted. It is current as of Sep 9, 2003.
...to the top
As posted, this snippet is for SPM2; I've edited it to work with SPM99.
Subject: Re: a script to use ImaCal
From: John Ashburner <john@FIL.ION.UCL.AC.UK>
Date: Thu, 16 Oct 2003 11:24:17 +0000 (07:24 EDT)
To: SPM@JISCMAIL.AC.UK
> Brain image for each subject to mask out CSF signal was generated by
> using MPR_seg1.img (i1) and MPR_seg2.img (i2) with (i1+i2)>0.5 in
> ImaCal.(called brainmpr.img for each subject)
>
> Then I have more than twohundred maps, which need to mask out
> CSF. I think I can use ImaCal again with selecting brainmpr.img (i1)
> and FAmap.img (i1), and then calculating (i1.*i2) to generate a new
> image named as bFAmap.img. Unfortunately, if I use the ImaCal, it
> take so long time to finish all subjects. Could anyone have a script
> to generate a multiplication imaging with choosing a brain
> image(brainmpr.img, i1) and a map image (FAmap.img, i2) and writing
> an output image (bFAmap.img) from i1.*i2?
You can do this with a script in Matlab. Something along the lines of the
following should do it:
P1=spm_get(Inf,'*.img','Select i1');
P2=spm_get(size(P1,1),'*.img','Select i2');
for i=1:size(P1,1),
P = strvcat(P1(i,:),P2(i,:)));
Q = ['brainmpr_' num2str(i) '.img'];
f = '(i1+i2)>0.5';
flags = {[],[],[],[]};
Q = spm_imcalc_ui(P,Q,f,flags);
end;
Note that I have not tested the above script. I'm sure you can fix it if
it doesn't work.
Best regards,
John
...to the top
IMHO, after spatial normalization, John's key contribution to SPM is
spm_orthviews, the function behind the 'Check Reg' button which let's
you view many volumes simeltaneously. Some of the Gems use
spm_orthviews (e.g. Gems 1 and 16) but listed here are some generally useful tricks
for spm_orthviews.
These tricks are useful anytime a threeview orthogonal slice view is
shown, whether from useing 'Check Reg', 'Display' or when overlaying
blobs from the 'Results' window.
 Turn off/on crosshairs
 spm_orthviews('Xhairs','off')
spm_orthviews('Xhairs','on').
 Return current x,y,z world space, mm location
 spm_orthviews('pos')'
(Note that I transpose the returned value into a row
vector, for easier copying and pasting.)
 Move to x,y,z world space, mm location
 spm_orthviews('reposition',[x y z])
...to the top
Subject: Fwd: Re: tabulating all statistics
From: John Ashburner <john@FIL.ION.UCL.AC.UK>
Date: Tue, 1 Jul 2003 11:47:07 +0000 (07:47 EDT)
To: SPM@JISCMAIL.AC.UK
> I was wondering if it would be possible to write t values for an
> entire volume into a file?
Try this:
fid=fopen('tvalues.txt','w');
P=spm_get(1,'*.img','Select statistic image');
V=spm_vol(P);
[x,y,z] = ndgrid(1:V.dim(1),1:V.dim(2),0);
for i=1:V.dim(3),
z = z + 1;
tmp = spm_sample_vol(V,x,y,z,0);
msk = find(tmp~=0 & finite(tmp));
if ~isempty(msk),
tmp = tmp(msk);
xyz1=[x(msk)'; y(msk)'; z(msk)'; ones(1,length(msk))];
xyzt=V.mat(1:3,:)*xyz1;
for j=1:length(tmp),
fprintf(fid,'%.4g %.4g %.4g\t%g\n',...
xyzt(1,j),xyzt(2,j),xyzt(3,j),tmp(j));
end;
end;
end;
fclose(fid);
best regards,
John
As noted in a 2 Feb 2004 email, to eliminate all voxels below a certain
threshold, change
msk = find(tmp~=0 & finite(tmp));
to
msk = find(tmp>0 & finite(tmp));
...to the top
This script of John's will find the corresponding coordinate in
unnormalised image: get_orig_coord.m
...to the top 