[Neuroimaging] affine issue (?)

[Christophe Pallier]

Thanks JB & Matthew !!! I should have thought about it...

Chris

--
Christophe Pallier

Personal web site: http://www.pallier.org
Lab web site: http://www.unicog.org
Email: christophe at pallier.org
Tel: +33 (0) 1 69 08 79 34

Address:
INSERM-CEA Cognitive Neuroimaging Lab, Neurospin, bat 145,
91191 Gif-sur-Yvette Cedex, FRANCE


On Thu, Jul 13, 2017 at 3:59 PM, Matthew Brett <matthew.brett at gmail.com> wrote:
> Hi,
>
> On Thu, Jul 13, 2017 at 2:40 PM, Christophe Pallier
> <christophe at pallier.org> wrote:
>> Hello,
>>
>> I have an nii image (a contrast computed by SPM, in the MNI space),
>> which, according to SPM, has the following affine:
>>
>>
>>>> V.mat
>> 2   0   0    -74
>> 0   2   0   -108
>> 0   0   2    -66
>> 0   0   0    1
>>
>>
>> However, the same image loaded with nibabel yields a different affine:
>> [[   2.            0.            0.          -72.00000763]
>>  [   0.            2.            0.         -106.        ]
>>  [   0.            0.            2.          -64.        ]
>>  [   0.            0.            0.            1.        ]]
>
> Right - what JB said - the affine is the mapping from voxel coordinate
> to mm coordinate.   For Matlab, the voxel coordinates are 1-based, so
> the first voxel is coordinate (1, 1, 1) whereas for Python (or C) the
> first coordinate is (0, 0, 0).
>
> For example, you can think of the Matlab affine as the composition of
> the translation taking the first coordinate to 0, 0, 0, followed by
> the Python affine:
>
> import numpy as np
> py_affine = np.array([[ 2.,  0., 0.,  -72.00000763],
>                       [ 0.,  2., 0., -106.        ],
>                       [ 0.,  0., 2.,  -64.        ],
>                       [ 0.,  0., 0.,    1.        ]])
>
> from_111 = np.array([[1, 0, 0, -1],
>                      [0, 1, 0, -1],
>                      [0, 0, 1, -1],
>                      [0, 0, 0, 1]])
>
> print(py_affine.dot(from_111))
>
> This gives:
>
> [[   2.            0.            0.          -74.00000763]
>  [   0.            2.            0.         -108.        ]
>  [   0.            0.            2.          -66.        ]
>  [   0.            0.            0.            1.        ]]
>
> Cheers,
>
> Matthew
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