Hi John, Daniel, et al.
Just my 2p.
What if you *normalised* the low quality T1 onto the (smoothed) high
quality T1 ? This would include a non-linear warping that could (possibly)
correct the distortions.
Then normalise the high Q T1 as usual, combine this transformation with the
previously estimated and apply the combined warping onto the EPIs...
But what about Jesper/Chloe's unwarping tools for fMRI then ?
HTH,
Chris
At 11:25 21/09/06 +0200, you wrote:
>Steve, if you're reading this could you comment? Feel free to do some
>FSL sales pitch.
>
>I'm guessing that the low quality T1 has the same (phase encode
>direction) distortions as the EPI data, and that the additional step is
>in order to correct some of these distortions. It is easier to obtain a
>more accurate registration if the relationship between the intensities
>in one image are simply related to the intensities of the other - so
>nonlinear matching a T1 weighted to another T1 weighted image is easier
>than matching a T2* weighted to a T1.
>
>If the transformation is indeed a nonlinear one, then there is no easy
>way of doing this within SPM. If it is a rigid-body, then the
>Coregister button should do a reasonable job. Coregistration of the low
>quality T1 and the EPI may not be necessary if the images were acquired
>in the same session and there was no subject motion.
>
>Best regards,
>-John
>
>
> I have a question. I have seen papers and spoken with people using
>FSL,
>who do the normalization process in several steps. First, they
>coregister
>the low-quality T1 image (that is taken at the same time and in the same
>space as the functional data, and thus will coregister much better) to
>the
>functional image. Next, they determine the paramaters of the non-linear
>transformation from the low-quality T1 to the high-quality T1 (eg
>MPRAGE)
>and from the MPRAGE to the standard-space template. They then multiply
>those two transformation matrices (low quality T1 -> MPRAGE -> template)
>and
>apply the parameters to the functional images in order to normalize them
>to
>standard space. Hence, my question is: how would I do this in SPM? I
>can
>do each transformation and apply the parameters separately, but warping
>the
>functional data twice will create more opportunities for the data to be
>mishandled; I would prefer a single warp with the combined parameters.
>Thanks for the help!
>
>Daniel Simmonds
>Developmental Cognitive Neurology
>Kennedy Krieger Institute
>[log in to unmask]
|