Hi Thilo, all,
I do not know of a particular paper investigating this, but can only report from my experience. Furthermore, I do not think it makes a lot of sense to study the differences between both strategies you mention, as it will all boil down to your specific EPI acquisition technique, and those are very different from site to site and study to study. So a general claim which strategy is superior isnt very generalizable. In other words, the MR physicists are right (aren't they always ;-) ).
And the best answer about what to do, is, as always: it depends.
Indeed, EPI deformations can be so substantial in the entire image and especially in certain areas (temporal, orbitofrontal) that the coregistration with T1 will be poor, and hence normalization of your EPIs will be way off when using T1 unified segementation parameters. When you have sufficient anatomical contrast in your mean EPI, for example, you would then be better of using unified segmentation on your mean EPI directly (apply norm.parameters to all EPI dynamics). When doing this it is important to check unified segmentation results for every subject though, you need high resolution EPIs for this to separate GM and WM. Unified segmentation, in my experience, often wont work for low resolution EPI (say anything at or above 3x3x3 mm). I recently used this strategy of direct EPI segmentation normalization in a 3T 2x2x2 mm^3 2DEPI study, see:
http://www.ncbi.nlm.nih.gov/pubmed/22235303
When accelerating your EPI readout (for example by using parallel imaging schemes such as SENSE, and/or by using a high acquisition bandwith) you can substantially reduce spatial distortions, for low res fMRI they would be hardly noticable on standard 8 channel headcoils with acceleration factors of 2 to 3. In that case, the 1st strategy where EPI is normalized based on unified segmentation parameters from (coregistered) T1 would work best.
In short: check your spatial distortion in your EPIs with respect to your T1. When they are large, use EPI segmentation directly for normalization (provided you have good anatomical contrast in your EPIs), check segmentation results manually for every subject. When EPI spatial distrortion it is neglegible, use T1 unified segmentation parameters for your EPIs, be sure to check coreg between T1 and mean EPI thoroughly for every subject. When spatial distortion is high AND EPI anatomical contrast is poor, you are lost and you will have to design better EPI acquisition (or live with classical normalization based on course anatomical shape, use enormous amounts of smoothing).
As said, one EPI isnt the other...
Cheers,
Bas
--------------------------------------------------
Dr. S.F.W. Neggers
Division of Brain Research
Rudolf Magnus Institute for Neuroscience
Utrecht University Medical Center
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:
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--------------------------------------------------
________________________________________
From: SPM (Statistical Parametric Mapping) [
[log in to unmask]] on behalf of Thilo Kellermann [
[log in to unmask]]
Sent: Tuesday, March 13, 2012 11:59 AM
To:
[log in to unmask]
Subject: [SPM] Normalization of EPI using unified segmentation
Dear SPM users,
the seemingly simple question is if a normalization of functional images (EPI)
will be more accurate using the following steps:
1) the structural image (usually T1) is co-registered to the mean EPI
2) normalization parameters are determined for the T1 (via unified
segmentation) and
3) normalization parameters are applied to EPI time series
The alternative would be to apply the unified segmentation procedure directly
to the mean EPI which provides normalization parameters for the time series.
Some people (usually neuroscientists) argue that the first procedure is more
accurate since the T1 provides much more spatial details (good contrast
between GM, WM and CSF) which improve the normalization.
Other people (usually MR physicists) argue that the distortions of the two
modalities (T1 weighted images vs. T2* weighted images) are quite different.
Therefore, good normalization parameters for the structural image are not
necessarily helpful in order to transform EPI images into standard MNI space
in the best possible way.
Is this debate still a "matter of taste" or is there any study that already
addressed this issue (which I am not able to find...)? I am aware of the
study by Crinion and colleagues (Neuroimage 2007) which addressed a related
but still different question. Are there any estimates of displacement using
the procedures described above?
Thanks a lot for your comments,
Thilo
--
Thilo Kellermann
RWTH Aachen University
Department of Psychiatry, Psychotherapy and Psychosomatics
JARA Translational Brain Medicine
Pauwelsstr. 30
52074 Aachen
Germany
Tel.:
+49 (0)241 / 8089977
Fax.:
+49 (0)241 / 8082401
E-Mail:
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