Hi again, Bas,
We have been using SENSE in our acquisitions, and the images you saw in
my original post were all SENSE based images. Now, when it comes to
SENSE it seems noone except for our physicist has any clue as to what it
actually does, so we've pretty much been using the factory set SENSE
factor for any type of acquisition without knowing precisely what it
does.
Since you are using a Philips scanner as well, I wondered whether you
would be willing to share some of your findings with regards to what
SENSE factors one should use, their effects on suceptibility artifacts
etc. Do you perchance know of any resources that explains SENSE in a
sensible fashion (sorry for the pun)?
I'll be sure to read your article as well, it should probably answer
some of my manifold questions with regards to SENSE.
Cheers,
h.
> (hope you guys don't mind I send this off-list continuation of an
> on-list discussion back to the list, it might be interesting for others)
>
> Marko Wilke schreef:
>> Hi there,
>>
>>> Anytime.
>>
>> Yep, same for me. And while I am completely oblivious of the finer
>> points of pulse programming, I agree in so far as
>>
> No pulse programming required, at least on Philips scanners one can
> usually tweak all these paramaters on the console from existing
> sequences, when you know what you are doing that is. Wasn't all that
> hard...
>>> But when you, for whatever reason, do have to use substantially
>>> distorted EPIs and did not record phase maps my advice would be to
>>> not coregister them with T1 at all (it might make matters worse
>>> because they simply would not match well). Instead, use the mean EPI
>>> and normalize it to the EPI template directly, after which you can
>>> apply this transformation to your EPIs.
>>
>> ... I have also used that approach to normalize EPIs directly.
>> Incidentally, I also looked into how unified segmentation would work
>> for a mean EPI, which was ok, but not really great. An interesting
>> byproduct, though, is the bias correction that you get. I always
>> wanted to look at the effect of bias correcting EPIs, but never got
>> around to doing it in a systematic fashion. Any experience on your
>> sides?
> That's interesting, I am actually trying this with a new dataset right
> now. It is high resolution 3T EPI data (2x2x2), eg with considerable
> distortion, and besides our time series data concerning a smaller FOV we
> also acquired one identically angulated 2x2x2 whole brain EPI. The
> contrast in that image is pretty good, it approaches T1 contrast at
> 1.5T. So for 9 out of 10 subjects it can be used for unified
> segmentation normalization. And, importantly, it has the same spatial
> distortion as the time series data (because we took grate care the
> angulation was identical), greatly improving coregistration.
> I am not sure however how well segmetation works on more common spatial
> resolution EPI data (eg 4x4x4), I think it is more problematic there.
>>
>>> To check how much your EPIs are distorted, visually compare your EPI
>>> with your T1 (from the same subject) using the 'check reg' option in
>>> SPM.
>>
>> ... or do something like (i1 > thr1) - (i2 > thr2) and be scared by
>> the amount of tissue that remains :)
>>
>> Best,
>> Marko
>
>
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> Dr. S.F.W. Neggers
> Division of Brain Research
> Rudolf Magnus Institute for Neuroscience
> Utrecht University Medical Center
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