Jeremy
You might want to check out this paper.
http://www.ncbi.nlm.nih.gov/pubmed/16628607?ordinalpos=10&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum
We routinly average up 7-8 MPRAGE studies in order to reduce motion in the
high-res anatomical images.
You can judge the quality of the individual segments by running a histogram
analysis on them
pk
----- Original Message -----
From: "Jeremy R. Gray" <[log in to unmask]>
To: <[log in to unmask]>
Sent: Friday, March 07, 2008 11:13 PM
Subject: [FSL] combine two MPRAGEs?
Hi FSL'ers,
I'm new to FSL (really liking it so far). I'm hoping for advice on the best
approach to combining two MPRAGE images from the same subject. This is for
100+ subjects, and so I will script it. the idea is to end up with one
higher-quality image to use in structural analyses. one image is from the
start of the scan session, and the other from the end (~1.25 hours later).
flirt seems like the way to go, so I searched the archives and the flirt
lecture notes from the web (pdf), but did not see something on combining
MPRAGES. my apologies if I missed it.
one question is:
- is it always better to combine two images? presumably there could be
pathological cases (e.g., lots of movement resulting in a blurry image)
where a single good MPRAGE is better than combining one good and one bad --
so is there a way to tell that you are in such a situation (especially for a
script to tell this)? just inspect afterwards?
using flirt seems straightforward:
- prior to flirt, run bet -B on each image (= my interpretation of flirt
lecture slide #45). but maybe for having the same sequence, the non-brain
stuff will actually help the alignment? and maybe doing bet on the combined
image will give a better extraction (for having a higher-quality input)?
- just pick one image to use as the reference, "better quality" should be
moot with 2 MPRAGEs (except in pathological cases)
- a rigid body 6-parameter model seems fine because the images are from the
same subject, same scanner, same day. is there any possible advantage to
more df for my situation?
- search option = "already virtually aligned" is probably fine
- cost function: correlation ratio is the default in the GUI. however, I've
heard that normalized mutual information is very good, in particular is
robust to small non-brain bits left over from brain extraction. any reason
not to use NMI (especially if I do bet prior to flirt)?
- trilinear interpolation (= default) -- any advantage to sinc?
thanks much,
--Jeremy
/*-------------------------------------------------------------
Jeremy R. Gray, PhD
Assistant Professor, Yale University
Dept. of Psychology & Interdepartmental Neuroscience Program
mail Box 208205, New Haven, CT 06520-8205 USA
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