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Image and Volume Registration with AFNI
¥Goal: bring
images collected with different methods and at different times into alignment
¥Facilitates
comparison of data on a voxel-by-voxel basis
²Functional
time series data will be less contaminated by artifacts due to subject
movement
²Can
compare results across scanning sessions once images are properly registered
¥Most (all?)
image registration methods now in use do pairwise aligment:
²Given
a base image J(x) and target image I(x), find a geometrical transformation T[x] so that I(T[x])ÅJ(x)
² T[x]
will depend on some parameters
åGoal is to find the parameters that make the transformed I a Ôbest fitÕ to J
²To
register an entire time series, each volume In(x)
is aligned to J(x) with its own transformation
Tn[x], for n=0, 1, É
åResult
is time series In(Tn[x])
for n=0, 1, É
åUser
must choose base image J(x)