Dear FSL Community,
I am trying to register Rhesus monkey brains from one individual to another. I've achieved some success with the following configuration file (below). However, I find that the majority of the work gets done by the linear affine transformation. The non-linear part improves the fit, but marginally.
Can anyone recommend sensible tweaks to this config file? Remember, these brains are only about 70mm long. Furthermore, I use ex-vivo scans, so there is no surrounding tissue to compare (hence why the brain mask switches are always on). Missing information includes the input, reference and mask images, and the affine transform ; these I enter as parameters when calling fnirt from the command line.
Kind regards,
Jackson
Configuration file:
# If =1, use implicit masking based on value in --ref image. Default =1
--imprefm=1
# If =1, use implicit masking based on value in --in image, Default =1
--impinm=1
# Value to mask out in --ref image. Default =0.0
--imprefval=0
# Value to mask out in --in image. Default =0.0
--impinval=0
# sub-sampling scheme, default 4,2,1,1
--subsamp=8,8,4,4,2,2
# Max # of non-linear iterations, default 5,5,5,5
--miter=5,5,5,5,5,10
# FWHM (in mm) of gaussian smoothing kernel for input volume, default 6,4,2,2
--infwhm=4,3,2,1.5,1,0.5
# FWHM (in mm) of gaussian smoothing kernel for ref volume, default 4,2,0,0
--reffwhm=4,3,2,1.5,1,0.5
# Weigth of membrane energy regularisation, default depending on --ssqlambda and --regmod switches. See user documetation.
--lambda=300,150,100,50,40,30
# Estimate intensity-mapping if set, deafult 1 (true)
--estint=1,1,1,1,1,0
# Apply the mask if set, default 1 (true)
#--applyrefmask=0,1,1,1,1,1
--applyrefmask=1
# Apply the mask if set, default 1 (true)
--applyinmask=1
# (approximate) resolution (in mm) of warp basis in x-, y- and z-direction, default 10,10,10
--warpres=5,5,5
# If set (=1), lambda is weighted by current ssq, default 1
--ssqlambda=1
# Model for regularisation of warp-field [membrane_energy bending_energy], default bending_energy
--regmod=bending_energy
# Model for intensity-mapping [none global_linear global_non_linear local_linear global_non_linear_with_bias local_non_linear]
--intmod=global_non_linear_with_bias
# Order of poynomial for mapping intensities, default 5
--intorder=5
# Resolution (in mm) of bias-field modelling local intensities, default 50,50,50
--biasres=10,10,10
# Weight of regularisation for bias-field, default 10000
--biaslambda=10000
# If =1, ref image is used to calculate derivatives. Default =0
--refderiv=0
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