Hi Barbara what exactly did you divide? If you look at the recon-all.log it will show the exact inputs and outputs of nu_correct.
cheers Bruce On Thu, 28 Aug 2014, Barbara Kreilkamp wrote: > Dear Christian, > > Thanks a bunch for this answer. I ran all the steps you mentioned > (except for the one where I simply do uncorrected/corrected, as these > images have different dimensions, it seems nu3 does more than just > normalization of intensities, but also image cropping). Do you know > anything about the image cropping? > > I ended up with the output nu1_mask and nu1_est and I think the last one > is the bias field, at least it looks very much like one :). Am I right? > > Thanks for your help, > Barbara > > > On 27/08/2014 22:53, Christian Thode Larsen wrote: >> Hi Barbara, >> >> I'm not aware of any way that you can do it directly by passing >> arguments to recon-all (some might correct me on that), but it is possible: >> >> 1) As N3 models the bias as a multiplicative effect uncorrected = >> corrected * bias, the simplest way is to divide each voxel of the volume >> before and after correction, in order to obtain a volume containing the >> bias. Note that N3 (by default) works within a mask where low-intensity >> voxels have been thresholded away. These voxels will contain garbage if >> you divide all voxels in the volume. >> >> 2) Somewhat more complicated: you can specify the -keeptmp flag combined >> with -tmp SOMEDIR/ (remember the trailing slash) to N3, in order to >> preserve its working files. This requires you to modify the N3 binary >> call in the mri_nu_correct.mni script. You also need to convert the mnc >> files from the tmp dir, so that you can work with the volumes. >> >> 3) if you do 2), you also get hold of the low-intensity voxel mask that >> N3 operates within. You can use this to constrain the division mentioned >> in 1). >> >> Best, >> Christian >> >> On 8/27/2014 11:18 PM, Barbara Kreilkamp wrote: >>> Dear all, >>> >>> Is there a way to output the N3's (non-parametric normalization step) >>> output? >>> I am interested in the bias field that was computed to correct the image >>> intensities. >>> >>> Thank you for your help, >>> Barbara >>> _______________________________________________ >>> Freesurfer mailing list >>> Freesurfer@nmr.mgh.harvard.edu >>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >>> >>> >>> The information in this e-mail is intended only for the person to whom it is >>> addressed. If you believe this e-mail was sent to you in error and the >>> e-mail >>> contains patient information, please contact the Partners Compliance >>> HelpLine at >>> http://www.partners.org/complianceline . If the e-mail was sent to you in >>> error >>> but does not contain patient information, please contact the sender and >>> properly >>> dispose of the e-mail. >>> >> _______________________________________________ >> Freesurfer mailing list >> Freesurfer@nmr.mgh.harvard.edu >> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
