[Neuroimaging] Using nibabel to merge 3D image pairs into single-file 4D Nifti1

[Dav Clark]

That works - FSLview shows up sensibly scaled and a subtraction of the FSL
data array from the f4 nibabel version yeilds all 0.0.

Thanks Matthew,
D

On Tue, Mar 28, 2017 at 5:27 PM, Matthew Brett <matthew.brett at gmail.com>
wrote:

> Yo,
>
> On Tue, Mar 28, 2017 at 10:22 PM, Dav Clark <davclark at gmail.com> wrote:
> > Yo!
> >
> > On Tue, Mar 28, 2017 at 4:47 PM, Matthew Brett <matthew.brett at gmail.com>
> > wrote:
> >>
> >> On Tue, Mar 28, 2017 at 7:45 PM, Dav Clark <davclark at gmail.com> wrote:
> >
> >
> >>
> >> I'm sure you know, but you can always save directly to whatever format
> >> the extension implies, as in:
> >>
> >> converted = nib.concat_images(fnames)
> >> nib.save(converted, 'my_filename.nii')
> >
> >
> > I'm sure I could've figured that out. I appreciate your efforts to save
> me
> > typing.
> >
> >>
> >> > This version, upon saving with .to_filename() comes up in fslview as
> >> > totally
> >> > black (the range is not calibrated for some reason - maybe there's an
> >> > errant
> >> > bright voxel somewhere?). Compared to the results from `fslmerge -t
> >> > fnames`,
> >> > setting the range the same for voxel intensities, I get the same
> images,
> >> > and
> >> > the same time-series shape. However, intensities are slightly off
> (even
> >> > in
> >> > the same TR) by values ranging from hundreds to thousands (the raw
> >> > intensities are in 100,000's). So, usually < 1% I suspect.
> >> >
> >> > Is this worth getting to the bottom of? Happy to share some EPIs with
> >> > you
> >> > somewhere... by default I would use JHU Box.
> >>
> >> Just to clarify - the result of `fslmerge` does not look totally black
> >> in fslview?
> >
> >
> > That's correct. Looks real pretty-like, from the deepest black to the
> > whitest white my monitor can display.
> >
> >>
> >> Do the images from fslmerge and `nib.concat_images` have the same data
> >> range?  What range do they have, when you load them into nibabel and
> >> do:
> >>
> >> data = img.get_data()
> >> print(data.min(), data.max())
> >
> >
> > This was the ticket, I think. Min values are both 0. But max are
> > 1253294.0009593964 for nib merged version, and 1253294.0 for the
> FSL-merged
> > version. Turns out the dtypes are '<i2' and '<f4' respectively. dtype on
> > initial images also '<i2'. So, FSL is type-converting to float, which
> would
> > explain why the nib file is smaller (75MB vs 90MB). Neither of the
> > concatenated images has the exact same max in the first slice as the
> first
> > original image either.
> >
> > Anyway, I'm happy to leave it at that - I think I prefer the floating
> point
> > option, as it makes the image simpler to work with and compression makes
> the
> > size difference not so huge. My guess is that keeping the image in
> integer
> > format also limits accuracy, as I'm pretty sure Nifti1 implements a
> single
> > scaling constant for the whole image - 3D or 4D, and I guess we're
> > round-tripping through float64 already.
>
> You can always:
>
> converted.set_data_dtype('f4')
>
> before saving, if you want the floats in the nibabel output.
>
> Actually, if you do that, do you still see the all-black image in fslview?
>
> Cheers,
>
> Matthew
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