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

[Matthew Brett]

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