Keeping track of whether images have been modified since load


This is a discussion of a missing feature in nibabel: the ability to keep track of whether an image object in memory still corresponds to an image file (or files) on disk.


We may need to know whether the image in memory corresponds to the image file on disk.

For example, we often need to get filenames for images when passing images to external programs. Imagine a realignment, in this case, in nipy (the package):

import nipy
img1 = nibabel.load('meanfunctional.nii')
img2 = nibabel.load('anatomical.nii')
realigner = nipy.interfaces.fsl.flirt()
params =, target=img2)

In there may at some point be calls like:

source_filename = nipy.as_filename(source_img)
target_filename = nipy.as_filename(target_img)

As the authors of the method, we need to make sure that the source_filename corresponds to the source_img.

Of course, in the general case, if source_img has no corresponding filename (from source_img.get_filename(), then we will have to save a copy to disk, maybe with a temporary filename, and return that temporary name as source_filename.

In our particular case, source_img does have a filename (meanfunctional.nii). We would like to return that as source_filename. The question is, how can we be sure that the user has done nothing to source_img to make it diverge from its original state? Could source_img have diverged, in memory, from the state recorded in meantunctional.nii?

If the image and file have not diverged, we return meanfunctional.nii as the source_filename, otherwise we will have to do something like:

import tempfile
fname = tempfile.mkstemp('.nii')
img = source_img.to_filename(fname)

and return fname as source_filename.

Another situation where we might like to pass around image objects that are known to correspond to images on disk is when working in parallel. A set of nodes may have fast common access to a filesystem on which the images are stored. If a master is farming out images to nodes, a master node distribution jobs to workers might want to check if the image was identical to something on file and pass around a lightweight (proxied) image (with the data not loaded into memory), relying on the node pulling the image from disk when it uses it.

Possible implementation

One implementation is to have dirty flag, which, if set, would tell you that the image might not correspond to the disk file. We set this flag when anyone asks for the data, on the basis that the user may then do something to the data and you can’t know if they have:

img = nibabel.load('some_image.nii')
data = img.get_fdata()
data[:] = 0
img2 = nibabel.load('some_image.nii')
assert not np.all(img2.get_fdata() == img.get_fdata())

The image consists of the data, the affine and a header. In order to keep track of the header and affine, we could cache them when loading the image:

img = nibabel.load('some_image.nii')
hdr = img.header
assert img._cache['header'] == img.header
assert img._cache['header'] != img.header

When we need to know whether the image object and image file correspond, we could check the current header and current affine (the header may be separate from the affine for an SPM Analyze image) against their cached copies, if they are the same and the ‘dirty’ flag has not been set by a previous call to get_fdata(), we know that the image file does correspond to the image object.

This may be OK for small bits of memory like the affine and the header, but would quickly become prohibitive for larger image metadata such as large nifti header extensions. We could just always assume that images with large header extensions are not the same as for on disk.

The user might be able to override the result of these checks directly:

img = nibabel.load('some_image.nii')
assert img.is_dirty == False
hdr = img.header
assert img.is_dirty == True
img.is_dirty == False

The checks are magic behind the scenes stuff that do some safe optimization (in the sense that we are not re-saving the data if that is not necessary), but drops back to the default (re-saving the data) if there is any uncertainty, or the cost is too high to be able to check.