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Image use-cases in SPM

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Reggie -- the one

Loading and saving image files from / to disk

Need for lightweight version of image object

Images can be very large, and therefore take up a large amount of memory, or processor / system time when loading the image from disk.

We would like to be able to create and look at an image (and not the image data) without incurring the full cost of the load from disk.

So, the image object can be lightweight:

import nibabel
img = nibabel.load('some_image.nii')

In this case, the img object has not yet loaded the data from disk. We may use the get_data() method to get the data, or something fancy with the data attribute to delay loading until we access the data.

Keeping track of the image and the disk file

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:

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

In nipy.interfaces.fsl.flirt.run there will at some point be calls like:

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

We need to make sure that the source_filename corresponds to the source_img. When we pass the source image, this will be true:

source_img.get_filename() == 'meanfunctional.nii'

We need to know whether the source_img still corresponds exactly to meanfunctional.nii. If so, 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)

Another application for this scheme 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 might want to check if the image was identical to something on file and pass a lightweight shell round the image (with the data not loaded into memory), relying on the node pulling the image from disk when it uses it.

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

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.get_header()
assert img._cache['header'] == img.get_header()
hdr.set_data_dtype(np.complex64)
assert img._cache['header'] != img.get_header()

When asking to return a filename, or similar, check the current header and current affine (the header may be separate from the affine for an SPM image) against their cached copies, if they are the same and the ‘dirty’ flag is not set, we know that the filename is OK.

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 can override the result of these checks directly:

img = nibabel.load('some_image.nii')
assert img.is_dirty == False
hdr = img.get_header()
hdr.set_data_dtype(np.complex64)
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 resaving the data if that is not necessary), but drops back to the default (resaving the data) if there is any uncertainty, or the cost is too high to be able to check.