# NiBabel

### Access a cacophony of neuro-imaging file formats

# BIAP6 - Identifying image axes

Author:

  Matthew Brett

Status:

  Draft

Type:

  Standards

Created:

  2015-07-11

## Background

Image axes can have meaningful labels.

For example in a typical 4D NIfTI file, as we move along the 4th dimension in the image array, we are also moving in time. For example, this would be the first volume (in time):

    img = nibabel.load('my_4d.nii')
    data = img.get_data()
    vol0 = data[..., 0]

and this would be second volume in time:

    vol1 = data[..., 1]

It would therefore be reasonable to label the 4th axis of this image as ‘time’ or ‘t’.

We need to know which axis is the “time” axis for many reasons, including being able to select whole image volumes to align during motion correction, and doing spatial smoothing, where we want to avoid smoothing along the time dimension.

It is common to acquire MRI images one slice at a time. In a 3D or 4D NIfTI, the 3rd axis often contains these slices. So this these would be the first and second slices of data collected by the scanner:

    slice0 = vol0[:, :, 0]
    slice1 = vol0[:, :, 1]

In this case we might refer to the 3rd axis as the “slice” axis. We might care about knowing the “slice” axis, because we do processing specific to the slice axis, such as slice-timing correction.

For an individual 2D slice, MRI physicists distinguish between the image axis encoded during a single continual readout of the signal (frequency encoding direction) and the image axis encoded in a series of stepwise changes in the phase encode gradient (phase encoding direction). We care about the phase encoding direction because we usually correct for image distortion only along this direction.

Let us say that the first axis is the frequency encoding axis, and the second is the phase encoding axis. Now we can label all four of our axes:

- “frequency”;

- “phase”;

- “slice”;

- “time”.

In fact the NIfTI format can store this information. NIfTI specifies that the fourth image dimension should have units in terms of time (seconds), frequency (Hertz, radians per second) or concentration (parts per million), where the value difference between elements on the fourth axis is in `img.header['pixdim'][4]`, and the units of this difference are available in `img.header['xyzt_units']`. The field `img.header['dim_info']` can identify the frequency, phase and slice-encoding axes.

### Time axis as the fourth axis

In the NIfTI standard, time must be the fourth dimension.

In fact, the NIfTI standard specifies that the fourth axis must be time. If we want to store more than one volume that do not differ across time, then we have to set the 4th dimension to be length 1, and have 5th dimension have length > 1. Quoting from the standard:

    In NIFTI-1 files, dimensions 1,2,3 are for space, dimension 4 is for time,
    and dimension 5 is for storing multiple values at each spatiotemporal
    voxel.

This arrangement happens in practice. For example, SPM deformation fields have three values for each voxel (x, y, z displacement), and have shape (I, J, K, 1, 3):

    In [7]: img = nib.load('y_highres001.nii')
    In [8]: img.shape
    Out[8]: (121, 145, 121, 1, 3)

So, for correctly written NIfTI images, we can identify time by the fact that it is the fourth axis.

MGH format also appears to use the fourth dimension for time. The dimensions are listed in order `width, height, depth, nframes` and “frames” is always the slowest changing dimension in the image data buffer. Of course, in numpy, this does not tell us which axis this must be in the returned array, but at least the `load_mgh.m` MATLAB function (see MGH format) returns the frame axis as the last axis, as does nibabel.

The ECAT and PAR / REC formats seem to be primarily based on and stored as slices (2D arrays) which can then be concatenated to form volumes, implying a slowest-changing axis of volume. Nibabel currently arranges PAR images with volume as the 4th and last axis.

On the other hand, the MINC format:

1. gives specific names to the image data axes so we can directly find the time axis

2. expects (given the common ordering of these names in MINC files) that the time axis will be first:

       In [31]: mnc2 = h5py.File('nibabel/tests/data/minc2_4d.mnc', 'r')['minc-2.0']
       In [32]: mnc2['dimensions'].values()
       Out[32]:
       [<HDF5 dataset "time": shape (2,), type "<f8">,
       <HDF5 dataset "xspace": shape (), type "<i4">,
       <HDF5 dataset "yspace": shape (), type "<i4">,
       <HDF5 dataset "zspace": shape (), type "<i4">]

This reflects MINC’s lineage as C-library, where the C convention is for the first axis in an array is the slowest changing. `arr[0]` in a C-convention 4D array would be the first volume, where time (volume) is the slowest changing axis.

MINC2 uses HDF5 storage, and HDF5 uses C storage order for standard contiguous arrays on disk - see “7.3.2.5. C versus Fortran Dataspaces” in chapter 7 of the HDF5 user guide.

BrainVoyager STC files store data in (fastest to slowest changing) order: columns (of slice); rows (of slice); time; slice. The VTC stores the data in the (fast to slow) order: time; Anterior->Posterior; Superior->Inferior; Left->Right.

### Images can have more than four axes

We’ve already seen the example of NIfTI images where the 4th axis is length 1 and the 5th axis is length 3, encoding a deformation field.

This is a trick NIfTI uses to allow us to identify the “time” axis.

We can also have (rarely) images of 5D, where the time axis has length > 1. For example, some MR acquisitions take two echoes per time point, so we might have an image of shape (64, 64, 32, 200, 2), where the fourth axis is time and the fifth axis is echo number.

### The current nibabel convention

The nibabel rule of thumb has been that, when we return an image array, it should be in the order described in the format’s user documentation.

So, for NIfTI format images, the image dimension sizes are listed in fastest to slowest changing order, implying that the expected array to be returned will have that same axis order. Time is always the fourth (rather than the first) dimension of a 4D NIfTI. Nibabel NIfTI images return the array in that order, and the time / volume axis is the last in a 4D nibabel NIfTI image array.

On the other hand MINC clearly expects that the axes will be returned in the order the axes are listed in the MINC file. This is also (usually) the slowest-to-fastest changing order in the underlying file, and by convention, the first axis is the time axis. Nibabel MINC images return the array in this same order with the time / volume axis first, but in general it returns the array with the axes in the order listed in the MINC file.

We don’t currently have BrainVoyager support, so this will be a decision we have to make before finalizing the API.

### Distinguishing time and volume

A volume is a complete set of slices making up one brain image.

In NIfTI:

- 3D image: volume == image array i.e. `arr[:, :, :]`;

- > 3D image: volume == a single slice over the final dim > 3 dimensions e.g.: `arr[:, :, :, 2]` (4D); `arr[:, :, :, 0, 3]` (5D).

We saw above that the MGH format refers to a volume (in our sense) as a frame. ECAT has the same usage - a frame is a 3D volume. The fmristat software uses frame in the same sense, e.g., line 32 of example.m.

Unfortunately DICOM appears to use “frame” to mean a 2D slice. For example, here is the definition of a “multi-frame image”:

    3.8.9 Multi-frame image:
        Image that contains multiple two-dimensional pixel planes.

From PS 3.3 of the 2011 DICOM standard.

## Possible solutions to finding axes

A general solution for finding axes would be to attach axis labels to the returned image data array, or to the image object.

A less general solution would be to identify the time axis by convention - say - by being the fourth axis in a 4D array.

Finding the time axis is an urgent problem, because we are currently considering utility routines for (spatial) smoothing, and viewing images, that need to know which axis is time.

### General solution: associating axes and labels

Possible options:

- Add a property time_axis_index to the image class. This always returns 3 (4th axis) for images other than MINC. For MINC, it returns the index of the image dimension labeled `time`;

- Add a property axis_labels to the image class. By default, most image types return ‘i’, ‘j’, ‘k’, ‘time’. MINC returns the image dimension labels;

- Copy or depend on datarray (no other dependencies) or xray (depends on Pandas). Use these to attach labels directly to the image data array axes. These labels could then be preserved through operations like slicing.

### Using convention : enforcing time as 4th axis

This solution could be implemented as well as the solution using labels.

At the moment, we can always identify the time axis in the NIfTI file, because it is the 4th axis in the returned image.

This is probably so for:

- PAR/REC

- ECAT

- MGH

but not so for MINC1 or MINC2, where time is typically (?always) the first axis.

One option would be to make a new MINC1, MINC2 image class that reorders the MINC axes to have time last. Call these new classes NiMINC1, NiMINC2.

In order to avoid surprise, we continue to return MINC1, MINC2 class images from nibabel.load, but give a `DeprecationWarning` when doing this, saying that the default load will change in future versions of nibabel, and suggesting the as_niminc=True keyword-only argument to load, defaulting to as_niminc=False (giving the current nibabel behavior).

In Nibabel 3.0, we require the as_niminc keyword argument.

In Nibabel 4.0, we default to as_niminc=True.

We would still have to deal with MINC1, MINC2 images in memory - and therefore cannot in general assume that the fourth dimension of any image data array is time. In order to deal with this, routines that need to know the time dimension would have to check whether they were dealing with MINC1, MINC2, which ends up being similar to the time_axis_index option above.