# NiBabel

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

# BIAP4 - Merging nibabel and dcmstack

Author:

  Brendan Moloney, Matthew Brett

Status:

  Draft

Type:

  Standards

Created:

  2012-11-21

In which we set out what dcmstack does and how it might integrate with the nibabel objects and functions.

## Motivation

It is very common to convert source DICOM images to another format, typically Nifti, before doing any image processing. The Nifti format is significantly easier to work with and has wide spread compatibility. However, the vast amount of meta data stored in the source DICOM files will be lost.

After implementing this proposal, users will be able to preserve all of the meta data from the DICOM files during conversion, including meta data from private elements. The meta data will then be easily accessible through the SpatialImage API:

    >>> nii = nb.load('input.nii')
    >>> data = nii.get_data()
    >>> print data.shape
    (256, 256, 24, 8)
    >>> print nii.get_meta('RepetitionTime')
    3500.0
    >>> echo_times = [nii.get_meta('EchoTime', (0, 0, 0, idx))
                      for idx in xrange(data.shape[-1])]
    >>> print echo_times
    [16.4, 32.8, 49.2, 65.6, 82.0, 98.4, 114.8, 131.2]
    >>> print nii.get_meta('AcquisitionTime', (0, 0, 1, 0))
    110455.370000
    >>> print nii.get_meta('AcquisitionTime', (0, 0, 2, 0))
    110457.272500
    >>> print nii.get_meta('AcquisitionTime', (0, 0, 1, 1))
    110455.387500

## Overview

dcmstack reads a series of DICOM images, works out their relationship in terms of slices and volumes, and compiles them into multidimensional volumes. It can produce the corresponding data volume and affine, or a Nifti image (with any additional header information set appropriately).

In the course of the read, dcmstack creates a DcmMeta object for each input file. This object is an ordered mapping that can contain a copy of all the meta data in the DICOM header. By default some filtering is applied to reduce the chance of including PHI. The set of DcmMeta objects are then merged together in the same order as the image data to create a single DcmMeta object that summarizes all of the meta data for the series.

To summarize the meta data, each element is classified based on how the values repeat (e.g. const, per_slice, per_volume, etc.). Each element has a name (the keyword from the DICOM standard) and one or more values (the number of values depends on the classification and the shape of the image). Each classification’s meta data is stored stored in a separate nested dictionary.

While creating the Nifti image output, the DcmMeta is stored in a DcmMetaExtension which can be added as a header extension. This extension simply does a JSON encoding directly on the DcmMeta object.

When working with these images, it’s possible to keep track of the meta-information in the DcmMetaExtension. For example, when taking slice out of a 3D volume, we keep track of the information specific to the chosen slice, and remove information for other slices. Or when merging 3D volumes to a 4D time series, we want to merge together the meta data too.

At the moment, dcmstack only creates Nifti images. There’s no reason that this should be so, and the relationship of dcmstack to other spatial images should be more flexible.

## Issues

### DcmMetaExtension tied to NiftiExtension

At the moment, DcmMetaExtension inherits from the NiftiExtension, allowing the data to be dumped out to JSON when writing into the extension part of a Nifti header.

There’s no reason that the DcmMetaExtension should be tied to the Nifti format.

#### Plan

Refactor DcmMetaExtension to inherit from object. Maybe rename DcmMeta or something. Make a NiftiExtension object when needed with a new object wrapping the DcmMeta in the Extension API?

#### Status

Resolved. We now have a separate DcmMeta object which inherits from OrderedDict and contains all of the functionality previously in DcmMetaExtension except those related to acting as a Nifti1Extension. The DcmMetaExtension now provides just the functionality for being a Nifti1Extension.

### Keeping track of metadata when manipulating images

When slicing images, it is good to be able to keep track of the relevant DICOM metadata for the particular slice. Or when merging images, it is good to be able to compile the metadata across slices into the (e.g) volume metadata. Or, say, when coregistering an image, it is good to be able to know that the metadata that is per-slice no longer directly corresponds to a slice of the data array.

At the moment, dcmstack deals with this by wrapping the image with DICOM meta information in NiftiWrapper object : see https://github.com/moloney/dcmstack/blob/d157741/src/dcmstack/dcmmeta.py#L1232. This object accepts a Nifti image as input, that usually contains a DcmMetaExtension, and has methods get_meta (to get metadata from extension), split (for taking slice specific metadata into the split parts), meta_valid to check the metadata against the Nifti information, and methods to remove / replace the extension, save to a filename, and create the object with various alternative classmethod constructors.

In particular, the meta_valid method needs to know about both the enclosed image, and the enclosed meta data.

Can we put this stuff into the SpatialImage image object of nibabel, so we don’t need this wrapper object?

#### Plan

Put the DcmMeta data into the extra object that is input to the SpatialImage and all other nibabel image types.

Add a get_meta method to SpatialImage that uses the to-be-defined API of the extra object. Maybe, by default, this would just get keys out of the mapping.

Define an API for the extra object to give back metadata that is potentially varying (per slice or volume). We also need a way to populate the extra object when loading an image that has an associated DcmMeta object.

Use this API to get metadata. Try and make this work with functions outside the SpatialImage such as four_to_three and three_to_four in nibabel.funcs. These functions could use the extra API to get varying meta-information.

** TODO : specific proposal for SpatialImage and extra API changes **

### Detecting slice or volume-specific data difficult for 3D and 4D DICOMS

The DcmMeta object needs to be able to identify slice and volume specific information when reading the DICOM, so that it can correctly split the resulting metadata, or merge it.

This is easy for slice-by-slice DICOM files because anything that differs between the slices is by definition slice-specific. For 3D and 4D data, such as Siemens Mosaic, some of the fields in the private headers contains slice-by-slice information for the volume contained. There’s not automatic way of detecting slice-by-slice information in this case, so we have to specify which fields are slice-by-slice when reading. That is, we need to specialize the DICOM read for each type of volume-containing DICOM - such as Mosaic or the Philips multi-frame format.

#### Plan

Add create_dcmmeta method to the nibabel DICOM wrapper objects, that can be specialized for each known DICOM format variation. Put the rules for slice information etc into each class.

For the Siemens files, we will need to make a list of elements from the private CSA headers that are known to be slice specific. For the multiframe DICOM files we should be able to do this in a programmatic manner, since the varying data should live in the PerFrameFunctionalSequence DICOM element. Each element that is reclassified should be simplified with the DcmMeta.simplify method so that it can be classified appropriately.

### Meta data in nested DICOM sequences can not be independently classified

The code for summarizing meta data only works on the top level of key/value pairs. Any value that is a nested dataset is treated as a single entity, which prevents us from classifying its individual elements differently.

In a DICOM data set, any element that is a sequence contains one or more nested DICOM data sets. For most MRI images this is not an issue since they rarely contain many sequences, and the ones they do are usually small and relatively unimportant. However in multiframe DICOM files make heavy use of nested sequences to store data.

#### Plan

This same issue was solved for the translated Siemens CSA sub headers by unpacking each nested dataset by joining the keys from each level with a dotted notation. For example, in the CsaSeries subheader there is a nested MrPhoenixProtocol dataset which has an element ulVersion so the key we use after unpacking is CsaSeries.MrPhoenixProtocol.ulVersion.

We can take the same approach for DICOM sequence elements. One additional consideration is that each of these element is actually a list of data sets, so we would need to add an index number to the key somehow.

The alternative is to handle nested data sets recursively in the meta data summarizing code. This would be fairly complex and you would no longer be able to refer to each element with a single string, at least not without some mini-language for traversing the nested datasets.

### Improving access to varying meta data through the Nifti

Currently, when accessing varying meta data through the get_meta method you can only get one value at a time:

    >>> echo_times = [nii.get_meta('EchoTime', (0, 0, 0, idx))
                      for idx in xrange(data.shape[-1])]

You can easily get multiple values from the DcmMeta object itself, but then you lose the capability to automatically check if the meta data is valid in relation to the current image.