Nibabel's As_closest_canonical: Should It Drop Scanner Anat?

Aug 15, 2025 by Marta Kowalska 61 views

Should `as_closest_canonical` Drop Scanner Anat Q/Sform Code? A Discussion

Hey everyone, let's dive into a pretty interesting question about how as_closest_canonical in Nibabel handles sform and qform codes, specifically the NIFTI_XFORM_SCANNER_ANAT code. This is super relevant for anyone working with medical imaging data, especially when converting DICOM files to NIfTI format.

The Curious Case of `as_closest_canonical` and Coordinate System Transformations

So, the main point of discussion revolves around the behavior of nibabel.as_closest_canonical. For those unfamiliar, this function is designed to reorder image axes to match a canonical orientation (like RAS+ in neuroimaging). This is a crucial step in many pipelines to ensure consistent orientation across datasets, making analysis and visualization much smoother. However, it seems that this reordering can sometimes lead to changes in the sform and qform codes within the NIfTI header.

Now, why is this important? These codes essentially tell us about the coordinate system transformations applied to the image. Specifically, a code of 1 often indicates that the image's coordinate system is aligned with the scanner's coordinate system (NIFTI_XFORM_SCANNER_ANAT). This is typically what you'd expect after converting a DICOM image to NIfTI using tools like dcm2niix. Tools like dcm2niix usually set these codes to 1 when converting DICOM to NIfTI, indicating the image is in scanner anatomical space.

The counter-intuitive part is that as_closest_canonical, while reorienting the image, might change these codes. Let's look at an example to illustrate this. Consider this scenario, when you convert a DICOM to NIfTI using dcm2niix, you often get sform and qform codes set to 1, representing the scanner anatomical space. However, after applying as_closest_canonical, these codes can change, potentially to 2 or even 0. This change in sform and qform codes after applying as_closest_canonical raises a fundamental question: Should we expect this function to maintain these codes, especially when the original image is already in scanner anatomical space? It seems unexpected that reorienting the image would alter the fundamental representation of its coordinate system, especially when the initial conversion process explicitly sets these codes.

A Practical Example: DICOM to NIfTI Conversion and Code Changes

To demonstrate this, let's walk through a scenario using dcm2niix and Nibabel. We'll use a sample DICOM dataset and observe how the sform and qform codes change after applying as_closest_canonical. First, we'll clone a repository containing sample DICOM data:

git clone https://github.com/effigies/nitest-dicom.git
cd nitest-dicom.git

Next, we'll use dcm2niix to convert a DICOM file to NIfTI format. The -f test flag sets the output filename prefix, and -o . specifies the output directory (current directory in this case):

dcm2niix -f test -o . 4d_multiframe_with_derived.dcm

Now, let's use Python and Nibabel to load the converted NIfTI image and inspect the sform and qform codes before and after applying as_closest_canonical:

import nibabel as nib
nii = nib.load("test.nii")
niiacc = nib.as_closest_canonical(nii)
print(nii.header['sform_code'])
print(nii.header['qform_code'])
print(niiacc.header['sform_code'])
print(niiacc.header['qform_code'])

Running this code, you might observe an output like this:

As you can see, the sform and qform codes initially are 1 (Scanner Anat). However, after applying as_closest_canonical, the sform code changes to 2, and the qform code changes to 0. This indicates a change in the coordinate system representation, which might not always be the desired outcome.

The Core Question: Should `as_closest_canonical` Preserve Scanner Anat Codes?

This brings us to the central question: Should as_closest_canonical be designed to maintain the scanner anatomical space representation (sform and qform codes of 1), or is the current behavior acceptable? One might argue that if an image is already in scanner anatomical space, reorienting it shouldn't necessarily change this fundamental representation. Imagine, for example, if you flip the rows during the dcm2niix conversion (dcm2niix -y n -f test -o . 4d_multiframe_with_derived.dcm), you'd still expect to get scanner anatomical space. Should as_closest_canonical then maintain these codes consistently, regardless of the reorientation?

Exploring the Implications and Potential Solutions

This behavior has implications for downstream analysis. If you're relying on the sform and qform codes to accurately represent the image's coordinate system, this change could lead to unexpected results. For instance, if you're performing spatial normalization or comparing images across different datasets, inconsistent coordinate system representations can introduce errors. It's crucial to have a clear understanding of how coordinate systems are handled throughout your processing pipeline to ensure the accuracy and reliability of your results.

One potential solution could be to modify as_closest_canonical to preserve the scanner anatomical space representation if it's initially present. This might involve checking the initial sform and qform codes and, if they indicate scanner anatomical space, ensuring that the reoriented image maintains this representation. This would provide a more consistent and predictable behavior, especially for users who rely on these codes for accurate spatial transformations. This enhancement would give users greater confidence in the as_closest_canonical function's ability to maintain coordinate system integrity, reducing the likelihood of errors in subsequent analyses.

Another approach could be to provide a flag or option to control this behavior. For example, a preserve_scanner_anat flag could be added to as_closest_canonical. When set to True, the function would attempt to maintain the scanner anatomical space representation. This would give users more flexibility in how they handle coordinate system transformations, allowing them to choose the behavior that best suits their specific needs.

Alternative Perspectives and Use Cases

However, there might be scenarios where the current behavior is desirable. For example, if the goal is to strictly enforce a specific canonical orientation (like RAS+), changing the sform and qform codes might be necessary to ensure consistency across all images. In these cases, explicitly transforming the image to a known coordinate system and updating the header information accordingly might be the preferred approach. It is crucial to consider the broader context of image processing workflows and how coordinate system transformations fit into the overall analytical goals.

Ultimately, the best approach depends on the specific use case and the desired level of control over coordinate system transformations. A deeper discussion within the Nibabel community is warranted to explore these different perspectives and determine the most appropriate way to handle sform and qform codes in as_closest_canonical.

A Call for Community Discussion and Potential Improvements

This discussion highlights the importance of understanding how image orientation and coordinate systems are handled in neuroimaging software. The current behavior of as_closest_canonical might be counter-intuitive in some cases, and a modification or enhancement could improve its usability and predictability. Let's discuss this further! What are your thoughts? Have you encountered similar issues? What solutions would you propose?

It's crucial to foster open discussions within the neuroimaging community to ensure that tools like Nibabel continue to meet the evolving needs of researchers and clinicians. By sharing experiences, insights, and potential solutions, we can collectively improve the reliability and robustness of our image processing pipelines.

In conclusion, the behavior of as_closest_canonical in Nibabel regarding sform and qform codes warrants careful consideration. The potential for these codes to change during reorientation raises questions about the function's intended behavior and its impact on downstream analyses. Community discussion and collaborative problem-solving are essential to ensure that Nibabel remains a valuable and dependable tool for the neuroimaging community. Whether through modifications to the existing function or the introduction of new options, the goal is to provide users with greater control and clarity over coordinate system transformations in their image processing workflows.