Automatic Head Model including 1010-System / Electrode Placement

[bryn]

The T1w image is placed in world (scanner) coordinates. This is useful, e.g. to align different acquisitions (e.g. T1, T2 with different resolutions or field of view).

You could remove the rotation and translation, e.g., by setting an identity transform. However, in my experience, it is often useful to preserve the position in world coordinates.

img = XCoreModeling.Import("some_t1w_mri.nii.gz")
img.Transform = XCoreModeling.Transform()

[lucky_lin]

@bryn I set the rotation and translation of T1W and the grid to 0, but I cannot generate the landmarks. [Error] Exception during import: Expecting 'Version 1.0' on first line Modeler : [Error] operation unsuccessful.

[bryn]

I don't understand what you are doing. The error looks like Sim4Life cannot parse the .pts file produced by the landmark predictor.

• did you edit the .pts file manually?
• where/how did you set the rotation/translation to "0"?
• did you try to set the transform [to zero] (before running the prediction) as suggested in my last post?

[lucky_lin]

After importing T1W in the GUI, it is not aligned with the grid, so I adjusted the rotation and translation of T1W in the controller.
I did not modify the .pts file; I just made the changes above and then tried to generate the landmarks.

[bryn]

But previously you managed to generate them, i.e. before you edited the transform?

[lucky_lin]

No, I only did these things after creating a new file.

[bryn]

I can reproduce your issue. If I set the transform to "0", i.e. Identity, the predictor fails. The head40 segmentation is also less accurate! We need to investigate.

A workaround would be:

• load image
• predict landmarks & segmentation
• compute inverse image transform
• apply this inverse to landmarks/segmentation/surfaces/etc

# assumes verts and labelfield are already predicted (without setting transform to "0")
inv_tx = img.Transform.Inverse()

# transform segmentation
labelfield.ApplyTransform(inv_tx )

# transform landmarks
for v in verts:
    v.ApplyTransform(inv_tx )

[bryn]

The issue is that our neural network was trained with the data in the RAS orientation (with some deviation +- 15 degrees, and flipping in all axes). If you manually edit the transform, you break assumptions used to pre-orient the data into RAS ordering.

Since RAS is a widely used convention in neuroscience, and medical images are always acquired with a direction (rotation) matrix and offset (translation), I think it is best you don't modify the transform.

For instance, if you try to assign DTI-based conductivity maps - you will need to rotate the grid AND the tensors accordingly. It can be done, but it will be more effort...

If this is to investigate if the fields are (nearly) symmetric, I suggest you

• find an approximate symmetry plane (wrt to the brain or skull or ...)
• align the plane of a slice viewer perpendicular to the symmetry plane

[lucky_lin]

@bryn Does this code only segment 40 types of tissues by default? I want to segment 16 types.

[bryn]

The default is 40 tissues. To be explicit you can specify this via

import ImageML

labelfield = ImageML.HeadModelGeneration([img], output_spacing=0.6, add_dura=False, version=ImageML.eHeadModel.head40)

For 30 (or 16) tissues you would specify the version head30 (or head16)

import ImageML

labelfield = ImageML.HeadModelGeneration([img], output_spacing=0.6, add_dura=False, version=ImageML.eHeadModel.head30)

But please note: the versions are an evolution. The head16 segmentation is not the same, with fewer tissues. It is also less accurate, as it was the first version we published (and trained on less training data).