CIT168 Reinforcement learning atlas

Pauli, W.M., Nili, A.N., Tyszka, J.M., 2018. A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei. Sci Data 5, 180063.

For details regarding the atlas and labels, please refer to the reference above.

There are two possible approaches to bring the atlas labels to the GRE space, which approach will be used depending on the input of the user.

Approach 1: Proving NIFTI data input

Data required:

• a 3D GRE magnitude NIFTI image (e.g. 1st echo)
• a 3D GRE NIFTI brain mask
• a T1w NIFTI images
• a T1w NIFTI brain mask

Procedures:

1. (optional) Bias field correction on both T1w and GRE image using N4BiasFieldCorrection.
2. Coregistration between GRE image and T1w image using rigid-body transformation.
3. Coregistration between T1w image and the T1w image provided with the atlas using nonlinear transformation (SyN).
4. Applying the derived transformation matrices to bring the atlas labels to the GRE space.

Approach 2: Proving the transformation information derived from ANTs

Data required:

• a 3D GRE magnitude NIFTI image (to define the final space only)
• a GRE to T1w rigid-body transformation matrix file (usually with suffix _0GenericAffine.mat)
• a T1w to atlas T1w template affine transformation matrix file (usually with suffix _0GenericAffine.mat)
• a T1w to atlas T1w template inverse wrap field (usually with suffix _1InverseWarp.nii.gz)

Procedure:

1. The provided transformation information is used to bring the atlas labels to the GRE space.