The self-attention mechanism proposed in the context for slice-to-volume registration (SVR) can be extended to handle non-rigid motion in fMRI scans by incorporating additional layers or modules in the neural network architecture. Non-rigid motion in fMRI scans involves deformations and distortions that go beyond simple translations and rotations. To address this, the self-attention mechanism can be enhanced to capture spatial relationships and dependencies across the slices and volumes more effectively.
One approach could involve integrating spatial transformers or deformable convolutional layers into the network. These components can learn to deform the input slices adaptively based on the motion present in the data. By allowing the network to learn non-linear transformations, it can better align the 2D slices with the 3D volume, even in the presence of complex non-rigid motion patterns.
Additionally, introducing spatial regularization techniques, such as spatial dropout or spatial batch normalization, can help the network generalize better to non-rigid motion scenarios. These techniques can encourage the model to focus on relevant spatial features and reduce overfitting to rigid transformations.