  We present a keypoint-based foundation model for general purpose brain MRI
registration, based on the recently-proposed KeyMorph framework. Our model,
called BrainMorph, serves as a tool that supports multi-modal, pairwise, and
scalable groupwise registration. BrainMorph is trained on a massive dataset of
over 100,000 3D volumes, skull-stripped and non-skull-stripped, from nearly
16,000 unique healthy and diseased subjects. BrainMorph is robust to large
misalignments, interpretable via interrogating automatically-extracted
keypoints, and enables rapid and controllable generation of many plausible
transformations with different alignment types and different degrees of
nonlinearity at test-time. We demonstrate the superiority of BrainMorph in
solving 3D rigid, affine, and nonlinear registration on a variety of
multi-modal brain MRI scans of healthy and diseased subjects, in both the
pairwise and groupwise setting. In particular, we show registration accuracy
and speeds that surpass current state-of-the-art methods, especially in the
context of large initial misalignments and large group settings. All code and
models are available at https://github.com/alanqrwang/brainmorph.
