ReBrain: Rebuilding MRI from Sparse CT Slices with Retrieval‑Augmented Diffusion

ReBrain: Rebuilding MRI from Sparse CT Slices with Retrieval‑Augmented Diffusion

Can AI fill in the MRI gaps?

When MRI isn’t possible, doctors rely on CT—but low‑dose CT can be very sparse. ReBrain is a research system that reconstructs a 3D, MRI‑like brain volume from just a limited set of CT slices.

  • It uses a diffusion model to “paint” missing MRI slices from available CT.
  • It retrieves look‑alike CT slices from a large database and, via a guidance network, keeps structures continuous and realistic.
  • If retrieval comes up short, it blends alternative cues with spherical linear interpolation to stay on track.

Tested on public datasets (SynthRAD2023, BraTS), ReBrain reached state‑of‑the‑art accuracy for this cross‑modal, low‑data setting.

Goal: more usable brain imaging when MRI access or patient tolerance is limited—not a replacement for clinical MRI.

Paper: https://arxiv.org/abs/2511.17068v1

Paper: https://arxiv.org/abs/2511.17068v1

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