  This paper presents a novel framework for processing volumetric medical
information using Visual Transformers (ViTs). First, We extend the
state-of-the-art Swin Transformer model to the 3D medical domain. Second, we
propose a new approach for processing volumetric information and encoding
position in ViTs for 3D applications. We instantiate the proposed framework and
present SuperFormer, a volumetric transformer-based approach for Magnetic
Resonance Imaging (MRI) Super-Resolution. Our method leverages the 3D
information of the MRI domain and uses a local self-attention mechanism with a
3D relative positional encoding to recover anatomical details. In addition, our
approach takes advantage of multi-domain information from volume and feature
domains and fuses them to reconstruct the High-Resolution MRI. We perform an
extensive validation on the Human Connectome Project dataset and demonstrate
the superiority of volumetric transformers over 3D CNN-based methods. Our code
and pretrained models are available at
https://github.com/BCV-Uniandes/SuperFormer.
