SuperFormer: Volumetric Transformer Architectures for MRI Super-Resolution
<|reference_start|>SuperFormer: Volumetric Transformer Architectures for MRI Super-Resolution: 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.<|reference_end|>
@article{forigua2024superformer:,
    title={SuperFormer: Volumetric Transformer Architectures for MRI
  Super-Resolution},
    author={Cristhian Forigua, Maria Escobar and Pablo Arbelaez},
    journal={7th International Workshop, SASHIMI 2022, Held in Conjunction with
  MICCAI 2022, Singapore, September 18, 2022, Proceedings},
    year={2024},
    doi={10.1007/978-3-031-16980-9_13},
    archivePrefix={arXiv},
    eprint={2406.03359},
    primaryClass={eess.IV cs.CV}
}