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
this https URL

本文提出了一种新颖的使用视觉 Transformer（ViTs）处理体积医学信息的框架，通过将先进的 Swin Transformer 模型拓展到三维医学领域，以及在 ViTs 中处理体积信息和编码位置的新方法。我们提出了基于 Transformer 的体积方法 SuperFormer，用于磁共振成像（MRI）超分辨率，通过利用 MRI 域的三维信息和具有三维相对位置编码的局部自注意机制来恢复解剖细节，并结合体积域和特征域的多域信息进行高分辨率 MRI 的重建，对人体连通项目数据集进行了广泛验证，并证明了体积 Transformer 相对于基于三维 CNN 的方法的优越性。