SuperFormer: Volumetric Transformer Architectures for MRI Super-Resolution

• Published in: Simulation and Synthesis in Medical Imaging Vol. 13570; pp. 132 - 141
• Main Authors: Forigua, Cristhian, Escobar, Maria, Arbelaez, Pablo
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2022; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: MRI reconstruction; Super-resolution; Visual transformers
• ISBN: 9783031169793; 3031169794
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-031-16980-9_13

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.