Papers › Swin Deformable Attention U-Net Transformer (SDAUT) for Explainable Fast MRI

Swin Deformable Attention U-Net Transformer (SDAUT) for Explainable Fast MRI

5 Jul 2022arXiv:2207.02390archive 2025-07-28

Jiahao Huang, Xiaodan Xing, Zhifan Gao, Guang Yang

Fast MRI aims to reconstruct a high fidelity image from partially observed measurements. Exuberant development in fast MRI using deep learning has been witnessed recently. Meanwhile, novel deep learning paradigms, e.g., Transformer based models, are fast-growing in natural language processing and promptly developed for computer vision and medical image analysis due to their prominent performance. Nevertheless, due to the complexity of the Transformer, the application of fast MRI may not be straightforward. The main obstacle is the computational cost of the self-attention layer, which is the core part of the Transformer, can be expensive for high resolution MRI inputs. In this study, we propose a new Transformer architecture for solving fast MRI that coupled Shifted Windows Transformer with U-Net to reduce the network complexity. We incorporate deformable attention to construe the explainability of our reconstruction model. We empirically demonstrate that our method achieves consistently superior performance on the fast MRI task. Besides, compared to state-of-the-art Transformer models, our method has fewer network parameters while revealing explainability. The code is publicly available at https://github.com/ayanglab/SDAUT.

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Medical Image Analysis

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Absolute Position Encodings Adam Attention BPE Concatenated Skip Connection Convolution Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Max Pooling Multi-Head Attention Position-Wise Feed-Forward Layer ReLU Residual Connection Softmax Transformer U-Net

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