Swin Transformer for Fast MRI
Jiahao Huang, Yingying Fang, Yinzhe Wu, Huanjun Wu, Zhifan Gao, Yang, Li, Javier Del Ser, Jun Xia, Guang Yang

TL;DR
This paper introduces SwinMR, a novel transformer-based method for fast MRI reconstruction that significantly improves image quality and robustness over existing techniques, reducing scan times and patient discomfort.
Contribution
The work presents a new Swin transformer architecture for MRI reconstruction, including a multi-channel loss and extensive validation on multiple datasets.
Findings
Achieved high-quality MRI reconstruction outperforming benchmarks.
Demonstrated robustness across various undersampling masks and noise levels.
Validated effectiveness through segmentation tasks on brain MRI datasets.
Abstract
Magnetic resonance imaging (MRI) is an important non-invasive clinical tool that can produce high-resolution and reproducible images. However, a long scanning time is required for high-quality MR images, which leads to exhaustion and discomfort of patients, inducing more artefacts due to voluntary movements of the patients and involuntary physiological movements. To accelerate the scanning process, methods by k-space undersampling and deep learning based reconstruction have been popularised. This work introduced SwinMR, a novel Swin transformer based method for fast MRI reconstruction. The whole network consisted of an input module (IM), a feature extraction module (FEM) and an output module (OM). The IM and OM were 2D convolutional layers and the FEM was composed of a cascaded of residual Swin transformer blocks (RSTBs) and 2D convolutional layers. The RSTB consisted of a series of…
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Taxonomy
TopicsAdvanced Neural Network Applications · Medical Image Segmentation Techniques · Brain Tumor Detection and Classification
MethodsFeatures Explanation Method · Multi-Head Attention · Attention Is All You Need · Linear Layer · Softmax · Residual Connection · Layer Normalization · Dense Connections · Stochastic Depth · Swin Transformer
