Abstract:
Objective: Magnetic Resonance Fingerprinting (MRF) enables simultaneous mapping of multiple tissue parameters such as T1 and T2 relaxation times. The working principle of...Show MoreMetadata
Abstract:
Objective: Magnetic Resonance Fingerprinting (MRF) enables simultaneous mapping of multiple tissue parameters such as T1 and T2 relaxation times. The working principle of MRF relies on varying acquisition parameters pseudo-randomly, so that each tissue generates its unique signal evolution during scanning. Even though MRF provides faster scanning, it has disadvantages such as erroneous and slow generation of the corresponding parametric maps, which needs to be improved. Moreover, there is a need for explainable architectures for understanding the guiding signals to generate accurate parametric maps. Methods: In this paper, we addressed both of these shortcomings by proposing a novel neural network architecture (CONV-ICA) consisting of a channel-wise attention module and a fully convolutional network. Another contribution of this study is a new channel selection method: attention-based channel selection. Furthermore, the effect of patch size and temporal frames of MRF signal on channel reduction are analyzed by employing a channel-wise attention. Results: The proposed approach, evaluated over 3 simulated MRF signals, reduces error in the reconstruction of tissue parameters by 8.88% for T1 and 75.44% for T2 with respect to state-of-the-art methods. Conclusion: It is demonstrated that channel attention mechanism helps to focus on informative channels and fully convolutional network extracts spatial information achieve the best reconstruction performance. Significance: As a consequence of improvement in fast and accurate manner, presented work can contribute to make MRF appropriate for clinical use.
Published in: IEEE Transactions on Biomedical Engineering ( Volume: 69, Issue: 4, April 2022)
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- IEEE Keywords
- Index Terms
- Channel Attention ,
- Channel Attention Network ,
- Neural Network ,
- Convolutional Network ,
- Relaxation Time ,
- Attention Mechanism ,
- Parametric Mapping ,
- Patch Size ,
- Channel Information ,
- Attention Module ,
- Channel Selection ,
- Reconstruction Performance ,
- T2 Relaxation Times ,
- Temporal Frame ,
- Channel Attention Mechanism ,
- Channel Reduction ,
- Convolutional Neural Network ,
- Convolutional Layers ,
- Qualitative Results ,
- Long Short-term Memory ,
- Convolutional Neural Network Architecture ,
- Important Channel ,
- T2 Mapping ,
- Attention Scores ,
- ReLU Activation Function ,
- Channel Attention Module ,
- Sigmoid Activation Function ,
- N-channel ,
- Recurrent Neural Network ,
- T2 Values
- Author Keywords
- MeSH Terms
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Channel Attention ,
- Channel Attention Network ,
- Neural Network ,
- Convolutional Network ,
- Relaxation Time ,
- Attention Mechanism ,
- Parametric Mapping ,
- Patch Size ,
- Channel Information ,
- Attention Module ,
- Channel Selection ,
- Reconstruction Performance ,
- T2 Relaxation Times ,
- Temporal Frame ,
- Channel Attention Mechanism ,
- Channel Reduction ,
- Convolutional Neural Network ,
- Convolutional Layers ,
- Qualitative Results ,
- Long Short-term Memory ,
- Convolutional Neural Network Architecture ,
- Important Channel ,
- T2 Mapping ,
- Attention Scores ,
- ReLU Activation Function ,
- Channel Attention Module ,
- Sigmoid Activation Function ,
- N-channel ,
- Recurrent Neural Network ,
- T2 Values
- Author Keywords
- MeSH Terms