Channel Attention Networks for Robust MR Fingerprint Matching


Soyak R., Navruz E., Ersoy E. O. , Cruz G., Prieto C., King A. P. , ...More

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, vol.69, no.4, pp.1398-1405, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 69 Issue: 4
  • Publication Date: 2022
  • Doi Number: 10.1109/tbme.2021.3116877
  • Journal Name: IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Biotechnology Research Abstracts, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.1398-1405
  • Keywords: Dictionaries, Computer architecture, Image reconstruction, Convolutional neural networks, Convolution, Testing, Principal component analysis, Channel attention, deep learning, MR fingerprinting, reconstruction, RESONANCE, RECONSTRUCTION
  • Istanbul Technical University Affiliated: Yes

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.