Coupling of state space modules and attention mechanisms: An input‐aware multi‐contrast MRI synthesis method

Our proposed method could effectively model not only the high-dimensional, nonlinear mapping relationships between the magnetic signals of the hydrogen nucleus in tissues and the proton density signals in tissues, but also of the recovery process of suppressed liquid signals in FLAIR. The model proposed in our work employed distinct mechanisms in the synthesis of images belonging to normal and lesion samples, which demonstrated that our model had a profound comprehension of the input data. We also proved that in a hierarchical network, only the deeper self-attention layers were responsible for directing more attention on lesion areas.

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Publication Details

Journal
Medical Physics
Published
2024-12-23
DOI
https://doi.org/10.1002/mp.17598
Citations
3
Primary Topic
Advanced Neural Network Applications
Type
article
Field-Weighted Citation Impact
0.75
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article

Coupling of state space modules and attention mechanisms: An input‐aware multi‐contrast MRI synthesis method

Yunzhu Qian, Huazheng Liang, Xuefeng Zhou, Ruoyu Zhang et al.
3 citations
Medical Physics
Advanced Neural Network Applications
0.75
article

Coupling of state space modules and attention mechanisms: An input‐aware multi‐contrast MRI synthesis method

Yunzhu Qian, Huazheng Liang, Xuefeng Zhou, Ruoyu Zhang, Shuai Chen
article en
3 citations

Abstract

Our proposed method could effectively model not only the high-dimensional, nonlinear mapping relationships between the magnetic signals of the hydrogen nucleus in tissues and the proton density signals in tissues, but also of the recovery process of suppressed liquid signals in FLAIR. The model proposed in our work employed distinct mechanisms in the synthesis of images belonging to normal and lesion samples, which demonstrated that our model had a profound comprehension of the input data. We also proved that in a hierarchical network, only the deeper self-attention layers were responsible for directing more attention on lesion areas.

Medical PhysicsVol. 52(4)
Tongji University (CN), Soochow University (CN), Suzhou Research Institute (CN), Southeast University (CN)
Partnerships for the goals
Openalex Percentile: Top 26%
Advanced Neural Network Applications
0.75
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