一个统一的混合变压器用于关节MRI序列的超分辨率和缺失数据归算
Yulin Wang1, Haifeng Hu1, Shangqian Yu1
1Key Laboratory of Biomedical Engineering of Hainan Province, the School of Biomedical Engineering, Hainan University, Haikou, People's Republic of China.
Physics in medicine and biology
|June 7, 2023
概括
SIFormer通过重建样本不足的图像并同时合成缺失的序列来增强磁共振成像 (MRI). 这种新的框架提高了临床环境中的诊断准确性和效率.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 高分辨率的多模态磁共振成像 (MRI) 对于诊断至关重要.
- 获得多个MRI序列往往受到成本和图像质量问题的限制.
研究的目的:
- 开发用于同时超分辨率 (SR) 和缺失MRI序列的归算的统一框架.
- 为解决临床和研究应用MRI采集的局限性.
主要方法:
- 提出SIFormer,一个使用生成器和区分器的混合框架.
- 发电机具有双分支注意力区块 (变压器+CNN) 和可学习的网关适应MLP.
- 框架重建了样本不足的图像,并在一个过程中赋予了缺失的序列.
主要成果:
- 在量化表现方面,SIFormer的表现优于六种最先进的方法.
- 在图像SR和跨多个数据集的合成方面取得了视觉上优异的结果.
- 在多中心,多对比度MRI数据上证明有效.
结论:
- SIFormer为增强MRI数据采集提供了一个有前途的解决方案.
- 有潜力补充临床和研究MRI序列获取.
- 适用于健康人群和脑瘤患者.
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