RETNet: 解析度增强 基于X空间的磁粒子成像变压器网络
Lishuang Guo1, Chenbin Ma2, Zhen Dong1
1School of Engineering Medicine, Beihang University, Beijing, 100191, People's Republic of China; Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, Beijing, 100191, People's Republic of China; School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, People's Republic of China.
Computers in biology and medicine
|August 27, 2024
概括
磁粒子成像 (MPI) 的分辨率通过一种新的深度学习方法得到了增强. 解析度增强变压器网络 (RETNet) 从低分辨率输入中重建高分辨率图像,改善细节,而不会增加系统噪音或功率.
科学领域:
- 生物医学成像技术 生物医学成像技术
- 医疗图像重建 医疗图像重建
- 深度学习应用程序
背景情况:
- 磁性粒子成像 (MPI) 提供高灵敏度和时间分辨率可用于可视化超偏磁铁氧化物纳米粒子 (SPIONs).
- 在MPI中的低空间分辨率目前限制了其临床适用性.
- 增加梯度强度可以提高分辨率,但会增加功耗和噪音水平.
研究的目的:
- 开发一种具有成本效益的深度学习方法,用于从低分辨率数据中重建高分辨率MPI图像.
- 为了避免高梯度场的缺点,例如增加功耗和噪声.
主要方法:
- 提出了一个分辨率增强变压器网络 (RETNet),使用浅特征提取器,用于纹理特征的交叉尺度变压器 (CST) 和用于结构特征的残余旋转变压器 (RST).
- 采用图像重建模块来汇总提取的特征以生成高清图像.
- 进行了废弃研究,以验证CST和RST模块的贡献.
主要成果:
- 从低分辨率的MPI数据中,RETNet成功地重建了高分辨率 (HG) 图像.
- 废弃实验证实了纹理和结构特征提取模块的重大影响.
- 对比实验证明了RETNet在无噪声和噪声条件下的稳定性,优于现有方法.
结论:
- 通过从LG输入中重建HG图像,RETNet有效地提高了MPI空间分辨率.
- 拟议的网络实现了高清图像重建,同时减轻了高功耗和噪声的问题.
- 模拟,幻影和体内结果验证了RETNet在MPI分辨率增强方面的卓越性能和稳定性.
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