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双通道端到端网络与先前知识嵌入,以提高磁粒子成像的空间分辨率

Jiaxuan Wen¹, Yu An², Lizhi Shao¹

¹CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China; School of Artificial Intelligence, The University of Chinese Academy of Sciences, Beijing, China.

Computers in biology and medicine

|June 23, 2024

概括

这项研究引入了一种用于磁粒子成像 (MPI) 的新型深度学习方法,可以提高图像分辨率而不会降低信号质量. 这种新方法提高了低梯度MPI扫描中的空间分辨率,提供了更好的生物医学成像能力.

科学领域:

医疗成像医学成像
生物医学工程生物医学工程
人工智能的人工智能

背景情况:

磁粒子成像 (MPI) 是一种具有高灵敏度和透深度的有前途的非侵入性成像技术.
空间分辨率和信号噪声比 (SNR) 是MPI的关键性能指标,受选择场 (SF) 梯度的影响.
目前用于MPI分辨率增强的深度学习 (DL) 方法经常忽略物理模糊,限制可解释性和突破性潜力.

研究的目的:

开发一种DL方法,在低梯度条件下提高MPI分辨率,而不损害SNR.
建立低梯度和高梯度MPI图像之间的有效隐藏映射.
将MPI物理过程的先前知识整合到DL框架中.

主要方法:

提出了一个双通道端到端网络,并为MPI (DENPK-MPI) 嵌入了先前的知识.
将MPI点差函数 (PSF) 集成到DL模型中.
通过模拟,幻影实验和体内MPI数据验证了该方法.

主要成果:

在低梯度MPI图像中,DENPK-MPI显著提高了空间分辨率.
该方法实现了在半最大 (FWHM) 的全宽度下降14.8%-23.8%.
与其他DL方法相比,图像重建的准确性高出18.2%-27.3%,而不会牺牲SNR.

结论:

拟议的DENPK-MPI方法有效地提高了MPI空间分辨率,而不影响SNR.
将MPI的先验知识纳入DL模型为提高成像性能提供了一个有希望的方向.
这种方法有可能增强MPI的生物医学应用.

关键词:

深度学习是一种深度学习.图像消除模糊的方法磁性颗粒成像技术磁粒子成像技术之前的知识之前的知识空间分辨率的空间分辨率

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