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一个基于模型的MR参数映射网络,能够应对收购设置的重大变化.

Qiqi Lu¹, Jialong Li¹, Zifeng Lian¹

¹School of Biomedical Engineering, Southern Medical University, Guangzhou 510000, China; Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510000, China; Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education & Guangdong-Hong Kong Joint Laboratory for Psychiatric Disorders, Southern Medical University, Guangzhou 510000, China; Department of Radiology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde, Foshan), Foshan 528000, China.

Medical image analysis

|March 30, 2024

概括

此摘要是机器生成的。

本研究介绍了MMPM-Net,这是一种用于强大的磁共振 (MR) 参数映射的新型深度学习方法. 它准确地估计了定量参数图,即使在不同的扫描设置下,也超过了现有的方法.

关键词:

深度学习是一种深度学习.磁共振成像技术磁共振成像技术进行参数映射.规范化规范化规范化

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科学领域:

医疗成像医学成像
人工智能的人工智能
量化MRI是指数量化的MRI.

背景情况:

深度学习在磁共振 (MR) 参数映射 (MPM) 中表现出色,但通常需要特定的获取设置.
在现实世界中,MRI扫描在各中心,扫描仪和研究中存在很大差异.
存在对深度学习MPM方法的需求,这些MPM方法对这些获取变量具有稳定性.

研究的目的:

开发一个强大的深度学习模型,用于定量MR参数映射,适应不同的获取设置.
为了解决对扫描协议变化敏感的当前MPM方法的局限性.

主要方法:

开发了MMPM-Net,这是一个基于模型的深度网络,将深度学习denoiser集成到MPM的非线性反转问题中.
利用乘数的交替方向方法 (ADMM) 来解决优化问题并将其展开到网络架构中.
整合了一个数据保真组件,以处理采集参数的变化.

主要成果:

在R2和R1映射数据集上,MMPM-Net表现出强大的性能,采集设置存在显著差异.
定性和定量实验表明,MMPM-Net的性能优于最先进的MR参数映射方法.
该方法有效地处理获取参数的变化,这是实际MR成像中的一个关键挑战.

结论:

MMPM-Net 提供了一种强大而通用的解决方案,用于在各种采集设置中进行定量MR参数映射.
拟议的方法通过适应协议的可变性,提高了深度学习在临床MRI成像中的适用性.
这项工作为基于深度学习的MPM在实践中更加可靠和广泛使用铺平了道路.