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专注于加速MRI成像的非刚性图像注册

Aya Ghoul, Jiazhen Pan, Andreas Lingg

IEEE transactions on medical imaging

|August 1, 2024

概括

这项研究提出了一个注意力意识的深度学习框架,用于在加速磁共振成像 (MRI) 中准确的运动估计. 该方法提高了运动补偿重建质量,即使在高加速度系数下也是如此.

科学领域:

医疗成像医学成像
人工智能的人工智能
生物医学工程生物医学工程

背景情况:

运动工件会降低磁共振成像 (MRI) 质量,特别是在高加速度因子时.
准确的运动估计对于快速,高质量的运动补偿MRI重建至关重要.
目前的方法在加速MRI下低样本造成的工件中扎.

研究的目的:

引入一个注意力意识的深度学习框架,用于在全样本和加速MRI中进行非刚性对联注册.
为了实现高加速度因子的准确运动估计,以改善MRI重建.
为了评估框架在不同采样轨迹和运动类型的性能.

主要方法:

开发了一个注意力意识深度学习框架,利用本地视觉表示和基于变压器的模块.
提取多个分辨率的相似性地图,并包含远程上下文信息.
结合了局部和全球依赖性,同时进行粗细运动估计.

主要成果:

该框架实现了对心脏 (高达16x) 和呼吸道 (高达30x) 运动的可靠和一致的运动场.
与现有方法相比,在运动补偿重建中显示出优越的定性和定量图像质量.
在各种数据集上得到验证,包括从101名患者和62名健康受试者的全样本和加速心脏和胸部MRI.

结论:

拟议的注意力意识深度学习框架使加速MRI的准确运动估计成为可能.
它显著提高了运动补偿MRI重建的质量,克服了低样本文物带来的挑战.
该方法在各种加速度因子和采样轨迹中显示出稳定性.

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