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基于变压器的动脉旋转标记输液MRI消噪

Muhammad Nadeem Cheema¹, Lei Zhang¹, Anam Nazir¹

¹Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine.

The Visual computer

|August 25, 2025

概括

这项研究引入了一种新的混合U-Net和Swin变压器 (HUST) 方法,用于消除动脉旋转标记 (ASL) perfusion MRI 图像. HUST显著提高图像质量,并保持纹理,使得数据获取更快,而不会损害准确度.

科学领域:

医学成像
医学中的人工智能
神经成像

背景情况:

动脉脊柱标记 (ASL) 输液MRI量化脑血流 (CBF) 的非侵入性方法.
由于数据有限,ASLMRI的信号噪声比较低,图像质量具有挑战性.
现有的卷积神经网络 (CNN) 无声化方法可能会失去图像纹理和强度的可变性.

研究的目的:

为ASL CBF图像开发一种先进的消噪方法.
解决目前基于CNN的无声化技术的局限性.
改进ASL CBF图像中的可视化和纹理保存.

主要方法:

提出了一种混合U-Net和Swin变压器 (HUST) 模型来消除ASL CBF的噪音.
使用U-Net作为支柱, 结合Swin变形机来取代CNN层.
通过层次结构和转移窗口的注意力,使用Swin变压器以减少参数的高效特征提取.

主要成果:

在ASL CBF图像可视化和纹理保存方面取得了实质性改进.
该方法在2D (277名受试者) 和3D (110名受试者) ASL CBF数据集上进行了训练和测试.
与三种最先进的方法相比,HUST 实现了更高的PSNR和SSIM值,无论是2D还是3D数据.

结论:

HUST有效地消除ASL CBF图像,增强可视化并保留基本图像特征.
该方法可以在不影响CBF量化质量的情况下缩短数据采集时间.
HUST代表了ASL输液MRI的重大进步,提供了卓越的图像质量和效率.

关键词:

动脉旋转标记 (ASL)大脑血流 (CBF)深度学习图像可视化变压器没有噪音

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Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...