从FLAIR卷中合成扩散权重的MRI标量图,使用生成对抗网络

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Updated: Jul 18, 2025

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Karissa Chan^1,2, Pejman Jabehdar Maralani³, Alan R Moody³

¹Electrical, Computer and Biomedical Engineering Department, Toronto Metropolitan University, Toronto, ON, Canada.

Frontiers in neuroinformatics

|August 21, 2023

概括

通过使用pix2pix模型从FLAIR MRI生成合成扩散加权成像 (DWI) 标量图,可以补充数据集. 这种方法显示了改进数据预处理管道的潜力.

科学领域:

医疗成像医学成像
人工智能的人工智能
神经科学是一个神经科学.

背景情况:

扩散权重成像 (DWI) 的采集和预处理是耗时的.
从流体减弱反转恢复 (FLAIR) MRI 中生成合成DWI标量图可以增强数据集.

研究的目的:

设计和比较基于生成对抗网络 (GAN) 的图像翻译模型,用于从FLAIR MRI生成DWI标量图.
评估pix2pix,修改后的CycleGANs和一个卷积自编码器在合成DWI分数异构 (FA) 和平均扩散率 (MD) 地图中的性能.

主要方法:

四个基于GAN的模型 (pix2pix,两个修改后的CycleGAN,卷积自编码器) 被训练和测试.
使用了420个多中心的FLAIR和DWI卷.
产生的图像使用峰值信号与噪声比率 (PSNR),结构相似性 (SSIM),直方图相似性 (Hist-KL) 和平均平方误差 (MSE) 来评估.

主要成果:

Pix2pix表现出卓越的性能,在MD和FA生成方面实现了高PSNR,SSIM和低MSE.
新的Hist-KL度量有效地捕获了细节.
Pix2pix显示了灰色和白色物质中真实和合成FA值之间的显著相关性.

结论:

与其他模型相比,Pix2pix模型实现了组织结构和细节的结构相似性明显更好.
合成DWI图像可以补充临床数据集,并通过绕过或纠正注册问题来改善预处理.

关键词:

循环GANAN是一个循环.酒后驾驶酒后驾驶酒后驾驶一个闪耀的火焰这是一把手枪.图像合成图像合成像素2pixxxx 在线观看翻译翻译翻译翻译翻译翻译

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