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基于深度学习的数字减去血管学方法的培训,使用合成数据.

Lizhen Duan^1,2,3, Elias Eulig^1,4, Michael Knaup¹

¹Division of X-Ray Imaging and Computed Tomography, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Medical physics

|February 14, 2024

概括

这项研究引入了一种新方法,用于训练基于深度学习的数字减去血管学 (DDSA) 模型,使用合成数据,改善图像质量并帮助诊断心血管疾病.

关键词:

深度学习是一种深度学习.数字减去血管学图.光镜是指光镜.综合训练数据综合训练数据

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科学领域:

医疗成像医学成像
人工智能在医学中的应用
心血管成像 - 心血管成像

背景情况:

数字减去血管学 (DSA) 对于诊断心血管疾病 (CVD) 至关重要.
基于深度学习的DSA (DDSA) 提供剂量降低和更好的图像质量,但受制于易受伪造的临床数据.
由于数据限制,当前的DDSA模型在特定结构和无噪声图像预测方面扎.

研究的目的:

为DDSA模型培训开发一个用于生成大量合成DSA图像对的策略.
创建合成DSA目标,免受临床DSA中常见的文物和噪音的影响.

主要方法:

利用超过7,000张CT投影图像和25,000张合成血管投影图像来创建合成DSA图像对.
使用随机林登迈尔系统和CT扫描生成的血管骨.
在合成数据集上训练DDSA模型,并将性能与在临床DSA数据上训练的模型进行比较.

主要成果:

在合成数据上训练的DDSA模型的表现与在腿部,腹部和心脏成像中接受临床数据训练的模型相比或更好.
合成数据训练导致更清晰的DSA类图像,优于传统的DSA和临床数据训练模型.
定量评估显示,在合成数据上训练的模型中,高峰信号噪声比 (PSNR),结构相似度指数 (SSIM),准确度,精度和Dice分数都优越.

结论:

建议使用合成DSA图像对来训练DDSA网络的一种新方法.
这种方法可以从对比度增强的X射线图像中直接提取类似DSA的图像.
开发的方法显示出作为一种有价值的工具来帮助心血管诊断的潜力.