基于物理模型的生成神经网络,用于合成扫描仪和算法特定的低剂量CT检查

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Hao Gong¹, Lifeng Yu¹, Shuai Leng¹

¹Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA.

Medical physics

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概括

一个新的深度学习模型,PALETTE,准确地模拟低剂量的CT噪声,没有专有数据. 这种基于物理的网络产生真实的噪声纹理,改善CT图像分析和剂量减少研究.

科学领域:

医疗成像医学成像
人工智能的人工智能
计算科学计算科学

背景情况:

精确的低剂量CT模拟对于评估重建和剂量减少技术至关重要.
在CT图像中插入噪声的现有方法面临局限性,特别是非线性重建算法和专有制造商数据.
深度学习方法看起来有前途,但往往缺乏基于物理的指导来生成现实的噪音纹理.

研究的目的:

介绍PALETTE,一个基于物理的,基于模型的生成神经网络,用于模拟扫描仪和算法特定的低剂量CT检查.
提供投影域噪声插入的替代方案,规避制造商专有信息的需求.
为了能够有效地评估CT重建和剂量降低技术.

主要方法:

PALETTE集成了基于物理的噪声预生成,偏差预的Noise2Noisier子网络和噪声纹理合成子网络.
使用明确的空间和频域规范化来捕获噪声相关性和特征.
该模型使用虚拟和患者数据与商业代重建算法 (SAFIRE) 进行了训练和验证.

主要成果:

帕莱特精确地复制了噪声功率光谱 (NPS) 峰值频率,并表现出低平均绝对误差 (MAE).
生成的噪声纹理取决于解剖学,在局部/全球颗粒度和条纹方面是现实的,与参考水平相比,噪声水平没有显著差异.
定量评估 (SCM,SAM) 显示噪声频率分布具有很高的相似性,表现优于基线模型.

结论:

PALETTE成功模拟了使用商业非线性算法重建的低剂量CT图像的高质量图像域噪声.
该模型为低剂量CT模拟提供了可行的替代方案,当专有数据不可用时.
帕莱特促进了CT重建和剂量降低方面的先进研究.

关键词:

计算机断层扫描 (CT) 是一种计算机断层扫描.图像域噪声插入插入图像质量评估图像质量评估基于物理的深度学习.

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