在儿科计算机断层扫描 (CT) 中减少辐射剂量,使用深 convolutional 神经网络 denoising.
K K Horst1, Z Zhou2, N C Hull1
1Pediatric Radiology Division, Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
Clinical radiology
|November 7, 2024
概括
深 convolutional神经网络 (CNN) denoising显著提高了图像质量在儿科CT扫描,即使在减少的辐射剂量. 这种先进的技术提供了比传统的代重建更好的结果,用于减少儿童的辐射剂量.
科学领域:
- 医疗成像医学成像
- 放射学 放射学是一门学科.
- 人工智能在医学中的应用
背景情况:
- 儿科非对比胸部计算机断层扫描 (CT) 对于诊断慢性咳等疾病至关重要.
- 在儿科CT中降低辐射剂量对于最大限度地降低长期健康风险至关重要.
- 传统的代重建 (IR) 方法可以引入噪音工件,影响图像质量.
研究的目的:
- 评估深 convolutional 神经网络 (CNN) 的有效性,用于图像 denoising 在儿科非对比胸部CT.
- 为了比较常规剂量 (RD) 和模拟20%剂量 (TD) 的CT扫描之间的图像质量和噪声水平,带有和没有CNN无声化.
- 为了评估CNN断的性能与用于减少辐射剂量的标准代重建 (IR) 相比.
主要方法:
- 包括40名为慢性咳而接受非对比胸部CT的儿科患者.
- CT图像在常规剂量 (RD) 和模拟20%剂量 (TD) 中采用噪声插入方法获得.
- 一个深度CNN模型被训练并应用到CT图像中.
- 三名儿科放射科医生使用利克尔特尺度在四个条件下评估了图像质量和噪声工件:RD+IR,RD+CNN,TD+IR和TD+CNN.
主要成果:
- 与RD+IR相比,RD+CNN和TD+CNN都显示出明显更高的主观图像质量.
- 与IR相比,CNN拒绝导致RD和TD协议的主观噪声人工物得分明显较低.
- 有效辐射剂量从0.46 mSv (RD) 减少到0.09 mSv (TD).
- 在所有图像重建方法中观察到优异的读取器内可靠性和适度的读取器间可靠性.
结论:
- 深度卷积神经网络 (CNN) 无声化优于代重建 (IR) 在提高儿科CT图像质量方面.
- 在儿童胸部CT检查中,CNN denoising有效地减少了噪音工件,使得显著的辐射剂量减少成为可能.
- 这种人工智能驱动的方法有望实现更安全,更有效的儿科CT成像.
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