在心脏CT图像中检测小金属工件和inpainting
Trevor McKeown1, H Michael Gach2,3,4, Yao Hao2
1Medical Physics Program, Duke University.
ArXiv
|October 14, 2024
概括
这项研究开发了深度学习模型,以减少心脏CT扫描中由可植入心脏转换器-除器 (ICD) 引起的金属工件. 这些模型可以准确地检测和染色文物,改善放射治疗患者的心室细分和运动分析.
科学领域:
- 医疗成像医学成像
- 辐射疗法 辐射疗法
- 人工智能的人工智能
背景情况:
- 在CT扫描中量化心脏运动,以测量立体心律失常的放射疗法是具有挑战性的,因为植入式心脏转换器-除器 (ICD) 的金属工件.
- 标准的CT金属工件减少工具是不够的,先进的技术需要原始数据,这往往是无法预先重建的图像.
- 需要新的方法来减少现有CT图像中的文物,以恢复丢失的解剖信息.
研究的目的:
- 开发一种自动化的方法来检测心脏CT扫描中的金属文物.
- 将受文物影响的区域涂上解剖学上一致的结构和价值.
主要方法:
- 开发了一个2D U-Net深度学习 (DL) 模型,用于自动对金属工件进行细分,使用来自12名患者的ECG-gated 4DCT扫描.
- 创建了一个由592个CTs组成的合成数据集,通过将细分工件添加到没有工件的图像中.
- 训练了一个3D图像,在DL模型中绘制DL模型,以重建文物损坏的卷,并评估其在合成和患者数据上的性能.
主要成果:
- 文物检测模型获得了0.958 ± 0.008.8的子得分.
- 彩绘模型重建的图像与基本真相几乎相同 (结构相似度指数为0.988±0.012).
- 改善了心室细分的平均表面子分数从0.684 ± 0.247到0.964 ± 0.067以及从3.4 ± 3.9毫米减少的豪斯多夫距离到0.7 ± 0.7毫米.
结论:
- 成功开发了两种深度学习模型,用于检测和绘制心脏CT图像中的金属工件.
- 这些模型增强了心室细分和心脏运动分析在文物损坏的CT扫描中.
- 训练有素的模型和示例数据在GitHub上公开提供.
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