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与生成对抗网络和形状先验的阴影图画
Emilien Valat1, Katayoun Farrahi2, Thomas Blumensath3
1Cambridge Image Analysis Group, Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, University of Cambridge, Wilberforce Rd., Cambridge CB3 0WA, UK.
Tomography (Ann Arbor, Mich.)
|June 27, 2023
概括
这项研究引入了一种用于X射线计算机断层扫描 (CT) 图像重建的新方法. 它使用生成对抗网络来推断缺失的X射线测量,显著减少图像文物,并在有限数据场景中提高图像质量.
科学领域:
- 医疗成像医学成像
- 计算成像技术的成像
- 图像重建 图像的重建
背景情况:
- 射线计算机断层扫描 (CT) 从X射线吸收概况 (sinograms) 中重建图像.
- 图像重建是一个错误的反向问题,特别是没有足够的X射线测量,导致文物.
- 有限角度CT扫描,其中某些方向的数据缺失,构成了重大挑战.
研究的目的:
- 开发一种新的方法来减少有限角度X射线CT中的图像工件.
- 使用关于物体形状的先前信息推断缺失的阴影图数据.
- 为了提高图像质量,在实质性,连续缺失断层扫描测量的场景中.
主要方法:
- 使用生成对抗网络 (GAN) 将有限的采购数据与形状先验结合起来.
- 该方法侧重于推断连续缺失的X射线测量,与以前的技术不同.
- 该方法与最先进的sinogram inpainting方法进行了评估.
主要成果:
- 与现有技术相比,拟议的方法始终提高了图像质量.
- 显示了显著的7dB峰值信号噪声比 (PSNR) 改进.
- 基于GAN的方法有效地减少了由有限的断层图像数据引起的图像工件.
结论:
- 使用GAN绘制的形状前导向阴影图是有限角度CT的有效方法.
- 该方法提供了一个强大的解决方案,用于从不完整的数据集中重建高质量的CT图像.
- 这种方法推动了CT图像重建领域的发展,特别是对于具有挑战性的采集几何形状.
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