论文标题
在光学断层扫描中深度学习的应用不适合反问题
Applications of Deep Learning for Ill-Posed Inverse Problems Within Optical Tomography
论文作者
论文摘要
越来越多的医学成像中出现了围绕原始测量数据重建嘈杂图像的问题。在前问题是从地面真实图像中生成原始测量数据的地方，反问题是从测量数据中重建这些图像。在大多数情况下，具有医学成像，经典的逆伦敦变换，例如MRI的反傅立叶变换，可以很好地从测量的数据中恢复干净的图像。不幸的是，在X射线CT的情况下，该CT的采样非常常见，这超出了解决忠实且可用的图像所需的更多。在本文中，我们探讨了解决X射线CT反向问题的经典方法的历史，然后分析利用有监督的深度学习的最新方法的状态。最后，我们将在未来提供一些可能的研究途径。
Increasingly in medical imaging has emerged an issue surrounding the reconstruction of noisy images from raw measurement data. Where the forward problem is the generation of raw measurement data from a ground truth image, the inverse problem is the reconstruction of those images from the measurement data. In most cases with medical imaging, classical inverse Radon transforms, such as an inverse Fourier transform for MRI, work well for recovering clean images from the measured data. Unfortunately in the case of X-Ray CT, where undersampled data is very common, more than this is needed to resolve faithful and usable images. In this paper, we explore the history of classical methods for solving the inverse problem for X-Ray CT, followed by an analysis of the state of the art methods that utilize supervised deep learning. Finally, we will provide some possible avenues for research in the future.
