论文标题
从不完整的层析成像数据中重建的功能重建
Feature reconstruction from incomplete tomographic data without detour
论文作者
论文摘要
在本文中，我们考虑了不完整的X射线CT数据重建功能重建问题。例如，由于上下文医学成像的剂量减少，发生了此类问题。由于来自不完整数据的图像重建是一个严重的问题，因此重建的图像可能患有特征性的人工制品或缺失的特征，并且显着复杂化了随后的图像处理任务（例如，边缘检测或分段）。在本文中，我们引入了一个新颖的框架，以直接从CT数据中重建卷积图像特征，而无需计算重建FIR。在我们的框架内，我们使用非线性（变异）正则化方法，可以适应各种功能重建任务以及几种有限的数据情况。在我们的数值实验中，我们考虑了来自角度不足数据的边缘重建的几种实例，并表明我们的方法能够在这种情况下可靠地重建特征图。
In this paper, we consider the problem of feature reconstruction from incomplete x-ray CT data. Such problems occurs, e.g., as a result of dose reduction in the context medical imaging. Since image reconstruction from incomplete data is a severely ill-posed problem, the reconstructed images may suffer from characteristic artefacts or missing features, and significantly complicate subsequent image processing tasks (e.g., edge detection or segmentation). In this paper, we introduce a novel framework for the robust reconstruction of convolutional image features directly from CT data, without the need of computing a reconstruction firs. Within our framework we use non-linear (variational) regularization methods that can be adapted to a variety of feature reconstruction tasks and to several limited data situations . In our numerical experiments, we consider several instances of edge reconstructions from angularly undersampled data and show that our approach is able to reliably reconstruct feature maps in this case.
