论文标题
在非理想条件下改善CT重建的全球限制
A Global Constraint to Improve CT Reconstruction Under Non-Ideal Conditions
论文作者
论文摘要
背景和目标：对医学成像应用的强烈需求导致CT重建问题的流行。研究人员提出了多种约束，以解决CT重建中的理想因素，例如稀疏视图，有限角度和低剂量条件。这些约束中的大多数（例如总变化）是局部约束，重点是像素与其邻居之间的关系。在本文中，我们提出了一个新的约束，利用CT图像的全球先验来大大降低条纹伪像并进一步提高重建精度。方法：人体的CT图像包含有限数量的不同类型的组织，因此CT图像中的像素可以根据其相应类型将几组分组为几组。在我们的工作中，我们专注于单个像素的组成分类，并将其用作全球先验，这与大多数当前约束所使用的先验不同。我们根据重建过程中的灰度水平进行分割，并迫使同一组的像素具有相似的灰度水平。结果：我们对Shepp-Logan Phantom和来自不同基准的两个真实CT图像的实验表明，所提出的约束可以帮助传统的局部约束进一步改善在稀疏视图，有限角度和低剂量条件下的重建结果。结论：与关注本地先验的大多数当前约束不同，我们提出的约束仅利用CT图像的全局先验。在这种情况下，我们提出的约束可以与大多数本地限制合作，并显着提高重建质量。此外，提出的约束也有进一步改进的潜力，因为可以使用一些更精致的方法（例如与神经网络相关的语义分割算法）进行组成分类。
Background and Objective: The strong demand for medical imaging applications leads to the popularity of the CT reconstruction problem. Researchers proposed multiple constraints to tackle none ideal factors in CT reconstruction such as sparse-view, limited-angle, and low-dose conditions. Most of these constraints such as total variation are local constraints focusing on the relationship between a pixel and its neighbors. In this paper, we propose a new constraint utilizing the global prior of CT images to greatly reduce the streak artifacts and further improve the reconstruction accuracy. Methods: A CT image of the human body contains a limited number of different types of tissues, so pixels in CT images can be grouped into several groups according to their corresponding types. In our work, we focus on the composition classification for individual pixels and utilize it as a global prior, which differs from priors utilized by most current constraints. We propose segmenting pixels based on their gray levels during the reconstruction process, and forcing pixels in the same group to have similar gray levels. Results: Our experiments on the Shepp-Logan phantom and two real CT images from different benchmarks show that the proposed constraint can help the conventional local constraints further improve the reconstruction results under sparse-view, limited-angle, and low-dose conditions. Conclusions: Different from most current constraints focusing on the local prior, our proposed constraint only utilizes the global prior of CT images. In that case, our proposed constraint can collaborate with most local constraints and improve the reconstruction quality significantly. Furthermore, the proposed constraint also has the potential for further improvement, as the composition classification can be done with some more delicate methods, such as neural network related semantic segmentation algorithms.
