FBP算法中的VVBP张量及其在低剂量CT重建中的应用

IF 8.9 1区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS IEEE Transactions on Medical Imaging Pub Date : 2020-03-01 DOI:10.1109/TMI.2019.2935187
X. Tao, Hua Zhang, Yongbo Wang, G. Yan, D. Zeng, Wufan Chen, Jianhua Ma
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引用次数: 16

摘要

几十年来,商用x射线计算机断层扫描(CT)扫描仪一直使用滤波反投影(FBP)算法进行图像重建。然而,对低辐射剂量的渴望已经将FBP算法推向了极限。以往的研究通过对正弦图进行预处理、修改斜坡滤波器或对重建图像进行后处理来改善FBP的结果。本文重点研究了FBP算法中逐视图叠加的反向投影(VVBP-Tensor)的分析和处理。我们分析的一个关键挑战在于每个反向投影切片的径向结构。为了克服这个困难,在其${z}$方向(投影视图的方向)上对vvbp -张量引入了排序操作。结果表明,经过排序后的张量包含了与物体相似的结构,并且张量的不同切片中的结构具有相关性。然后,我们分析了vvbp张量的性质,包括结构自相似性、张量稀疏性和噪声统计。考虑到这些特性,我们开发了一种使用张量奇异值分解(称为VVBP-tSVD)的算法来对低mas CT成像的vvbp -张量进行降噪。实验采用物理幻影和不同mAs水平的临床患者数据进行。结果表明,在正弦图预处理、图像后处理和迭代重建等不同重建方案下,VVBP-tSVD都优于所有竞争方法。结果表明,vvbp张量是提高FBP重建质量的合适处理目标,所提出的VVBP-tSVD是一种有效的低mas CT成像降噪算法。这一初步工作可能为回顾和反思FBP算法提供一个启发式的视角。
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VVBP-Tensor in the FBP Algorithm: Its Properties and Application in Low-Dose CT Reconstruction
For decades, commercial X-ray computed tomography (CT) scanners have been using the filtered backprojection (FBP) algorithm for image reconstruction. However, the desire for lower radiation doses has pushed the FBP algorithm to its limit. Previous studies have made significant efforts to improve the results of FBP through preprocessing the sinogram, modifying the ramp filter, or postprocessing the reconstructed images. In this paper, we focus on analyzing and processing the stacked view-by-view backprojections (named VVBP-Tensor) in the FBP algorithm. A key challenge for our analysis lies in the radial structures in each backprojection slice. To overcome this difficulty, a sorting operation was introduced to the VVBP-Tensor in its ${z}$ direction (the direction of the projection views). The results show that, after sorting, the tensor contains structures that are similar to those of the object, and structures in different slices of the tensor are correlated. We then analyzed the properties of the VVBP-Tensor, including structural self-similarity, tensor sparsity, and noise statistics. Considering these properties, we have developed an algorithm using the tensor singular value decomposition (named VVBP-tSVD) to denoise the VVBP-Tensor for low-mAs CT imaging. Experiments were conducted using a physical phantom and clinical patient data with different mAs levels. The results demonstrate that the VVBP-tSVD is superior to all competing methods under different reconstruction schemes, including sinogram preprocessing, image postprocessing, and iterative reconstruction. We conclude that the VVBP-Tensor is a suitable processing target for improving the quality of FBP reconstruction, and the proposed VVBP-tSVD is an effective algorithm for noise reduction in low-mAs CT imaging. This preliminary work might provide a heuristic perspective for reviewing and rethinking the FBP algorithm.
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来源期刊
IEEE Transactions on Medical Imaging
IEEE Transactions on Medical Imaging 医学-成像科学与照相技术
CiteScore
21.80
自引率
5.70%
发文量
637
审稿时长
5.6 months
期刊介绍: The IEEE Transactions on Medical Imaging (T-MI) is a journal that welcomes the submission of manuscripts focusing on various aspects of medical imaging. The journal encourages the exploration of body structure, morphology, and function through different imaging techniques, including ultrasound, X-rays, magnetic resonance, radionuclides, microwaves, and optical methods. It also promotes contributions related to cell and molecular imaging, as well as all forms of microscopy. T-MI publishes original research papers that cover a wide range of topics, including but not limited to novel acquisition techniques, medical image processing and analysis, visualization and performance, pattern recognition, machine learning, and other related methods. The journal particularly encourages highly technical studies that offer new perspectives. By emphasizing the unification of medicine, biology, and imaging, T-MI seeks to bridge the gap between instrumentation, hardware, software, mathematics, physics, biology, and medicine by introducing new analysis methods. While the journal welcomes strong application papers that describe novel methods, it directs papers that focus solely on important applications using medically adopted or well-established methods without significant innovation in methodology to other journals. T-MI is indexed in Pubmed® and Medline®, which are products of the United States National Library of Medicine.
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