超声层析成像中多分辨率技术对断层重建的影响

IF 0.6 Q4 COMPUTER SCIENCE, THEORY & METHODS
L. T. Theu, Q. Tran, Vijender Kumar Solanki, Tatiana R. Shemeleva, Duc-Tan Tran
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引用次数: 2

摘要

基于散射理论的超声层析成像(UT)的最大优点是它能够研究小结构。DBIM是一种畸变Born迭代法。采用最近邻插值法提高了重建性能,缩短了重建时间。原始(N 1 × N 1)和密集(N 2 × N 2)网格积分区域分别在NN 1和NN 2迭代中重构。然而,选择NN 1的最佳值来获得最高的性能在之前的作品中并没有提到。如果选择不好,重建质量甚至比不使用插值时还要差。本研究提出了一种利用最近邻插值(MR-DBIM)增强UT重建的方法。相应的算法由Sleptsov网络的图形并行编程语言指定。一些重要的结果是:(1)MR-DBIM只有在(即稀疏散射域)时才有意义;(2)当Nt = Nr时,DBIM的性能最好,而当Nr = 2Nt时,MR-DBIM的性能最好;(3)当,nnn 1的值为2,当,nnn 1的值为3。图形抽象
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Influence of the multi-resolution technique on tomographic reconstruction in ultrasound tomography
The greatest advantage of scattering theory-based ultrasound tomography (UT) is its ability to investigate small structures. DBIM is the Distorted Born Iterative Method. The nearest neighbour interpolation method is used to enhance the reconstruction performance and reduce the reconstruction time. The raw (N 1 × N 1) and dense (N 2 × N 2) meshed integration areas are reconstructed in NN 1 and NN 2 iterations, respectively. However, choosing the best value of NN 1 to get the highest performance was not mentioned in previous works. If it is not well chosen, the reconstruction quality is even worse than that when using no interpolation. This study proposes a method to enhance the UT reconstruction by using the nearest neighbour interpolation (MR-DBIM). The corresponding algorithms are specified by the graphical concurrent programming language of Sleptsov nets. Some significant results are (1) the MR-DBIM is only meaningful when (i.e. sparse scattering domain); (2) the best performance is obtained in the DBIM when Nt  = Nr , but in the MR-DBIM when Nr  = 2Nt ; (3) the well-investigated value of NN 1 is 2 when and is 3 when . GRAPHICAL ABSTRACT
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来源期刊
CiteScore
2.30
自引率
0.00%
发文量
27
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