Efficient parallel implementation of iterative reconstruction algorithms for electron tomography

Electron tomography (ET) combines electron microscopy and the principles of tomographic imaging in order to reconstruct the three-dimensional structure of complex biological specimens at molecular resolution. Weighted back-projection (WBP) has long been the method of choice since the reconstructions are very fast. It is well known that iterative methods produce better images, but at a very costly time penalty. In this work, it is shown that efficient parallel implementations of iterative methods, based primarily on data decomposition, can speed up such methods to an extent that they become viable alternatives to WBP. Precomputation of the coefficient matrix has also turned out to be important to substantially improve the performance regardless of the number of processors used. Matrix precomputation has made it possible to speed up the block-iterative component averaging (BICAV) algorithm, which has been studied before in the context of computerized tomography (CT) and ET, by a factor of more than 3.7. Component-averaged row projections (CARP) is a recently introduced block-parallel algorithm, which was shown to be a robust method for solving sparse systems arising from partial differential equations. It is shown that this algorithm is also suitable for single-axis ET, and is advantageous over BICAV both in terms of runtime and image quality. The experiments were carried out on several datasets of ET of various sizes, using the blob model for representing the reconstructed object.     

基于截面含气率的气／液两相流相关速度测量

基于双截面电阻层析成像技术进行气／液两相流的相关速度测试研究,分析了相关速度测量原理,提出了基于截面含气率的相关测速方法。通过插值增加上游、下游双截面含气率的采样数据,利用独立开发的软件系统具有的数据存储与回放功能对基于截面含气率的相关测速方法进行评定实验。实验结果表明：该方法测速精度较高,且稳定可靠。     

基于神经网络和D-S证据理论辨识两相流流型

提出一种基于电容层析成像(ECT)系统、神经网络和证据理论辨识两相流流型的方法。这种方法采用神经网络与D-S证据理论相结合的方法来辨识两相流流型,并对两相流的几种常见流型进行了辨识。仿真实验结果表明:此种方法在两相流流型辨识中具有较高的判别精度,为两相流流型辨识提供了一种有效的手段。     

A multi-scale template method for shape detection with bio-medical applications

In this paper we present a novel methodology based on non-parametric deformable prototype templates for reconstructing the outline of a shape from a degraded image. Our method is versatile and fast and has the potential to provide an automatic procedure for classifying pathologies. We test our approach on synthetic and real data from a variety of medical and biological applications. In these studies it is important to reconstruct accurately the shape of the object under investigation from very noisy data. Here we assume that we have some prior knowledge about the object outline represented by a prototype shape. Our procedure deforms this shape by means of non-affine transformations and the contour is reconstructed by minimizing a newly developed objective function that depends on the transformation parameters. We introduce an iterative template deformation procedure in which the scale of the deformation decreases as the algorithm proceeds. We compare our results with those from a Gaussian Mixture Model segmentation and two state-of-the-art Level Set methods. This comparison shows that the proposed procedure performs consistently well on both real and simulated data. As a by-product we develop a new filter that recovers the connectivity of a shape.

基于消光断层法的喷雾分布不均匀度测量技术

燃油喷嘴喷雾分布不均匀度作为衡量喷嘴喷雾性能的重要参数之一,直接影响发动机的燃烧效率以及污染排放。为了验证消光断层法激光式分布器(Statistical Extinction Tomography Scan Optical Patternator,SETscan)在喷雾分布不均匀度方面的测量能力,利用离心式喷嘴在不同燃油工况条件下开展燃油喷嘴雾化性能实验,并与传统机械式测量方法进行了对比。结果表明,相比于传统机械式测量方法,激光式测量方法稳定性更好、实验效率更高、实验数据更加直观,具有更高的时间和空间分辨率,对激光遮挡率为10%~90%的燃油喷雾能够得出合理有效的浓度分布实验结果,同时由于激光式测量方法属于非接触测量,不会受到喷雾本身特性和安装位置等因素的影响。     

融合隐向量对齐和Swin Transformer的OCTA血管分割

目的 光学相干断层扫描血管造影（optical coherence tomography angiography,OCTA）是一种非侵入式的新兴技术,越来越多地应用于视网膜血管成像。与传统眼底彩照相比,OCTA技术能够显示黄斑周围的微血管信息,在视网膜血管成像邻域具有显著优势。临床实践中,医生可以通过OCTA图像观察不同层的血管结构,并通过分析血管结构的变化来判断是否存在相关疾病。大量研究表明,血管结构的任何异常变化通常都意味着存在某种眼科疾病。因此,对OCTA图像中的视网膜血管结构进行自动分割提取,对众多眼部相关疾病量化分析和临床决策具有重大意义。然而,OCTA图像存在视网膜血管结构复杂、图像整体对比度低等问题,给自动分割带来极大挑战。为此,提出了一种新颖的融合隐向量对齐和Swin Transformer的视网膜血管结构的分割方法,能够实现血管结构的精准分割。方法 以ResU-Net为主干网络,通过Swin Transformer编码器获取丰富的血管特征信息。此外,设计了一种基于隐向量的特征对齐损失函数,能够在隐空间层次对网络进行优化,提升分割性能。结果 在3个OCTA图像数据集上的...     

InPBi薄膜材料的结构性能和光学性能研究

室温下,InPBi表现出强而宽的光致发光光谱,其宽光谱特性来自于材料中的PIn反位深能级和与Bi相关的深能级。该特性使得InPBi有希望应用于制备光学相干层析扫描系统中的超辐射光源。文章利用透射电子显微镜和三维原子探针研究了InPBi薄膜材料的结构性能,发现Bi原子在InPBi薄膜中的分布极不均匀,在InPBi/InP界面出现了Bi的富集区,从该区域沿[001]方向出现了Bi的纳米面,此纳米面位于(110)平面上。这种Bi原子的富集分布阻碍了PIn反位参与的载流子复合过程,对InPBi的光学性能有显著的影响。研究结果可为制造光学相干层析扫描系统的超辐射发光二极管提供一定的理论基础。     

基于高密度子网格光线追迹的三维发射层析重建算法

三维(three- dimensional,3D) 发射层析技术(emission computerized tomography,ECT) 是一种简单、高效且准确的燃 烧场3D成像与检测技术,其中权重矩阵的计算 精度决定了层析重建的精度和质量。本文研究了一种基于高密度子网格光线追迹的权重矩阵 计算方法, 将被测区域划分为密度更高的子网格,并根据相机成像模型实现光线追迹,以确定离散网格 对投影像素 的权重因子。数值模拟和燃烧火焰重建实验表明该算法具有较高的精度和计算效率。该研究 对于3D发射层析技术的实用化具有重要的理论意义。