基于Kalman滤波的ECT图像重建算法

由于测量环境等因素的影响,实测数据往往带有噪声。为此,应用Kalman滤波算法进行电容层析成像（ ECT）系统图像重建,在归一化电容值中加入不同程度的噪声信号,利用噪声的统计特性进行测量值滤波,最终得到图像灰度值。在三种流型下与Landweber迭代算法进行了仿真比较,并在ECT实验装置上进行了静态实验。仿真与实验结果表明：Kalman滤波去噪效果优于Landweber迭代算法。     

平面式电容传感器阵列测量复合材料技术研究

基于复合材料及其内部不同损伤具有不同介电特性,采用平面式电容传感器阵列实现可视化无损检测。论述了系统的构成和测量原理,分析了影响测量效果的因素,并设计了信号转换和采集处理电路。基于简单的激励测量模式和共轭梯度算法,对比分析了仿真与实验结果。实验结果验证了基于电容敏感机理的平面式电容传感器阵列对复合材料内部缺陷检测的可行性和有效性。     

ARSlice: Head-Mounted Display Augmented with Dynamic Tracking and Projection

Computed tomography (CT) generates cross-sectional images of the body. Visualizing CT images has been a challenging problem. The emergence of the augmented and virtual reality technology has provided promising solutions. However, existing solutions suffer from tethered display or wireless transmission latency. In this paper, we present ARSlice, a proof-of-concept prototype that can visualize CT images in an untethered manner without wireless transmission latency. Our ARSlice prototype consists of two parts, the user end and the projector end. By employing dynamic tracking and projection, the projector end can track the user-end equipment and project CT images onto it in real time. The user-end equipment is responsible for displaying these CT images into the 3D space. Its main feature is that the user-end equipment is a pure optical device with light weight, low cost, and no energy consumption. Our experiments demonstrate that our ARSlice prototype provides part of six degrees of freedom for the user, and a high frame rate. By interactively visualizing CT images into the 3D space, our ARSlice prototype can help untrained users better understand that CT images are slices of a body.     

The Role of the Choroid in Stargardt Disease

Stargardt disease is the commonest juvenile macular dystrophy. It is caused by genetic mutations in the ABCA4 gene. Diagnosis is not always straightforward, and various phenocopies exist. Late-onset disease can be misdiagnosed with age-related macular disease. A correct diagnosis is particularly critical because of emergent gene therapies. Stargardt disease is known to affect retinal pigment epithelium and photoreceptors. Many studies have also highlighted the importance of the choroid in the diagnosis, pathophysiology, and progression of the disease. The choroid is in an integral relationship with the retinal pigment epithelium and photoreceptors, and its possible involvement during the disease should be considered. The purpose of this review is to analyze the current diagnostic tools for choroidal evaluation and the extrapolation of useful data for ophthalmologists and researchers studying the disease.     

电阻层析成像系统全电极模型仿真研究

在电阻层析成像系统中,激励模式和电极数目的设计直接影响到整个系统。为了提高重建图像质量,更好地指导ERT系统设计,对敏感场进行深入的分析是必要的。通过电磁场有限元仿真软件COMSOL,构建ERT的电极模型,通过对空场和离散介质场域的仿真研究,分析了各个因素对敏感场的影响,并对四种典型流型进行了图像重建。仿真实验表明,COMSOL图像重建效果满意,为图像重建的研究提供了新的思路。     

近似最优正则化参数方法在电容成像图像重建中的应用

将基于近似最优正则化参数的Tikhonov方法应用于电容成像(Electrical capacitance tomography，ECT)图像重建以解决其中存在的病态性问题，并利用几种典型分布对该方法进行仿真测试。数值结果表明，在先验知识满足的条件下，近似最优参数法所找到的正则化参数是对最优正则化参数的较合理近似。在重建结果方面，基于近似最优参数的Tikhonov方法在不同的介电常数分布下与目前普遍采用的线性反投影算法(Linear back projection，LBP)各有优势。结果表明，该方法尚不能完全取代LBP算法，但能在一定程度上弥补LBP算法的不足。     

Microwave tomography for breast cancer detection on Cell broadband engine processors

Microwave tomography (MT) is a safe screening modality that can be used for breast cancer detection. The technique uses the dielectric property contrasts between different breast tissues at microwave frequencies to determine the existence of abnormalities. Our proposed MT approach is an iterative process that involves two algorithms: Finite-Difference Time-Domain (FDTD) and Genetic Algorithm (GA). It is a compute intensive problem: (i) the number of iterations can be quite large to detect small tumors; (ii) many fine-grained computations and discretizations of the object under screening are required for accuracy. In our earlier work, we developed a parallel algorithm for microwave tomography on CPU-based homogeneous, multi-core, distributed memory machines. The performance improvement was limited due to communication and synchronization latencies inherent in the algorithm. In this paper, we exploit the parallelism of microwave tomography on the Cell BE processor. Since FDTD is a numerical technique with regular memory accesses, intensive floating point operations and SIMD type operations, the algorithm can be efficiently mapped on the Cell processor achieving significant performance. The initial implementation of FDTD on Cell BE with 8 SPEs is 2.9 times faster than an eight node shared memory machine and 1.45 times faster than an eight node distributed memory machine. In this work, we modify the FDTD algorithm by overlapping computations with communications during asynchronous DMA transfers. The modified algorithm also orchestrates the computations to fully use data between DMA transfers to increase the computation-to-communication ratio. We see 54% improvement on 8 SPEs (27.9% on 1 SPE) for the modified FDTD in comparison to our original FDTD algorithm on Cell BE. We further reduce the synchronization latency between GA and FDTD by using mechanisms such as double buffering. We also propose a performance prediction model based on DMA transfers, number of instructions and operations, the processor frequency and DMA bandwidth. We show that the execution time from our prediction model is comparable (within 0.5 s difference) with the execution time of the experimental results on one SPE.     

层析成像技术在网络属性估算中的应用研究

网络层析成像技术在网络属性估算中的应用主要分为网络拓扑判定和链路属性估算两个重要应用.主要讨论通过对网络上的主机进行单播的测量来获得网络的逻辑拓扑及链路属性,并提出进行网络属性估算的新方案.首先介绍基于延时的"三明治"测量方案和相关网络拓扑判定算法,随后通过实验对网络拓扑判定的系统聚类树算法进行了分析,在此基础上提出进行网络属性估算的新方案.     

肺腺癌CT影像分子分型研究进展

研究表明在明确驱动基因后进行特异性靶向治疗,肺癌患者的中位生存期显著延长。而除高通量测序技术和荧光原位杂交等分子生物学技术外,影像基因组学的出现,也为肺腺癌分子分型预测提供了一种无创的新方法。本文对肺腺癌计算机断层扫描(computed tomography, CT)影像分子分型的研究进展进行综述。首先,介绍肺腺癌分子分型的研究背景及肺腺癌主要的基因突变类型;然后,重点介绍两种主要的研究方法,即CT语义特征与肺腺癌分子亚型的相关性分析和基于机器学习的肺腺癌分子分型预测模型;最后,总结了该领域现阶段面临的主要问题,并对未来的研究方向做出展望。肺腺癌CT影像分子分型研究已经取得了一定成果,但仍存在很多问题。相关性分析与基于影像组学的预测模型研究由于样本各异且受过多人为干预,导致研究结果差异大,甚至有部分文献得到的结论截然相反。而基于深度学习的预测模型研究采用端到端的神经网络模型,人为参与极少,降低了研究难度,但尚处于起步阶段,构建的模型大多相对简单,远不能达到临床应用标准。今后的研究应聚焦于结合多种医学图像构建肺腺癌分子分型的大样本深度学习预测模型,同时结合临床信息、语义特征及影像组学特征...     

网络编码在拓扑推断中的应用研究

针对现有的基于线性网络编码的网络拓扑推断算法中拓扑错误推断率较高、节点资源消耗大等问题,提出了一种改进的网络拓扑推断解决方案。在无链路丢包的情况下,提出了组合包测量方法,不仅适合二叉树类型的网络,也适合于存在节点度数大于3的中间节点的树状网络拓扑;在存在链路丢包的情况下,提出了快速发包算法,避免了网络中间节点和探测包帧结构设计上的额外开销。仿真结果表明,该算法具有更广泛的适用范围,具有更严谨的推断过程,同时能够在不增加网络节点运行负担的情况下降低错误推断率。