基于先验信息的肺部电阻抗成像算法

电阻抗层析成像 (EIT) 系统逆问题求解的欠定性是导致重构图像分辨率低的主要原因之一.结合先验信息,改善逆问题的不适定性,提高算法鲁棒性是提高成像质量可行的办法.本研究基于Tikhonov正则化算法,结合肺部组织结构、器官电导率分布参数以及肺部呼吸动态结构变化等先验信息,构建正则化矩阵,进行EIT图像重构.研究结果表明,结合先验信息的Tikhonov正则化图像重构算法减小了成像相对误差,改进了图像质量,EIT用于区域性肺部通气变化的检测和监护是可行的.     

电阻抗成像中变差正则化算法的研究

我们将变差函数作为罚函数项引入到电阻抗逆问题的正则化重构算法中，形成了变差正则化算法，与常用Tikhonov正则化算法不同，该算法在重构中不仅能够保证逆问题解适定，同时还能够很好地提高重构图像的对比度和锐度，使所得重构图像目标区域与背景区域之间边界更加清晰，定位更加准确，进而使EIT重构图像与医学图像更加吻合，对EIT成像技术早日走上实用化有积极意义。     

脑阻抗断层成像的图像重构算法

脑部一些疾病和脑功能活动期间常伴随产生脑组织电阻抗的变化，利用电阻抗断层成像技术(EIT)可以对大脑疾病和脑功能活动进行临床诊断和连续监护。本主要讨论了脑EIT成像的计算模型和图像重构算法，并对几类典型算法进行了评价。     

电阻抗断层成像算法应用综述

电阻抗断层成像（EIT）技术是非侵入性、无损伤、低成本的功能成像技术，在生物医学、地质勘探等领域有着广泛的应用，文章重点对EIT的关键技术，即从基础理论出发对EIT正问题和EIT现阶段广泛应用的重构算法进行介绍，同时对EIT与CT融合成像技术的研究方向进行介绍。EIT技术日趋成熟，具有广泛的应有前景，对EIT技术在生物医学应用领域做概述，并对EIT技术发展提出一些建议。     

脑阻抗断层成像的图像重构算法

脑部一些疾病和脑功能活动期间常伴随产生脑组织电阻抗的变化,利用电阻抗断层成像技术(EIT)可以对大脑疾病和脑功能活动进行临床诊断和连续监护。本文主要讨论了脑EIT成像的计算模型和图像重构算法,并对几类典型算法进行了评价。     

一个基于物理模型的生物电阻抗断层成像系统的研究

电阻抗断层成像(electrical impedance tomography,EIT)是以目标体内电阻抗的分布或变化为成像对象的一种新型成像技术.它具有简便、无创、造价较低的优势,并具有功能成像及动态图像监护的优点.我们设计了基于物理模型的EIT系统,该系统由硬件系统和软件系统组成.硬件系统由32电极、物理模型、恒流驱动模块、信号检测模块、数据采集模块、驱动及测量模式设置模块、多组电源模块以及计算机等组成.我们主要在图像重构模式和算法方面进行了研究,实现了等位线反投影动态重构算法法并提出了广义逆动态重构算法.应用该系统在物理模型上得到了较好的结果.     

一种基于频谱约束的多频动态电阻抗断层成像算法

本文旨在提出一种基于频谱约束的多频动态电阻抗断层成像(EIT)算法。在已知成像域内各组分电导率频谱的情况下,通过重构独立于频率的参数——体积分数变化,同时利用多个激励频率下的测量电压差重构一帧时差图像,从而大大增加测量数据量以改善逆问题的病态性。数值仿真实验显示,该算法较传统阻尼最小二乘算法具有更小的图像伪影,且在低信噪比情形下具有更小的位置误差和形变误差。本研究有望为动态EIT提供一种有效利用多频信息的方法,并为在已知各组分电导率频谱情况下的动态EIT发展提供一个新思路。     

一种电阻抗图像与CT图像融合方法研究

研究一种电阻抗功能图像与CT结构图像相融合的方法,获得既能反映电阻抗分布又能反映组织结构的医学图像.以人体呼吸过程的电阻抗图像及胸部CT图像的融合为例,首先采用disk算子对CT图像滤波,再用canny算子提取CT图像轮廓,将提取后的结构图像构建EIT正问题模型,并进行正问题求解,更新灵敏度矩阵,且基于共轭梯度算法重建EIT图像,然后用小波算法将EIT图像与CT结构图像进行融合.在融合图像中,电阻抗功能变化信息在结构图像中凸显出来,可实现电阻抗变化区域的初步定位.研究结果表明,电阻抗功能图像与CT结构图像的融合有效、可行.本研究为实现电阻抗成像技术与其他成像技术的信息互补、进一步发挥EIT功能成像技术的优势奠定基础.     

结合生成对抗网络的电阻抗断层成像技术在肺通气监测中的应用

电阻抗断层成像（EIT）技术在肺通气监测和区域性肺功能检测中发挥着重要作用。然而,EIT算法固有的病态特性导致从含有噪声的电压数据中求解电导率时存在明显偏差,难以获得准确的电导率变化分布图像以及清晰的边界轮廓。为了提高EIT在肺通气监测中的成像质量,本文提出将EIT算法与深度学习算法相结合的方法。首先,引用优化因子对卡尔曼滤波算法进行修正并将吉洪诺夫（Tikhonov）正则化引入算法的状态空间表达式,以获得初始肺部重建图像;其次将初始成像结果输入生成对抗网络模型,以重构出精确的肺部轮廓。仿真实验结果表明,该方法生成的肺部图像边界清晰,对噪声具有更强的鲁棒性,基本实现了可视化的效果,可为计算机断层扫描等影像的诊断提供参考意义。     

基于GMRES和Tikhonov正则化的生物电阻抗图像重建算法

电阻抗层析成像(Electrical impedance tomography,EIT)是利用被测物体场内部电导率分布不均匀性,通过边界注入电流,测量边界电压变化,重构被测场内电导率分布图像。由于EIT测量数据有限,场域存在严重的非线性,导致问题的欠定性。我们介绍了一种新的组合算法,利用GMRES算法生成Krylov子空间,并结合Tik-honov正则化方法进行图像重建。该算法不仅改善了实时性,而且提高了成像质量及鲁棒性。     

Figures of merit for comparing reconstruction algorithms with a volume-imaging PET scanner

For volume-imaging PET scanners, no septa are used to maximize the sensitivity by collecting events oblique to the scanner axis. We answer two questions: (i) how does the performance of an image reconstruction algorithm for a volume-imaging PET scanner depend on its general dimensions? and (ii) at what point is a three-dimensional (3D) reconstruction algorithm needed for a volume-imaging scanner, as the axial extent is increased? A 3D reconstruction algorithm will accurately incorporate the oblique events in a reconstruction of the original source distribution. From simulations of an existing volume PET scanner with a maximum axial acceptance angle (+/-alpha) of alpha = 9 degrees, however, we show that the single-slice rebinning algorithm is a good compromise between sensitivity, speed, and accuracy when compared to standard two-dimensional reconstruction (alpha = 1 degrees), and a 3D reconstruction with alpha = 9 degrees. We also show with simulations that a new scanner with alpha = 27 degrees requires 3D reconstruction in order to achieve maximum sensitivity without unacceptable losses in accuracy. Measurements of scanner performance are based on a series of figures of merit that characterize image quality and quantitative accuracy measured from a set of simulated test phantoms.     

Tilted plane Feldkamp type reconstruction algorithm for spiral cone beam CT

An approximate image reconstruction method for spiral cone beam computed tomography (CT), called tilted plane Feldkamp type reconstruction algorithm (TPFR), is presented in this paper, which extends Feldkamp cone beam reconstruction algorithm to deal with its inaccuracy and artifact problems caused by large cone angle. This is done by tilting the reconstructing planes to minimize the cone angle and optimally fit the spiral segment of the source. The tilted plane image reconstruction requires reforming the three-dimensional projection data set for the tilted plane and application of Feldkamp algorithm to the reformed data set. Analytical and computational results can show that the image reconstruction performance of the proposed TPFR algorithm is superior to that of the Feldkamp reconstruction algorithm in the image quality, volume coverage speed, maximum achievable pitch value, and slice sensitivity profiles. Moreover, it provides more accurate image reconstruction than the existing two-dimensional reconstruction algorithms.     

DLT算法中象平面误差对三维重构的影响

ＤＬＴ算法是利用平面图象进行三维重构的广泛采用的基本算法。本文针对平面图象处理中难以避免的象坐标误差,讨论了各坐标误差对三维重构坐标的影响,由此分析了三维重构坐标对各象坐标误差的敏感程度,并进行了数值模拟实验。     

Fast GPU-based computation of the sensitivity matrix for a PET list-mode OSEM algorithm

During the last decade, studies have shown that 3D list-mode ordered-subset expectation-maximization (LM-OSEM) algorithms for positron emission tomography (PET) reconstruction could be effectively computed and considerably accelerated by graphics processing unit (GPU) devices. However, most of these studies rely on pre-calculated sensitivity matrices. In many cases, the time required to compute this matrix can be longer than the reconstruction time itself. In fact, the relatively long time required for the calculation of the patient-specific sensitivity matrix is considered as one of the main obstacle in introducing a list-mode PET reconstruction algorithm for routine clinical use. The objective of this work is to accelerate a fully 3D LM-OSEM algorithm, including the calculation of the sensitivity matrix that accounts for the patient-specific attenuation and normalization corrections. For this purpose, sensitivity matrix calculations and list-mode OSEM reconstructions were implemented on GPUs, using the geometry of a commercial PET system. The system matrices were built on-the-fly by using an approach with multiple rays per detector pair. The reconstructions were performed for a volume of 188×188×57 voxels of 2×2×3.15 mm(3) and for another volume of 144×144×57 voxels of 4×4×3.15 mm(3). The time to compute the sensitivity matrix for the 188×188×57 array was 9 s while the LM-OSEM algorithm performed at a rate of 1.1 millions of events per second. For the 144×144×57 array, the respective numbers are 8 s for the sensitivity matrix and 0.8 million of events per second for the LM-OSEM step. This work lets envision fast reconstructions for advanced PET applications such as real time dynamic studies and parametric image reconstructions.     

A new iterative algorithm to reconstruct the refractive index

The latest developments in x-ray imaging are associated with techniques based on the phase contrast. However, the image reconstruction procedures demand significant improvements of the traditional methods, and/or new algorithms have to be introduced to take advantage of the high contrast and sensitivity of the new experimental techniques. In this letter, an improved iterative reconstruction algorithm based on the maximum likelihood expectation maximization technique is presented and discussed in order to reconstruct the distribution of the refractive index from data collected by an analyzer-based imaging setup. The technique considered probes the partial derivative of the refractive index with respect to an axis lying in the meridional plane and perpendicular to the propagation direction. Computer simulations confirm the reliability of the proposed algorithm. In addition, the comparison between an analytical reconstruction algorithm and the iterative method has been also discussed together with the convergent characteristic of this latter algorithm. Finally, we will show how the proposed algorithm may be applied to reconstruct the distribution of the refractive index of an epoxy cylinder containing small air bubbles of about 300 micro of diameter.     

Three-dimensional PET reconstruction with time-of-flight measurement.

The interest in fully three-dimensional image reconstruction, especially in positron emission tomography (PET) has significantly increased for the last few years. Taking into account the cross-plane gamma rays in a three-dimensional reconstruction algorithm improves the sensitivity. At LETI, our specialty in PET is the time-of-flight (TOF) measurement. Thus, we present in this article two reconstruction techniques for 3D TOF PET. The first is a backprojection-convolution algorithm. Due to the redundancy in the 3D data set, there exists an infinite number of filters. As Defrise and co-workers did for classical tomography, we established a general condition that characterizes the filters and propose an algorithm with a factorizable filter. However, this first technique requires an acquisition system with revolution symmetry. Thus, we present a second one which is adapted to a detection geometry with a small number of angular positions. It consists of a multi-image deconvolution algorithm with Wiener filter.     

Implementation of an accelerated iterative algorithm for cone-beam SPECT

In this paper we describe the implementation of an accelerated iterative reconstruction algorithm (AIRA) for cone-beam (CB) projections using a single circular orbit in single-photon-emission computed tomography (SPECT). This algorithm is a modified maximum-likelihood-expectation-maximization (ML-EM) algorithm and several approaches have been used to accelerate the reconstruction process. These approaches include: (i) the use of ordered subsets; (ii) the use of active areas and volumes; and (iii) the storing in memory of the transition vector for a given ray (during the forward projection step). This algorithm, which compensates for collimator geometric sensitivity variation as a function of position and makes uniform attenuation corrections has been evaluated using experimentally acquired phantom data. The results demonstrate a two-orders-of-magnitude decrease of the computational time of this algorithm over the conventional ML-EM algorithm with similar convergence properties.     

MSCT轴向分辨力和图像噪声影响因素分析

目的:探讨多排螺旋CT（MSCT）轴向分辨力或断层灵敏度曲线（SSP）和图像噪声的影响因素。方法:使用临床常 用的腹部扫描模式,采用不同直径的模板,重建层厚、螺距、电压（kV）、重建算法等扫面参数,进行MSCT扫描,对不同参数 对断层图像SSP和图像噪声的影响进行统计学分析。结果:当螺距和准直器宽度保持不变,不同层厚和重建算法得到的 SSP的半高宽FWHM值基本保持不变（P>0.05）;当重建算法和准直器宽度保持不变,不同螺距和层厚得到的SSP的半高 宽FWHM值基本保持不变（P>0.05）;不同准直器宽度,同螺距和层厚得到的SSP 的半高宽FWHM值基本保持不变（P> 0.05）;随着层厚、mAs增加,图像噪声减小（P<0.05）;随着kV增加,图像噪声随之减小,不同重建算法下图像噪声存在明显 差异（P<0.05）。结论:卷积重建算法、螺距和准直器宽度对SSP的影响很小,螺距对图像噪声影响很小,而层厚、重建算 法、mAs、kV对图像噪声影响大。层厚、mAs、kV增大,图像噪声减小;重建算法分辨率越高,图像噪声越大。     

Theoretical evaluation of accuracy in position and size of brain activity obtained by near-infrared topography

Near-infrared (NIR) topography can obtain a topographical distribution of the activated region in the brain cortex. Near-infrared light is strongly scattered in the head, and the volume of tissue sampled by a source-detector pair on the head surface is broadly distributed in the brain. This scattering effect results in poor resolution and contrast in the topographic image of the brain activity. In this study, a one-dimensional distribution of absorption change in a head model is calculated by mapping and reconstruction methods to evaluate the effect of the image reconstruction algorithm and the interval of measurement points for topographic imaging on the accuracy of the topographic image. The light propagation in the head model is predicted by Monte Carlo simulation to obtain the spatial sensitivity profile for a source-detector pair. The measurement points are one-dimensionally arranged on the surface of the model, and the distance between adjacent measurement points is varied from 4 mm to 28 mm. Small intervals of the measurement points improve the topographic image calculated by both the mapping and reconstruction methods. In the conventional mapping method, the limit of the spatial resolution depends upon the interval of the measurement points and spatial sensitivity profile for source-detector pairs. The reconstruction method has advantages over the mapping method which improve the results of one-dimensional analysis when the interval of measurement points is less than 12 mm. The effect of overlapping of spatial sensitivity profiles indicates that the reconstruction method may be effective to improve the spatial resolution of a two-dimensional reconstruction of topographic image obtained with larger interval of measurement points. Near-infrared topography with the reconstruction method potentially obtains an accurate distribution of absorption change in the brain even if the size of absorption change is less than 10 mm.     

Advanced single-slice rebinning in cone-beam spiral CT

To achieve higher volume coverage at improved z-resolution in computed tomography (CT), systems with a large number of detector rows are demanded. However, handling an increased number of detector rows, as compared to today's four-slice scanners, requires to accounting for the cone geometry of the beams. Many so-called cone-beam reconstruction algorithms have been proposed during the last decade. None met all the requirements of the medical spiral cone-beam CT in regard to the need for high image quality, low patient dose and low reconstruction times. We therefore propose an approximate cone-beam algorithm which uses virtual reconstruction planes tilted to optimally fit 180 degrees spiral segments, i.e., the advanced single-slice rebinning (ASSR) algorithm. Our algorithm is a modification of the single-slice rebinning algorithm proposed by Noo et al. [Phys. Med. Biol. 44, 561-570 (1999)] since we use tilted reconstruction slices instead of transaxial slices to approximate the spiral path. Theoretical considerations as well as the reconstruction of simulated phantom data in comparison to the gold standard 180 degrees LI (single-slice spiral CT) were carried out. Image artifacts, z-resolution as well as noise levels were evaluated for all simulated scanners. Even for a high number of detector rows the artifact level in the reconstructed images remains comparable to that of 180 degrees LI. Multiplanar reformations of the Defrise phantom show none of the typical cone-beam artifacts usually appearing when going to larger cone angles. Image noise as well as the shape of the respective slice sensitivity profiles are equivalent to the single-slice spiral reconstruction, z-resolution is slightly decreased. The ASSR has the potential to become a practical tool for medical spiral cone-beam CT. Its computational complexity lies in the order of standard single-slice CT and it allows to use available 2D backprojection hardware.