基于水平集的光学层析图像重建

为解决重建图像中异常区域边界不规则这一问题,提出一种基于水平集的光学层析图像重建方法.该方法在背景区域光学参数已知情况下,可以使重建异常目标区域的位置更加接近其真实位置,且实现了异常区域位置和光学参数的同时重建.采用扩散方程作为光子传输模型,基于有限元方法实现正向模型的数值求解,用零水平集曲线表示异常区域边界.实验结果...     

一种基于共轭梯度最优化技术的三维时域扩散光学层析方法

扩散光学层析(DOT)中的图像重建是一个面向大参数集的非线性最优化问题,其标准求解方法为牛顿类迭代法,需要对整个Jacobian矩阵进行构建、求逆和存贮,这对大规模的三维问题是不可行的,为此常采用基于逐行线性逆策略的非创伤性填充(ART)技术,图像质量受到严重制约.采用共轭梯度算法,直接求解非线性目标函数梯度.可避免对Jacobian矩阵的操作,为有效降低步长因子求解引起的附加计算量,采用一维不精确搜索算法,通过对双非均匀目标体的平板模型进行模拟成像,与代数重建算法结果进行比较,表明共轭梯度法的重建质量、收敛速度和收敛性都优于ART算法.     

扩散拉曼层析成像方法的可行性模拟研究

扩散拉曼层析成像(DRT)结合了拉曼光谱的分子指纹特性和扩散光学层析成像的深度分辨能力,是一种新型的无标记在体三维光学成像技术。提出一种基于蒙特卡罗光子输运模型的DRT可行性研究方法,该方法应用互易原理建立了快速有效的拉曼数据模拟器,可对实验条件下不同信噪比的拉曼测量数据进行客观模拟,同时结合扩散荧光层析线性成像原理,实现多波段下薄层组织的拉曼光谱分析。通过开展178个波段下5种物质成分的薄层组织模型的模拟重建研究,表明线性DRT图像重建方法能有效构建多种成分浓度的三维空间分布。     

面向乳腺肿瘤诊断的时域扩散荧光―光学层析成像系统

为了解决传统乳腺扩散光学层析成像可靠性低的问题,设计了一套基于多通道时间相关单光子计数的时域扩散荧光—光学层析成像系统.该系统采用32根同轴光纤均匀分布于组织体表面,作类似X射线层析工作方式的同层扫描,由此获得多角度下的时间分辨投影.通过测量不同仿体,应用相应的迭代图像重建算法,获得了可靠的重建结果.研究表明,该系统工作可靠,是进行乳腺肿瘤早期诊断研究的理想模式之一.     

空间扩展目标对准误差图像的高分辨复原

大气湍流、光子噪声和光学跟踪系统对准误差严重降低了空间目标观测图像的分辨率.根据最大似然估计原理,建立了提高目标图像分辨率的多帧盲反卷积算法,用共轭梯度优化方法从目标记录图像估计出原始目标函数和点扩散函数.运用低通平滑滤波技术在算法迭代过程中逐步完成对噪声的抑制.模拟实验数据和实际图像的复原结果表明,论文建立的盲反卷积算法有效地克服了大气湍流、光子噪声和光学系统对准误差,提高了目标图像的分辨率,复原目标图像的分辨率达到了光学衍射极限的水平.     

基于体积补偿的荧光分子断层图像重建算法

在荧光分子断层图像重建过程中,对于生物组织体内具有相同光学参数、相同深度,但不同体积的荧光团,重建出的荧光团光学参数存在较大误差。提出了一种基于体积补偿的荧光分子断层图像重建算法。该算法利用改进的迭代自组织数据分析技术(ISODATA),设计了初始聚类中心选择和初始期望聚类个数确定的方法,对预迭代的重建图像进行聚类分析。根据聚类得出的荧光团体积大小,设计了一种基于对数运算的体积权值系数计算方法,对重建的荧光光学参数进行非线性补偿。仿真实验结果表明,非线性补偿方法能够较好地修正因荧光团体积大小不一而造成的图像重建误差,显著提高重建图像的质量。     

面向吲哚菁绿药代动力学成像的动态实验系统

为实现小动物吲哚菁绿(ICG)药代动力学成像,设计了一套高灵敏度扩散荧光层析成像系统。该系统采用了基于离散光纤耦合光子计数采集的仿计算机断层成像扫描方式,在保障高灵敏度和大动态范围测量的前提下,可有效提高系统的空间采样分辨率;同时采用了基于光开关切换四通道串-并混和的测量模式,兼顾了测量时间分辨率和系统性价比之间的平衡。为验证实验系统的有效性,设计了可模拟小动物体内ICG代谢规律的动态仿体,并结合实验室开发的荧光剂药代动力学直接重建算法,实现了ICG代谢速率的重建。实验结果表明,该系统具有较高的空间分辨率、灵敏度和量化性。     

基于迭代优化展开的Cherenkov激发的荧光扫描成像重建算法

Cherenkov激发的荧光扫描成像（CELSI）是一种新型的光学成像技术，为监测体内恶性肿瘤的生物学特性提供了一种手段。为提高CELSI图像重建质量，本文提出了一种基于迭代优化展开的深度学习图像重建算法——ADMM-Net。在该算法中，交替方向乘子法（ADMM）与卷积神经网络（CNN）相结合组成一个深度网络，网络中的所有参数通过端到端训练进行学习。实验结果表明：该算法可以有效提升重建图像的质量。当网络层数为5时，该算法重建的单荧光目标图像的平均峰值信噪比和结构相似性值分别可达到33.75 dB和0.86。该算法不仅可以分辨出边沿距离最小为2 mm的双荧光目标，而且在多荧光目标和不同荧光量子产额比率下表现出了良好的泛化能力。     

基于多测量矢量压缩感知的超分辨荧光显微成像研究

在超分辨荧光显微成像技术中,单分子定位显微方法是被广泛应用的技术之一。根据荧光显微成像原理构造多测量矢量压缩感知模型（Multiple Measurement Vector-Compressed Sensing, MMV-CS）,并采用多重稀疏贝叶斯学习算法进行求解,来实现超分辨荧光图像重建。分析了有效像元大小、荧光分子生成的光子数和背景信号泊松化噪声对重建结果的影响,以及在图像进行分块处理时算法运行时间的分析。模拟和实验计算分析表明,当点扩展函数的标准差在160 nm时,有效像元大小在120、160、200 nm能取得较好的重构效果,而在60 nm时效果较差。探测器收集的光子数越多,重构效果越好,随着背景信号光子数增加时,离得越近的样品结构越不能分辨。在同样的分块处理情况下,MMV-CS比同伦算法(L1-Homotopy, L1-H）和凸优化算法(CVX)分别快一个数量级和三个数量级,因此,在研究三维超分辨荧光显微成像时,MMV-CS算法在运行时间上具有更大的优势。     

基于深度学习理论的光学分子成像图像重建方法

光学分子成像图像重建是当前的研究重点,由于传统光学分子成像图像重建方法存在重建误差,效果不理想等缺陷,为了获得理想的光学分子成像图像重建效果,提出了基于深度学习理论的光学分子成像图像重建方法.首先分析光学分子成像图像重建工作原理,找到导致光学分子成像图像重建质量差的因素,然后采集光学分子成像图像,选择卷积神经网络算法进行光学分子成像的图像重建.实验结果显示:采用本方法进行峰值信噪比(PSNR)范围为34.16～38.96,结构相似性(SSIM)范围为0.854 9～0.980 8,均远远大于常规方法数值,表明提出方法图像重建质量较高,充分证明提出方法性能较好.应用价值相似性指标更大,图像重建质量更高,则提出方法的图像重建性能更佳.     

Imaging the body with diffuse optical tomography

Diffuse optical tomography (DOT) is an ongoing medical imaging modality in which tissue is illuminated by near-infrared light from an array of sources, the multiply-scattered light which emerges is observed with an array of detectors, and then a model of the propagation physics is used to infer the localized optical properties of the illuminated tissue. The three primary absorbers at these wavelengths, water and both oxygenated and deoxygenated hemoglobin, all have relatively weak absorption. This fortuitous fact provides a spectral window through which we can attempt to localize absorption (primarily by the two forms of hemoglobin) and scattering in the tissue. The most important current applications of DOT are detecting tumors in the breast and imaging the brain. We introduce the basic idea of DOT and review the history of optical methods in medicine as relevant to the development of DOT. We then detail the concept of DOT, including a review of the tissue's optical properties, modes of operation for DOT, and the challenges which the development of DOT must overcome. The basics of modelling the DOT forward problem and some critical issues among the numerous implementations that have been investigated for the DOT inverse problem, with an emphasis on signal processing. We summarize with some specific results as examples of the current state of DOT research     

Diffuser-aided diffuse optical imaging for breast tumor: a feasibility study based on time-resolved three-dimensional Monte Carlo modeling

This study proposed diffuser-aided diffuse optical imaging (DADOI) as a new approach to improve the performance of the conventional diffuse optical tomography (DOT) approach for breast imaging. The 3-D breast model for Monte Carlo simulation is remodeled from clinical MRI image. The modified Beer-Lambert's law is adopted with the DADOI approach to substitute the complex algorithms of inverse problem for mapping of spatial distribution, and the depth information is obtained based on the time-of-flight estimation. The simulation results demonstrate that the time-resolved Monte Carlo method can be capable of performing source-detector separations analysis. The dynamics of photon migration with various source-detector separations are analyzed for the characterization of breast tissue and estimation of optode arrangement. The source-detector separations should be less than 4 cm for breast imaging in DOT system. Meanwhile, the feasibility of DADOI was manifested in this study. In the results, DADOI approach can provide better imaging contrast and faster imaging than conventional DOT measurement. The DADOI approach possesses great potential to detect the breast tumor in early stage and chemotherapy monitoring that implies a good feasibility for clinical application.     

Compressive diffuse optical tomography: noniterative exact reconstruction using joint sparsity

Diffuse optical tomography (DOT) is a sensitive and relatively low cost imaging modality that reconstructs optical properties of a highly scattering medium. However, due to the diffusive nature of light propagation, the problem is severely ill-conditioned and highly nonlinear. Even though nonlinear iterative methods have been commonly used, they are computationally expensive especially for three dimensional imaging geometry. Recently, compressed sensing theory has provided a systematic understanding of high resolution reconstruction of sparse objects in many imaging problems; hence, the goal of this paper is to extend the theory to the diffuse optical tomography problem. The main contributions of this paper are to formulate the imaging problem as a joint sparse recovery problem in a compressive sensing framework and to propose a novel noniterative and exact inversion algorithm that achieves the l(0) optimality as the rank of measurement increases to the unknown sparsity level. The algorithm is based on the recently discovered generalized MUSIC criterion, which exploits the advantages of both compressive sensing and array signal processing. A theoretical criterion for optimizing the imaging geometry is provided, and simulation results confirm that the new algorithm outperforms the existing algorithms and reliably reconstructs the optical inhomogeneities when we assume that the optical background is known to a reasonable accuracy.     

Overcoming ill-posedness of diffuse optical tomography in steady-state domain

In recent decades, diffuse optical tomography (DOT) has drawn more and more interest in molecular imaging because of its advantage of large penetration depth in optical image technology. However, ill-posedness problems have dramatically limited this application technique. In this paper, a new method to remove the ill-posedness of DOT is introduced. With a rotating steady-state domain experiment system, by increasing experimental data that could be obtained from any visual angle, four contrast experiments were simulated. It was proved that when the sum of the experiment data is larger than that of the unknown optical coefficient of phantom,ill-posedness would be reduced and the quality of the reconstructed image could be improved.     

高速128通道小动物多光谱光声断层成像系统

为了实现小动物光声断层信号的高速采集和实时高质量图像的重建,采用了覆盖角度为270°的128阵元弧形聚焦超声换能器、4个32通道的NI公司数据采集模块和可调谐脉冲激光器以及正则化优化的基于模型的光声断层重建算法。结果表明,系统的空间分辨率可以达到180μm;此系统可以在1ms内完成光声断层数据的采集,在40s以内获得高质量的重建图像。该系统可以用于开展小动物在体的多光谱光声断层成像实验研究。     

Discrete tomography in medical imaging

Discrete tomography (DT) deals with the reconstruction of a function from its projections, when the function has a known discrete range. The knowledge of the discrete range, possibly together with some prior information, can significantly reduce the number of projections required for a high-quality reconstruction. The reconstruction methods used in DT applications are usually based on some formulation as an optimization problem. This paper presents methods and results of DT based on problems of angiography, emission tomography, and electron microscopy (EM).     

A general class of preconditioners for statistical iterativereconstruction of emission computed tomography

A major drawback of statistical iterative image reconstruction for emission computed tomography is its high computational cost. The ill-posed nature of tomography leads to slow convergence for standard gradient-based iterative approaches such as the steepest descent or the conjugate gradient algorithm. Here, new theory and methods for a class of preconditioners are developed for accelerating the convergence rate of iterative reconstruction. To demonstrate the potential of this class of preconditioners, a preconditioned conjugate gradient (PCG) iterative algorithm for weighted least squares reconstruction (WLS) was formulated for emission tomography. Using simulated positron emission tomography (PET) data of the Hoffman brain phantom, it was shown that the convergence rate of the PCG can reduce the number of iterations of the standard conjugate gradient algorithm by a factor of 2-8 times depending on the convergence criterion     

Half-time image reconstruction in thermoacoustic tomography

Thermoacoustic tomography (TAT) is an emerging imaging technique with great potential for a wide range of biomedical imaging applications. In this paper, we propose and investigate reconstruction approaches for TAT that are based on the half-time reflectivity tomography paradigm. We reveal that half-time reconstruction approaches permit for the explicit control of statistically complementary information that can result in the optimal reduction of image variances. We also show that half-time reconstruction approaches can mitigate image artifacts due to heterogeneous acoustic properties of an object. Reconstructed images and numerical results produced from simulated and experimental TAT measurement data are employed to demonstrate these effects.     

基于梯度树的光学层析图像重建方法

光学相干层析成像技术是一种新型成像方法,在生物医学和材料等许多领域有广泛的应用.介绍了一种基于梯度树的迭代图像重建方法,讨论了输运模型下含空洞状区域的图像重建方法.证明了基于输运模型的图像重建能克服扩散方程在非散射区域的重建弊端,准确地重建光学层析图像.     

光学干涉层析仪的设计与信息处理研究

本文在光学层析术（OCT）的数学模型多维Radon变换的基础上,设计基于F－P干涉仪的多方向采样的旋转扫描光学干涉CT仪。分析了OCT问题和重建中5个关键参数对重建精度的影响,以对实验仪器设计和实验方案拟定精度指标提供指导。同时还研究了在对超音速风洞中复杂流场数据严重不完全的情况下的重建,提出了正交窗口小角度范围采样和充分利用先验知识的多信息融合重建方案。