CASIA OpenIR  > 中国科学院分子影像重点实验室
基于光源邻域信息的激发荧光断层重建算法研究
孟慧
Subtype博士
Thesis Advisor田捷
2020-05-24
Degree Grantor中国科学院大学
Place of Conferral中国科学院大学
Degree Discipline计算机应用技术
Keyword光学分子影像 激发荧光断层成像 非局部全变分正则先验 自适应高 斯权重拉普拉斯正则先验 k 近邻局部连接网络
Abstract

分子影像技术是一种结合了分子生物学、化学、物理学、放射医学以及计算
机科学的医学影像技术。该影像技术可以在组织水平、细胞水平乃至亚细胞水平
上对机体的生理活动进行成像。相比于传统的医学影像技术,分子影像技术具有
高灵敏度、高特异性的优势。目前,分子影像已被广泛地应用在生物医学研究领
域。光学分子影像是分子影像的重要组成部分。它以光信号作为成像媒介,可以
在细胞和分子层面上对特定的生理活动进行无创的定性和定量研究。近年来,光
学分子影像技术得到了快速的发展,并被成功地应用到了预临床研究和临床实验
中。
激发荧光断层成像是一种重要的光学分子影像技术。相比于二维的激发荧光
成像技术,该影像技术可以获取荧光探针在生物体内的三维分布信息,进而实现
特定标记物的三维可视化。激发荧光断层成像首先利用高灵敏度探测器采集生物
体表面的荧光信息,然后利用重建方法得到荧光光源在生物体内的分布信息。传
统的重建方法依赖于光子传输模型的构建,而模型描述偏差影响了激发荧光断层
重建的精度。此外,激发荧光断层重建的逆向问题具有严重的病态性和欠定性,
这限制了激发荧光断层重建的形态学重建质量和重建速度的提升。
本文针对如上问题,开展了基于光源邻域信息的激发荧光断层重建算法的研
究。该研究主要分为两个部分:首先,在传统的基于光子传输模型的重建策略下,
利用光强的邻域分布先验知识, 设计了逆向问题中的正则项;其次, 在前期研究
工作的基础上,提出了基于机器学习的重建策略, 通过数据驱动的方式训练重建
模型, 避免了光子传输模型的构建和逆向问题的求解。 本文的主要工作和贡献如
下:
 提出了基于非局部全变分正则的激发荧光断层重建方法,提高了激发荧
光断层成像的形态学重建质量。该算法参考荧光光源在生物体内的光强
分布规律,即空间点间的光强差异与距离呈正相关,进而设计了非局部
全变分正则。该先验正则将空间点之间的物理距离转换为高斯距离,并
将高斯权重作为径向函数构建非局部全变分正则矩阵。因此,该正则矩
阵对距离较近的空间点产生较强的光强差异惩罚,而对距离较远的空间
点产生较弱的光强差异惩罚。相关的数值仿真实验和小鼠原位脑胶质瘤
模型实验的结果表明,该方法能够提供比传统重建方法更好的形态学重
建质量。
 提出了基于自适应高斯权重拉普拉斯先验正则的激发荧光断层重建方法。
该算法的先验正则改良自高斯权重拉普拉斯先验正则。此正则在空间点
的光强差异与距离呈正相关的先验假设基础上,增加了光强分布与能量
区域相关的先验假设。通过结合两个先验假设,本算法构建了基于高斯
距离和光强值的自适应高斯权重拉普拉斯正则矩阵。该正则矩阵在考虑
空间点间的物理距离的同时,还兼顾了空间点所属邻域的能量范围,进
一步减小了光源区域的荧光差异, 并且提高了光源边界处的荧光差异。
相关的数值仿真实验和小鼠原位脑胶质瘤模型实验的结果表明, 该方法
能够提高重建结果的对比噪声比,并且实现了准确的形态学重建。
 提出了基于 K 近邻局部连接网络的激发荧光断层重建方法。不同于传统
的基于光子传输模型的重建策略,该重建策略是一种基于机器学习的重
建策略。该重建策略利用神经网络从大量的样本中直接学习光子在生物
组织中传输的逆过程,避免了光子传输模型的构建和逆向问题的求解。
该重建方法的 K 近邻局部连接网络改良自全连接网络。该网络利用了空
间点的光强差异与物理距离呈正相关的先验假设,基于空间点的 K 近邻
信息设计了局部连接子网络,并将其级联在了全连接子网络之后,进而
优化了网络的形态学重建效果。此外,为了获取大量的激发荧光断层成
像样本,本重建算法利用蒙特卡洛方法仿真了若干单光源样本,并利用
样本组合的方法组装了大量的双光源样本和大光源样本。由数值仿真实
验和小鼠原位脑胶质瘤模型实验的重建结果可知,基于 K 近邻局部连接
网络的激发荧光断层重建方法能够实现准确的光源定位和形态学重建。
相比于传统方法,基于数据驱动的重建策略极大地提高了重建速度,相
应的重建时间减少了三个数量级。
 

Other Abstract

Molecular imaging is a medical imaging technology which involves
molecular biology, chemistry, physics, radiology, and computer science. It
can image the physiological activities of the organism at tissue level, cell
level, and even subcellular level. Compared with traditional medical
imaging, molecular imaging has the advantages of high sensitivity and high
specificity. Nowadays, molecular imaging has been widely used in
biomedical research. Optical molecular imaging is an important part of
molecular imaging. It takes optical signal as imaging medium, and can
conduct noninvasive, qualitative, and quantitative research on specific
physiological activities at the cellular and molecular level. In recent years,
optical molecular imaging has been greatly developed, and has been
successfully applied to preclinical research and clinical experiments.
Fluorescence molecular tomography (FMT) is an important optical
molecular imaging technology. Compared with two-dimensional
fluorescence molecular imaging, FMT can obtain the three-dimensional
distribution information of fluorescence probe in the organism, and realize
the three-dimensional visualization of certain biomarkers. In the process of
FMT imaging, the fluorescence information on the surface of organism is
first collected by high sensitivity detector, and then the distribution
information of fluorescence sources in the organism is obtained by
reconstruction method. The conventional reconstruction methods are based
on photon propagation model. The deviation of model description affects
the accuracy of FMT reconstruction. In addition, the inverse problem of
FMT reconstruction is ill-posed and under-determined, which limits the
quality and speed of morphological reconstruction in FMT.
This research focuses on FMT reconstruction method based on the
neighborhood information of fluorescence source to solve the above
problems, and can be divided into two parts. The first part depends on the
conventional photon propagation model-based reconstruction strategy. It
aims to design the regularization terms in inverse problem using the
neighborhood information of fluorescence distribution. On the basis of the
previous research, the second part proposes a machine learning-based
reconstruction strategy. This strategy utilizes data-driven method to train
the reconstruction model, and avoids building the photon propagation
model and solving the inverse problem. The major contributions of this
thesis are listed as follows:
 A FMT reconstruction method based on nonlocal total variation
regularization has been proposed to improve the morphological
imaging quality. This algorithm refers to the intensity distribution
rule of fluorescence source in the organism, i.e., the fluorescence
difference between spatial points is positively related to their
distance. Based on this assumption, this algorithm designs the
nonlocal total variation regularization. This regularization
transforms the physical distance between spatial points into
Gaussian distance, and uses Gaussian weight as radial function to
construct nonlocal total variation regularization matrix. Thus, this
matrix heavily penalizes the intensity difference between the close
pairs of points, and lightly penalizes the intensity difference between
the distant pairs of points. The results of numerical simulation
experiments and orthotopic glioma mouse model experiments
demonstrate that the proposed method achieved more accurate
morphological reconstruction than the conventional methods.
 A FMT reconstruction method based on adaptive Gaussian
weighted Laplace prior regularization has been proposed. The
prior regularization of the algorithm is improved from the Gaussian
weighted Laplace prior (GWLP) regularization. Besides the prior
assumption that the fluorescence difference of spatial points is
positively related to the corresponding distance, adaptive Gaussian
weighted Laplace prior (AGWLP) regularization assumes that the
fluorescence distribution is related to the energy region. Based on
the above two assumption, this algorithm builds an AGWLP
regularization matrix with both Gaussian distance and fluorescence
intensity. The regularization matrix not only considers the physical
distance between spatial points, but also takes into account the
energy range of neighborhood to which the spatial points belong.
The design of the regularization matrix reduces the fluorescence
difference in the fluorescence source, and improves the fluorescence
difference at the edge of the fluorescence source. The results of
numerical simulation experiments and orthotopic glioma mouse
model experiments demonstrate that the proposed method improved
the contrast-to-noise of reconstruction results, and achieved accurate
morphological reconstruction.
 A FMT reconstruction method based on K-nearest neighbor
based locally connected network has been proposed. Different
from the conventional photon propagation model-based
reconstruction methods, this algorithm is a machine learning-based
reconstruction strategy. This reconstruction strategy uses neural
network to directly learn the inverse process of photon propagation
in biological tissue, which avoids building the photon propagation
model and solving the inverse problem. K-nearest neighbor based
locally connected (KNN-LC) network is improved from fully
connected (FC) network. It utilizes the prior assumption that the
fluorescence difference between spatial points is positively related
to the physical distance, and designs locally connected (LC) subnetwork. KNN-LC network cascades a FC sub-network with a LC
sub-network, which improves the imaging quality of morphological
reconstruction. In addition, in order to obtain a large number of FMT
samples, this algorithm simulates a number of single-source samples
with Monte Carlo method, and assembles a large number of dualsource samples and big-source samples using the method of sample
assembling. The results of numerical simulation experiments and
orthotopic glioma mouse model experiments demonstrate that KNNLC network can achieve both accurate source localization and
morphological reconstruction. Compared with the conventional
methods, machine learning-based reconstruction strategy greatly
improves the reconstruction speed and reduces the reconstruction
time by three orders of magnitude.
 

Pages122
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/38573
Collection中国科学院分子影像重点实验室
Recommended Citation
GB/T 7714
孟慧. 基于光源邻域信息的激发荧光断层重建算法研究[D]. 中国科学院大学. 中国科学院大学,2020.
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