Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
无肿瘤区域引导的生物自发荧光断层成像重建算法研究 | |
高源 | |
2019-05 | |
页数 | 120 |
学位类型 | 博士 |
中文摘要 | 分子影像技术是一种基于信息科学,生物物理学以及生物医学等多领域理论交叉的计算机成像技术。该影像技术使用靶向性探针或生物医学试剂来标记成像目标的遗传因子、表面受体等生物化学特征,并结合一系列的生物影像获取技术和计算机信号处理方法,进而在组织水平,细胞水平,乃至亚细胞水平上获得生物体内示踪目标的空间分布。因此,分子影像技术能够为活体生物内部的肿瘤位置分布观测,病变组织生长追踪,体内药物代谢以及药效评估等生物医学研究提供更具特异性和灵敏性的成像手段。 生物自发荧光断层成像是一种重要的光学分子影像技术。该断层成像技术以肿瘤等示踪目标的特异性受体与荧光素反应产生的生物荧光作为成像信号。它通过采集由荧光信号传播到生物体表面形成的荧光光斑,进而重建得到荧光光源在生物体内的三维空间分布。传统的生物自发荧光断层成像以光子在生物体内传播过程的数理模型为基础,逆向求解该模态的影像结果。但在实际应用中,现有数理模型对实际光子传播过程的描述存在偏差,且具有严重的欠定性和病态性。这限制了逆向重建的定位精度、形态学信息以及重建鲁棒性等成像性能。此外,基于高分辨率结构模态提供肿瘤先验区域的重建方法虽然能够提升成像结果,却受限于引导模态的影像质量,无法完全体现生物自发荧光的信号特异性。 本文针对如上问题,开展了针对无肿瘤区域引导的生物自发荧光断层成像重建算法的研究。该研究主要分为两个部分:首先,在传统的以描述光子传播过程为基础的重建策略下,参考光源分布规律,设计无肿瘤区域引导的先验正则;其次,在前期研究的基础上,还提出了基于机器学习的重建策略,以数据驱动的方式构建重建模型,进而避免传统策略模型描述存在偏差的缺陷。本文的主要工作和贡献如下:
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英文摘要 | Molecular imaging is a computer imaging technology which based on the intersection of information science, biophysics and biomedicine. It utilizes targeted probes or biomedical reagents to mark the biochemical characterization of imaging target, such as genetic factors and receptors. Combined with a series of biological image acquisition techniques and computer signal processing methods, molecular imaging obtains the spatial distribution of tracer targets in organisms at tissue level, cell level and even subcellular level. Thus, molecular imaging provides more specific and sensitive imaging tools for the biomedical research about tumor location observation, lesion growth tracking, drug metabolism and pharmacodynamic evaluation. Bioluminescence tomography (BLT) is one of the important optical molecular imaging technologies. It utilizes the bioluminescence produced by the reaction between specific receptors and fluorescein as the imaging signal, and reconstructs the three-dimensional spatial distribution of source in the organism by using the corresponding surface bioluminescence distribution. Conventional bioluminescence tomography reconstruction is an inverse problem of the photon propagation model. However, in practical applications, this model is underdetermined, illness, and has deviations with the true photon propagation. These problems limit the positioning accuracy, morphological recovery and robustness of BLT reconstruction. Furthermore, the reconstruction methods, which based on the tumor region prior referred from the other high-resolution structural modalities, improve the quality of BLT imaging. However, these methods are limited by the imaging quality of these prior, and unable to reflect the signal specificity of bioluminescence completely. This research focuses on the no tumor region guided BLT reconstruction algorithm to overcome these limitations, and can be divided into two parts: The first part relies on the conventional photon propagation model-based reconstruction strategy. It aims to design a no-tumor-region guided method that only depends on the prior of bioluminescence source distribution. On the basis of no guided method research, the second part proposes a machine learning based reconstruction strategy. This strategy utilizes a data-driven manner to build the reconstruction model, which is used to avoid the defects of model description in conventional strategy. The major contributions of this thesis are listed as follows:
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关键词 | 光学分子影像 生物自发荧光断层成像 高斯权重拉普拉斯正则先验 双边权重拉普拉斯正则先验 多层感知机重建模型 |
语种 | 中文 |
七大方向——子方向分类 | 医学影像处理与分析 |
文献类型 | 学位论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/23811 |
专题 | 中国科学院分子影像重点实验室 |
推荐引用方式 GB/T 7714 | 高源. 无肿瘤区域引导的生物自发荧光断层成像重建算法研究[D]. 北京. 中国科学院大学,2019. |
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无肿瘤区域引导的生物自发荧光断层成像重建(7003KB) | 学位论文 | 开放获取 | CC BY-NC-SA |
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