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基于结构相关正交匹配追踪的激发荧光肝肿瘤重建研究
孔令鑫
Subtype硕士
Thesis Advisor杜洋
2020-05-24
Degree Grantor中国科学院大学
Place of Conferral自动化研究所
Degree Discipline模式识别与智能系统
Keyword激发荧光断层成像 匹配追踪 组稀疏 斯密特正交化
Abstract

光学分子影像技术作为医工多学科交叉的前沿成像技术,具有高特异性、高灵敏度、高分辨率的显著优势,能够在细胞或分子层面对活体状态下的生命过程开展定性和定量研究。激发荧光断层成像是光学分子影像技术的重要模态,通过融合激发荧光成像和计算机断层成像实现多种模态的优势互补,能够连续、动态地观测荧光标记物在生物体内的三维分布情况。
激发荧光断层成像技术依托荧光标记物对于特定细胞的靶向作用,通过特定波长激发光产生荧光信号,并基于光子在生物组织中的传播模型反演重建出体内荧光光源的分布情况。然而,由于采集条件等限制,激发荧光断层重建面临着高不适定性、强病态性、低鲁棒性等挑战性问题。传统方法是基于肿瘤分布的稀疏先验,添加L1范数正则化项来提升重建质量,但普遍面临着结果过稀疏、空间不连续、缺乏鲁棒性等显著问题。因此,本文针对上述存在的问题,根据激发荧光断层重建中存在的局部空间结构相关性和系统矩阵列向量元素相关性,提出两种重建方法,从定位误差、形态学相似度、相对稀疏度、鲁棒性等多个方面优化算法性能。本文的主要工作和创新点归纳如下:
1. 提出了一种基于自适应组正交匹配追踪的激发荧光断层重建方法
在临床应用中,肿瘤在体内的分布不仅满足全局稀疏性空间约束,还由于肿瘤生长过程中的成团特性,具有局部关联性空间约束。自适应组正交匹配追踪算法,基于上述肿瘤分布的两种先验信息,在不需要其他成像模态提供肿瘤分布硬先验的前提下,创新性地提出了局部空间结构化正则化项,并采用自适应正交匹配追踪算法展开求解。该算法利用局部空间结构关联性,依托有限元离散化过程中生成的微小四面体网格作为分组策略构建组稀疏基本求解原子,并带入到自适应组正交匹配追踪算法中迭代完成重建工作。基于数字鼠模型的仿体实验、以及多只荷肝肿瘤裸鼠实验均表明,该重建算法能较好地缓解稀疏约束类算法普遍存在的过稀疏问题,拥有更高的定位精度、更准确的量子产额、更好的形态学相似性、和更鲁棒的抗噪能力。
2.提出了一种基于正则化双正交匹配追踪的激发荧光断层重建方法
正交匹配追踪类算法基于贪婪思想,通过迭代计算与残差相关度最大的原子并更新支撑集来完成稀疏重建任务。然而,激发荧光断层成像在采集和重建过程中普遍受到噪声干扰、分割偏差以及节点间相互作用的影响,导致基于贪婪思想的稀疏重建任务容易出现原子错选,极大地影响荧光分布的重建质量。基于正则化双正交匹配追踪的激发荧光断层重建方法,融合了斯密特正交化操作和正则化匹配追踪算法,通过对系统矩阵开展斯密特正交化去除支撑集中原子对于待定原子选择的干扰,从而在不断迭代的过程中提高元素选择的正确率。针对该算法,本文设计了多光源数字鼠仿真实验和单光源荷瘤裸鼠在体实验来验证算法的性能。结果表明,正则化双正交匹配追踪算法较对比方法,具有更好的原子选择能力,较好地避免了在真实区域产生空间不连续的微小肿瘤,从而提升了重建质量。
 

Other Abstract

Optical molecular imaging is a promising and cutting-edge technology with the features of high specificity, high resolution, and high sensitivity, aiming to quantitatively and qualitatively analyse in vivo life processes at cellular and molecular level. Fluorescence molecular tomography (FMT) is an important part of optical molecular imaging. Through fusing the fluorescence molecular imaging (FMI) and computer tomography to make the best use of each imaging modality, FMT can achieve the dynamic and consecutive observation of the three-dimensional biodistribution of fluorophore.
FMT is based on specialized fluorophore with specific cell targetting property, high qualified signal acquisition equipment like EMCCD, and through the mathematical model of light transmission among biological tissues to reconstruct the fluorephore biodistribution under excitation condition. However, due to the restriction on imaging processes, FMT faces high ill-posedness, high ill-conditionedness, as well as lack of robustness. Although conventional methods for FMT reconstruction is based on sparsity prior from the tumor biodistribution through adding L1-norm regularization to enhance the performance, introduce over-sparseness, spatial discontinuity, and low robustness. Hence, this study aims to overcome the above challenges, and proposes two novel reconstruction methods according to local spatial structural correlation and columns correlation in system matrix, to reduce the locating error, as well as improving the morphological similarity, relative sparsity, and robustness. The main contributions of my work can be summarized as follows.   
1. Adaptive group orthogonal matching pursuit method (AGOMP)
In clinical applications, tumor spatial biodistribution inside imaging object not only satisfies the global sparsity, but is also accordance with local spatial structural correlation because of the clustering feature during the tumor growth. AGOMP method combines above two priors as group sparsity to design a novel local spatial structural regularization without the hard prior of tumor region from other modalities, and adopts sparsity adaptive orthogonal matching pursuit method to address the FMT reconstruction. The grouping strategy is based on small tetrahedron caused by finite element segmentation, and the grouped elements are packaged as a unit during searching and iteration. A series of numerical simulation experiments, based on digital mouse with both one and several tumors, were conducted, as well as in vivo mouse experiments. The results demonstrated the higher locating accuracy, more precise fluorescent yields, better morphological similarity, and more robust of AGOMP.
2. Regularized doubly orthogonal matching pursuit method (RDOMP)
Orthogonal matching pursuit method is based on greedy algorithm, through iteratively calculating residual error related element to update support set and complete FMT reconstruction. However, due to the impact of noise interference, segmentation deviation, and interaction among nodes on data acquisition and reconstruction, incorrected elements were always selected into support set and greatly affect the reconstruction performance. Therefore, this thesis proposed RDOMP method through synergistically integrating Gram-Schmidt (GS) orthogonalization with regularized orthogonal matching pursuit (ROMP) to decorrelate the elements in support set against remaining elements. Experiments based on the numerical mouse with double tumors and in vivo mouse were conducted to validate the enhancement of RDOMP. The reconstructed results demonstrated the better ability of atom selection compared with contrast methods, as well as avoid generating tiny tumor discontinuously located near the ground truth region, thus to enhance the performance of FMT reconstruction.
 

Pages85
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/39018
Collection中国科学院分子影像重点实验室
Recommended Citation
GB/T 7714
孔令鑫. 基于结构相关正交匹配追踪的激发荧光肝肿瘤重建研究[D]. 自动化研究所. 中国科学院大学,2020.
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