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A Novel Adaptive Parameter Search Elastic Net Method for Fluorescent Molecular Tomography

机译：一种新型自适应参数搜索弹性网荧光分子断层扫描方法

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摘要

Fluorescence molecular tomography (FMT) is a new type of medical imaging technology that can quantitatively reconstruct the three-dimensional distribution of fluorescent probes in vivo. Traditional Lp norm regularization techniques used in FMT reconstruction often face problems such as over-sparseness, over-smoothness, spatial discontinuity, and poor robustness. To address these problems, this paper proposes an adaptive parameter search elastic net (APSEN) method that is based on elastic net regularization, using weight parameters to combine the L1 and L2 norms. For the selection of elastic net weight parameters, this approach introduces the L0 norm of valid reconstruction results and the L2 norm of the residual vector, which are used to adjust the weight parameters adaptively. To verify the proposed method, a series of numerical simulation experiments were performed using digital mice with tumors as experimental subjects, and in vivo experiments of liver tumors were also conducted. The results showed that, compared with the state-of-the-art methods with different light source sizes or distances, Gaussian noise of 5%-25%, and the brute-force parameter search method, the APSEN method has better location accuracy, spatial resolution, fluorescence yield recovery ability, morphological characteristics, and robustness. Furthermore, the in vivo experiments demonstrated the applicability of APSEN for FMT.
机译：荧光分子断层扫描（FMT）是一种新型的医学成像技术，可以定量地重建体内荧光探针的三维分布。传统的LP规范正则化技术用于FMT重建通常面临过稀疏，过度平滑，空间不连续性和鲁棒性差的问题。为了解决这些问题，本文提出了一种基于弹性净正常化的自适应参数搜索弹性网（APSEN）方法，使用权重参数来组合L1和L2规范。为了选择弹性净重参数，该方法介绍了有效重建结果的L0规范和残余向量的L2标准，用于自适应地调节重量参数。为了验证所提出的方法，使用具有肿瘤的数字小鼠作为实验对象进行一系列数值模拟实验，并且还进行了肝肿瘤的体内实验。结果表明，与用不同光源尺寸或距离的最先进方法相比，高斯噪声为5％-25％，以及布声参数搜索方法，APSEN方法具有更好的位置精度，空间分辨率，荧光产量回收能力，形态特征和鲁棒性。此外，体内实验表明APSEN用于FMT的适用性。

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  • 来源
    《IEEE Transactions on Medical Imaging》 |2021年第5期|1484-1498|共15页
  • 作者

    Wang Hanfan; Bian Chang; Kong Lingxin; An Yu; Du Yang; Tian Jie;

  • 作者单位

    Xidian Univ Sch Life Sci & Technol Xian 710071 Peoples R China|Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging Beijing 100190 Peoples R China;

    Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging Beijing 100190 Peoples R China|Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100080 Peoples R China;

    Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging Beijing 100190 Peoples R China|Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100080 Peoples R China;

    Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging Beijing 100190 Peoples R China|Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Sch Med Sci & Engn Beijing 100191 Peoples R China;

    Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging Beijing 100190 Peoples R China|Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100080 Peoples R China;

    Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging Beijing 100190 Peoples R China|Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Sch Med Sci & Engn Beijing 100191 Peoples R China|Xidian Univ Sch Life Sci & Technol Minist Educ Engn Res Ctr Mol & Neuro Imaging Xian 710071 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Image reconstruction; Fluorescence; Probes; Mathematical model; Photonics; Molecular imaging; Biological tissues; Fluorescence molecular tomography; adaptive parameter search; elastic net;

    机译：图像重建;荧光;探针;数学模型;光子学;分子成像;生物组织;荧光分子断层扫描;自适应参数搜索;弹性网;弹性网;

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