Non model-based bioluminescence tomography using a machine-learning reconstruction strategy
Non model-based bioluminescence tomography using a machine-learning reconstruction strategy
复制标题
DOI：
10.1364/optica.5.001451
复制
发表时间：
2018-11-20
期刊：
影响因子：
10.4
通讯作者：
Tian, Jie
中科院分区：
文献类型：
--
作者：
Gao, Yuan;Wang, Kun;Tian, Jie
生物发光断层扫描（BLT）是一种有效的非侵入性的分子成像方式在小动物体内肿瘤研究。然而，BLT重建的质量受到光子传播的简化线性模型的限制。在这里，我们提出了一种基于多层感知器的逆问题模拟（IPS）方法，以提高在体肿瘤BLT重建的质量。IPS方法不是求解光子传播的简化线性模型的逆问题，而是直接拟合物体表面光密度与其内部生物发光源之间的非线性关系。仿真和原位胶质瘤BLT重建实验表明，IPS大大提高了重建质量相比，传统的方法。(C)2018年美国光学学会根据OSA开放获取出版协议的条款
Bioluminescence tomography (BLT) is an effective noninvasive molecular imaging modality for in vivo tumor research in small animals. However, the quality of BLT reconstruction is limited by the simplified linear model of photon propagation. Here, we proposed a multilayer perceptron-based inverse problem simulation (IPS) method to improve the quality of in vivo tumor BLT reconstruction. Instead of solving the inverse problem of the simplified linear model of photon propagation, the IPS method directly fits the nonlinear relationship between an object surface optical density and its internal bioluminescent source. Both simulation and orthotopic glioma BLT reconstruction experiments demonstrated that IPS greatly improved the reconstruction quality compared with the conventional approach. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement