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Nonmodel-based bioluminescence tomography using a machine-learning reconstruction strategy
Optica ( IF 10.4 ) Pub Date : 2018-11-09 , DOI: 10.1364/optica.5.001451
Yuan Gao , Kun Wang , Yu An , Shixin Jiang , Hui Meng , Jie Tian

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.

中文翻译:

基于非模型的生物发光层析成像,采用机器学习重建策略

生物发光断层扫描(BLT)是一种有效的非侵入性分子成像方法,用于在小动物体内进行肿瘤研究。但是,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,以提高体内肿瘤BLT重建的质量。IPS方法不是解决简化的光子传播线性模型的反问题,而是直接拟合对象表面光密度与其内部生物发光源之间的非线性关系。仿真和原位神经胶质瘤BLT重建实验均表明,与传统方法相比,IPS大大提高了重建质量。
更新日期:2018-11-21
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