Non model-based bioluminescence tomography using a machine-learning reconstruction strategy

被引：62

作者：

Gao, Yuan ^{[1
,2
]}

Wang, Kun ^{[1
,2
]}

An, Yu ^{[1
,2
]}

Jiang, Shixin ^{[1
,3
]}

Meng, Hui ^{[1
,2
]}

Tian, Jie ^{[1
,2
,4
]}

机构：

[1] Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China

[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China

[3] Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China

[4] Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China

来源：

OPTICA | 2018年 / 5卷 / 11期

基金：

中国国家自然科学基金;

关键词：

LIGHT; REGISTRATION; ALGORITHM; ACCURACY;

D O I：

10.1364/OPTICA.5.001451

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

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

引用

页码：1451 / 1454

页数：4

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