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

共 50 条

[41] Towards Model-based Bias Mitigation in Machine Learning
Yohannis, Alfa
Kolovos, Dimitris
PROCEEDINGS OF THE 25TH INTERNATIONAL ACM/IEEE CONFERENCE ON MODEL DRIVEN ENGINEERING LANGUAGES AND SYSTEMS, MODELS 2022, 2022, : 143 - 153
[42] Model-based machine learning of critical brain dynamics
Bocaccio, Hernan
Tagliazucchi, Enzo
EPL, 2024, 147 (01)
[43] A Decomposition Machine-learning Strategy for Automated Fruit Grading
Susnjak, Teo
Barczak, Andre
Reyes, Napoleon
WORLD CONGRESS ON ENGINEERING AND COMPUTER SCIENCE, WCECS 2013, VOL II, 2013, Ao, : 819 - +
[44] Reconstruction of time-varying objects in computerized tomography using a model-based neural network
Deming, RW
JOINT CONFERENCE ON THE SCIENCE AND TECHNOLOGY OF INTELLIGENT SYSTEMS, 1998, : 422 - 427
[45] Machine-Learning based model order reduction of a biomechanical model of the human tongue
Calka, Maxime
Perrier, Pascal
Ohayon, Jacques
Grivot-Boichon, Christelle
Rochette, Michel
Payan, Yohan
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2021, 198
[46] A novel machine-learning based prediction model for ship manoeuvring emissions by using bridge simulator
Senol, Yunus Emre
Seyhan, Alper
OCEAN ENGINEERING, 2024, 291
[47] Credit-Risk Prediction Model Using Hybrid Deep - Machine-Learning Based Algorithms
Melese, Tamiru
Berhane, Tesfahun
Mohammed, Abdu
Walelgn, Assaye
Scientific Programming, 2023, 2023
[48] Prediction of breath-holding spells based on electrocardiographic parameters using machine-learning model
Khalilian, Mohammad Reza
Tofighi, Saeed
Attar, Elham Zohur
Nikkhah, Ali
Hajipour, Mahmoud
Ghazavi, Mohammad
Samimi, Sahar
ANNALS OF NONINVASIVE ELECTROCARDIOLOGY, 2024, 29 (01)
[49] Enabling personalized perioperative risk prediction by using a machine-learning model based on preoperative data
Graessner, Martin
Jungwirth, Bettina
Frank, Elke
Schaller, Stefan Josef
Kochs, Eberhard
Ulm, Kurt
Blobner, Manfred
Ulm, Bernhard
Podtschaske, Armin Horst
Kagerbauer, Simone Maria
SCIENTIFIC REPORTS, 2023, 13 (01)
[50] A Novel Machine-learning Model to Classify Schizophrenia Using Methylation Data Based on Gene Expression
Vijayakumar, Karthikeyan A.
Cho, Gwang-Won
CURRENT BIOINFORMATICS, 2025, 20 (01) : 31 - 45

← 1 2 3 4 5 →