王洪凯

个人信息Personal Information

教授

博士生导师

硕士生导师

主要任职:医学部副部长

性别:男

毕业院校:清华大学

学位:博士

所在单位:生物医学工程学院

学科:生物医学工程

联系方式:wang.hongkai@dlut.edu.cn

电子邮箱:wang.hongkai@dlut.edu.cn

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Bioluminescence tomography reconstruction in conjunction with an organ probability map as an anatomical reference

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论文类型:期刊论文

发表时间:2022-01-01

发表刊物:BIOMEDICAL OPTICS EXPRESS

所属单位:[1] Dalian University of Technology

刊物所在地:美国

学科门类:[1] Biochemical Research Methods [2] Optics [3] Radiology, Nuclear Medicine & Medical Imaging

卷号:13

期号:3

页面范围:1275-1291

ISSN号:2156-7085

关键字:ATLAS,FLUORESCENCE,ALGORITHMS,SYSTEM,REGISTRATION,LOOKING,LIGHT,CT

摘要:To alleviate the ill-posedness of bioluminescence tomography (BLT) reconstruction, anatomical information from computed tomography (CT) or magnetic resonance imaging (MRI) is usually adopted to improve the reconstruction quality. With the anatomical information, different organs could be segmented and assigned with appropriate optical parameters, and the reconstruction could be confined into certain organs. However, image segmentation is a time-consuming and challenging work, especially for the low-contrast organs. In this paper, we present a BLT reconstruction method in conjunction with an organ probability map to effectively incorporate the anatomical information. Instead of using a segmentation with a fixed organ map, an organ probability map is established by registering the CT image of the mouse to the statistical mouse atlas with the constraints of the mouse surface and high-contrast organs (bone and lung). Then the organ probability map of the low-contrast organs, such as the liver and kidney, is determined automatically. After discretization of the mouse torso, a heterogeneous model is established as the input for reconstruction, in which the optical parameter of each node is calculated according to the organ probability map. To take the advantage of the sparse Bayesian Learning (SBL) method in recovering block sparse signals in inverse problems, which is common in BLT applications where the target distribution has the characteristic of sparsity and block structure, a two-step method in conjunction with the organ probability map is presented. In the first step, a fast sparse algorithm, L1-LS, is used to reveal the source distribution on the organ level. In the second step, the bioluminescent source is reconstructed on the pixel level based on the SBL method. Both simulation and in vivo experiments are conducted, and the results demonstrate that the organ probability map in conjunction with the proposed two-step BLT reconstruction method is feasible to accurately reconstruct the localization of the bioluminescent light source. (c) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

DOI码:10.1364/BOE.448862

影响因子:3.7