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Indexed by:Journal Papers

Date of Publication:2022-01-01

Journal:BIOMEDICAL OPTICS EXPRESS

Affiliation of Author(s):[1] Dalian University of Technology

Place of Publication:美国

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

Volume:13

Issue:3

Page Number:1275-1291

ISSN No.:2156-7085

Key Words:ATLAS，FLUORESCENCE，ALGORITHMS，SYSTEM，REGISTRATION，LOOKING，LIGHT，CT

Abstract: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 number:10.1364/BOE.448862

Impact Factor:3.7