Iterative reweighting via homotopy for reconstruction of bioluminescence tomography

2016 ◽  
Author(s):  
Jingjing Yu ◽  
Qiyue Li ◽  
Haiyu Wang
Keyword(s):  
Bioluminescence Tomography ◽  
Iterative Reweighting

scholarly journals Moment searching algorithm for bioluminescence tomography

2009 ◽  
Vol 7 (7) ◽  
pp. 614-616
Author(s):  
靳露冬 Ludong Jin ◽  
吴艳 Yan Wu ◽  
田捷 Jie Tian ◽  
黄鹤羽 Heyu Huang ◽  
屈晓超 Xiaochao Qu
Keyword(s):  
Bioluminescence Tomography

Simultaneous diffuse optical and bioluminescence tomography to improve source localization

2021 ◽  
Author(s):  
Alexander Bentley ◽  
Xiangkun Xu ◽  
Zijian Deng ◽  
Jonathan E. Rowe ◽  
Ken Kang-Hsin Wang ◽  
...  
Keyword(s):  
Source Localization ◽  
Bioluminescence Tomography

scholarly journals Analytical quality assessment of iteratively reweighted least-squares (IRLS) method

2014 ◽  
Vol 20 (1) ◽  
pp. 132-141 ◽  
Author(s):  
Jianfeng Guo
Keyword(s):  
Least Squares ◽  
Order Approximation ◽  
Diagnostic Tools ◽  
Iteratively Reweighted Least Squares ◽  
First Order ◽  
Iterative Reweighting ◽  
First Order Approximation ◽  
Detectable Bias ◽  
Theoretical Analyses ◽  
Exact Analytical Method

The iteratively reweighted least-squares (IRLS) technique has been widely employed in geodetic and geophysical literature. The reliability measures are important diagnostic tools for inferring the strength of the model validation. An exact analytical method is adopted to obtain insights on how much iterative reweighting can affect the quality indicators. Theoretical analyses and numerical results show that, when the downweighting procedure is performed, (1) the precision, all kinds of dilution of precision (DOP) metrics and the minimal detectable bias (MDB) will become larger; (2) the variations of the bias-to-noise ratio (BNR) are involved, and (3) all these results coincide with those obtained by the first-order approximation method.

scholarly journals A source reconstruction algorithm based on adaptive hp-FEM for bioluminescence tomography

2009 ◽  
Vol 17 (17) ◽  
pp. 14481 ◽  
Author(s):  
Runqiang Han ◽  
Jimin Liang ◽  
Xiaochao Qu ◽  
Yanbin Hou ◽  
Nunu Ren ◽  
...  
Keyword(s):  
Reconstruction Algorithm ◽  
Source Reconstruction ◽  
Bioluminescence Tomography

Bioluminescence Tomography Based on Gaussian Weighted Laplace Prior Regularization for In Vivo Morphological Imaging of Glioma

2017 ◽  
Vol 36 (11) ◽  
pp. 2343-2354 ◽  
Author(s):  
Yuan Gao ◽  
Kun Wang ◽  
Shixin Jiang ◽  
Yuhao Liu ◽  
Ting Ai ◽  
...  
Keyword(s):  
Bioluminescence Tomography

scholarly journals Source reconstruction for bioluminescence tomography via L1∕2 regularization

2018 ◽  
Vol 11 (02) ◽  
pp. 1750014 ◽  
Author(s):  
Jingjing Yu ◽  
Qiyue Li ◽  
Haiyu Wang
Keyword(s):  
Image Quality ◽  
Matching Pursuit ◽  
Imaging Modality ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Reconstruction Algorithms ◽  
Preclinical Research ◽  
Bioluminescence Tomography ◽  
Comparison Results ◽  
Stable Reconstruction

Bioluminescence tomography (BLT) is an important noninvasive optical molecular imaging modality in preclinical research. To improve the image quality, reconstruction algorithms have to deal with the inherent ill-posedness of BLT inverse problem. The sparse characteristic of bioluminescent sources in spatial distribution has been widely explored in BLT and many L1-regularized methods have been investigated due to the sparsity-inducing properties of L1 norm. In this paper, we present a reconstruction method based on L[Formula: see text] regularization to enhance sparsity of BLT solution and solve the nonconvex L[Formula: see text] norm problem by converting it to a series of weighted L1 homotopy minimization problems with iteratively updated weights. To assess the performance of the proposed reconstruction algorithm, simulations on a heterogeneous mouse model are designed to compare it with three representative sparse reconstruction algorithms, including the weighted interior-point, L1 homotopy, and the Stagewise Orthogonal Matching Pursuit algorithm. Simulation results show that the proposed method yield stable reconstruction results under different noise levels. Quantitative comparison results demonstrate that the proposed algorithm outperforms the competitor algorithms in location accuracy, multiple-source resolving and image quality.

scholarly journals Bioluminescence tomography with structural information estimated via statistical mouse atlas registration

2018 ◽  
Vol 9 (8) ◽  
pp. 3544
Author(s):  
Bin Zhang ◽  
Wanzhou Yin ◽  
Hao Liu ◽  
Xu Cao ◽  
Hongkai Wang
Keyword(s):  
Structural Information ◽  
Bioluminescence Tomography ◽  
Atlas Registration ◽  
Mouse Atlas

L p Regularization for Bioluminescence Tomography Based on the Split Bregman Method

2016 ◽  
Vol 18 (6) ◽  
pp. 830-837 ◽  
Author(s):  
Yifang Hu ◽  
Jie Liu ◽  
Chengcai Leng ◽  
Yu An ◽  
Shuang Zhang ◽  
...  
Keyword(s):  
Split Bregman Method ◽  
Split Bregman ◽  
Bioluminescence Tomography
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