结合流形正则和变量分离近似稀疏重构的荧光分子断层成像

侯榆青; 张文元; 王晓东; 贺小伟; 曹欣

doi:10.3788/OPE.20182610.2592

您当前的位置：

首页 >

文章列表页 >

结合流形正则和变量分离近似稀疏重构的荧光分子断层成像

信息科学 | 更新时间：2020-07-05

- 结合流形正则和变量分离近似稀疏重构的荧光分子断层成像
- Light source reconstruction method in fluorescence molecular tomography based on Laplacian manifold regularization and sparse reconstruction by separable approximation
- 光学精密工程 2018年26卷第10期页码：2592-2604
- 作者机构：
  
  西北大学信息科学与技术学院, 陕西西安 710127
- 作者简介：
  
  [ "侯榆青(1963-), 女, 陕西榆林人, 教授, 博士生导师, 1984年于西北大学获得学士学位, 1990年于中国科学院西安光机所获得硕士学位, 主要从事数字图像处理、医疗大数据相关影像组学研究。E-mail:houyuqin@nwu.edu.cn" ]
  [ "贺小伟(1977-), 男, 陕西米脂人, 教授, 博士生导师, 2005年于西安交通大学获得硕士学位, 2011年于西安电子科技大学获得博士学位, 主要从事光学分子影像、医学图像处理及可视化、颅面形态学等方面的研究。E-malil:hexw@nwu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金资助项目(11571012);国家自然科学基金资助项目(61640418);陕西省教育厅服务地方专项资助项目(17JF027)
- DOI：10.3788/OPE.20182610.2592
  中图分类号： Q63;TP394.1
- 收稿日期：2018-01-14，
  
  录用日期：2018-3-2，
  
  纸质出版日期：2018-10-25
- 稿件说明：
移动端阅览
侯榆青, 张文元, 王晓东, 等. 结合流形正则和变量分离近似稀疏重构的荧光分子断层成像[J]. 光学精密工程, 2018,26(10):2592-2604.

Yu-qing HOU, Wen-yuan ZHANG, Xiao-dong WANG, et al. Light source reconstruction method in fluorescence molecular tomography based on Laplacian manifold regularization and sparse reconstruction by separable approximation[J]. Optics and precision engineering, 2018, 26(10): 2592-2604.
侯榆青, 张文元, 王晓东, 等. 结合流形正则和变量分离近似稀疏重构的荧光分子断层成像[J]. 光学精密工程, 2018,26(10):2592-2604. DOI： 10.3788/OPE.20182610.2592.

Yu-qing HOU, Wen-yuan ZHANG, Xiao-dong WANG, et al. Light source reconstruction method in fluorescence molecular tomography based on Laplacian manifold regularization and sparse reconstruction by separable approximation[J]. Optics and precision engineering, 2018, 26(10): 2592-2604. DOI： 10.3788/OPE.20182610.2592.

摘要

为改善荧光分子断层成像的重建结果，本文采用联合稀疏-流形正则模型进行光源重建，该联合稀疏-流形正则模型能同时利用重建光源聚集性和稀疏性的先验信息。为有效求解该联合稀疏-流形正则模型，本文通过重新推导变量分离近似稀疏重构算法对其进行求解。为加快变量分离近似稀疏重构算法求解联合稀疏-流形正则模型的速度，本文在光源重建过程中采用了热启动策略。实验结果表明，相比变量分离近似稀疏重构算法求解范数模型，变量分离近似稀疏重构算法求解联合稀疏-流形正则模型将重建结果的对比噪声比从6.45提升至9.18。另外，相比没有采用热启动策略，采用热启动策略的变量分离近似稀疏重构算法求解联合稀疏-流形正则模型的时间从101.84 s减至50.10 s。本文方法显著提高了光源目标重建的精度和速度，取得了更优的重建结果。

Abstract

To enhance reconstruction performance in fluorescence molecular tomography

a joint-norm and a Laplacian manifold regularization model that combined both sparsity and spatial aggregation information was utilized for light source reconstruction. In this report

sparse reconstruction by separable approximation (SpaRSA) was developed to investigate the joint model (SpaRSA-resolved Laplacian manifold regularization model

SpaRSALM). To improve the convergence speed of the SpaRSALM algorithm

a warm-start strategy was applied for light source reconstruction. The experimental results show that the SpaRSALM algorithm solved the joint model problem and improved the contrast to noise ratio (CNR) from 6.45 to 9.18 compared to using the SpaRSA algorithm to solve for the -norm regularization model. In addition

the reconstruction of the SpaRSALM algorithm using the warm-start strategy (compared to without the warm-start strategy) required 50.10 s (as opposed to 101.84 s). The accuracy and speed of light source reconstruction were significantly improved

and better reconstruction results were achieved using the presented method.

关键词

Keywords

references

NTZIACHRISTOS V, BREMER C, GRAVES E E, et al..In vivo tomographic imaging of near-infrared fluorescent probes[J].Molecular Imaging, 2002, 1(2):82-88.

WILLMANN J K, VAN BRUGGEN N, DINKELBORG L M, et al.. Molecular imaging in drug developm-ent[J]. Nature Reviews Drug Discovery, 2008, 7(7):591-607.

HE X W, DONG F, YU J J, et al.. Reconstruction algorithm for fluorescence molecular tomography using sorted L-one penalized estimation[J].Journal of the Optical Society of America A Optics Image Science & vision, 2015, 32(11):1928-35.

BANGERTH W, JOSHI A.Adaptive finite element methods for the solution of inverse problems in optical tomography[J].Inverse Problems.2008, 24(3):657-682.

HAN D, YANG X, LIU K, et al.. Efficient reconstruction method for L1 regularization in fluorescence molecular tomography[J].Applied optics, 2010, 49(36):6930-6937.

HE X W, LIANG J M, WANG X R, et al.. Sparse reconstruction for quantitative bioluminescence tomo-graphy based on the incomplete variables truncated conjugate gradient method[J]. Optics Express, 2010, 18(24):24825-24841.

郭红波, 贺小伟, 侯榆青, 等.基于非凸稀疏正则的荧光分子断层成像[J].光学学报, 2015, 35(7):252-259.

GUO H B, HE X W, HOU Y Q, et al.. Fluorescence Molecular Tomography Based on Nonconvex Sparse Regularization[J].Acta Optica Sinica, 2015, 35(7):252-259.(in Chinese)

YANG F G, OZTURK M S, ZHAO L L et al.. High-Resolution Mesoscopic Fluorescence Molecular Tomography Based on Compressive Sensing[J]. IEEE Transactions on Biomedical Engineering, 2015, 62(1):248-255.

ZHANG J L, SHI J W, GUANG H Z, et al..Iterative Correction Scheme Based on Discrete Cosine Transform and L1 Regularization for Fluorescence Molecular Tomography With Background Fluorescence[J]. IEEE Transactions on Biomedical Engineering, 2016, 63(6):1107-1115.

YU J J, LI Q Y, WANG H Y. Source reconstruction for bioluminescence tomography via L1∕2 regularization[J]. Journal of Innovative Optical Health Sciences, 2017, 1(11):1750014.

GUO H B, YU J J, HE X W, et al..Improved sparse reconstruction for fluorescence molecular tomography with L1/2 regularization[J].Biomedical Optics Express.2015, 6(5):1648-1664.

ZHANG H B, GENG G H, WANG X D, et al.. Fast and Robust Reconstruction for Fluorescence Molecular Tomography via L1-2 Regularization[J]. BioMed Research International, 2016(2016), Article ID 5065217.

YIN P H, LOU Y F, HE Q, et al..Minimization of L1-2 for Compressed Sensing[J]. Siam Journal on Scientific Computing, 2015, 37(1):A536-A563.

LOU Y F, YIN P H, HE Q, et al..Computing Sparse Representation in a Highly Coherent Dictionary Based on Difference of L1 and L2[J].Journal of Scientific Computing, 2015, 64(1):178-196.

HE X L, WANG X D, YI H J, et al..Laplacian manifold regularization method for fluorescence molecular tomography[J].Journal of Biomedical Optics, 2017, 22(4):45009.

ZHANG J L, SHI J W, ZUO S M, et al..Fast reconstruction in fluorescence molecular tomography using data compression of intra-and inter-projections[J].Chinese Optics Letters, 2015, 13(7):52-56.

CONG W X, WANG G, DURAIRAJ K, et al.. Practical reconstruction method for bioluminescence tomography[J]. Optics Express, 2005, 13(18):6756-6771.

FIGUEIREDO M T, NOWAK R D, WRIGHT S J.Gradient Projection for Sparse Reconstruction:Application to Compressed Sensing and Other Inverse Problems[J]. IEEE Journal of Selected Topics in Signal Processing, 2008, 1(4):586-597.

WANG F, ZHANG C S, LI T. Clustering with Local and Global Regularization[J]. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(12):1665-1678.

WRIGHT S J, NOWAK R D, FIGUEIREDO M T.Sparse Reconstruction by Separable Approximation[J].IEEE Transactions on Signal Processing.2009, 57(7):2479-2493.

侯榆青, 贾涛, 易黄健, 等.基于改进谱投影梯度算法的X射线发光断层成像[J].光学精密工程, 2017, 25(1):42-49.

HOU Y Q, JIA T, YI H J, et al..X-ray luminescence computed tomography based on improved spectral projected gradient algorithm[J].Optics and Precision Engineering, 2017, 25(1):42-49.

SONG X M, POGUE B W, JIANG S D, et al.. Automated region detection based on the contrast-to-noise ratio in near-infrared tomography[J]. Applied Optics, 2004, 43(5):1053-1062.

浏览量

313

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据