本文選題：生物發(fā)光斷層成像 + 貪婪算法��； 參考：《陜西師范大學(xué)》2016年碩士論文

【摘要】：分子影像能夠在分子水平上對(duì)生物組織實(shí)現(xiàn)在體成像,為研究基因功能、疾病發(fā)病機(jī)理、療效評(píng)估等方面提供了新方法,現(xiàn)已廣泛應(yīng)用于腫瘤檢測(cè)、基因治療和藥物研發(fā)等領(lǐng)域。作為光學(xué)分子影像的一種重要模態(tài),生物發(fā)光斷層成像(Bioluminescence Tomography,BLT)是根據(jù)生物體表測(cè)量到的光子分布來(lái)反演體內(nèi)光源(靶目標(biāo))的分布情況,與其他斷層成像類似,這是典型的逆問(wèn)題,特別是由于測(cè)量信息不足,加劇了重建問(wèn)題的不適定性,使得準(zhǔn)確地對(duì)光源進(jìn)行三維重構(gòu)成為挑戰(zhàn)性難題。在現(xiàn)有基于有限元方法的生物發(fā)光斷層成像中,由于靶向目標(biāo)在生物組織中分布非常稀疏,光源所在區(qū)域包含的網(wǎng)格節(jié)點(diǎn)數(shù)量遠(yuǎn)遠(yuǎn)小于整個(gè)重建域中的節(jié)點(diǎn)數(shù)量。借鑒信號(hào)處理中的稀疏信號(hào)恢復(fù)和壓縮感知重構(gòu)的理論和方法,本文對(duì)能高效重建生物發(fā)光信號(hào)的稀疏重構(gòu)算法進(jìn)行了對(duì)比研究。已有的壓縮感知重構(gòu)算法包括凸優(yōu)化算法、貪婪算法以及組合算法等。其中貪婪算法是通過(guò)迭代的方法,每次構(gòu)造并求解一個(gè)局部最優(yōu)解來(lái)逐步逼近全局最優(yōu)解,具有計(jì)算代價(jià)小,效率高的優(yōu)點(diǎn)。本文著重對(duì)基于貪婪思想的幾種代表性算法進(jìn)行了對(duì)比研究,包括正交匹配追蹤(orthogonal matching pursuit,OMP)、分段正交匹配追蹤(Stagewise Orthogonal Matching Pursuit,StOMP)、正則化正交匹配追蹤(Regularized Orthogonal Matching Pursuit,ROMP),以及基于壓縮感知的正交匹配追蹤算法(Compressive Sampling Matching Pursuit,CoSaMP)等,結(jié)合生物組織的解剖結(jié)構(gòu)先驗(yàn),在有限元方法的基礎(chǔ)上,分別將這些代表性算法結(jié)合到生物發(fā)光斷層成像的稀疏光源重建中。為了評(píng)估和比較各算法的性能,在異質(zhì)數(shù)字鼠模型上,設(shè)計(jì)了多組仿真實(shí)驗(yàn)對(duì)以上算法對(duì)單目標(biāo)和多目標(biāo)的重建能力進(jìn)行了測(cè)試。實(shí)驗(yàn)結(jié)果表明:它們可以在含噪情況下準(zhǔn)確重構(gòu)出光源位置,特別是CoSaMP作為一種深度改進(jìn)的匹配追蹤算法,其表現(xiàn)出更好的定位穩(wěn)定性以及對(duì)抗噪聲的魯棒性。本研究可為實(shí)際的生物發(fā)光斷層成像應(yīng)用給予算法選擇指導(dǎo)。
[Abstract]:Molecular imaging can perform in vivo imaging of biological tissues at the molecular level, which provides a new method for the study of gene function, pathogenesis of disease, evaluation of therapeutic effect, etc., and has been widely used in tumor detection. Gene therapy and drug development and other areas. As an important mode of optical molecular imaging, Bioluminescence TomographyBLTs invert the distribution of light source (target) based on photon distribution measured by biological surface, which is similar to other tomographic imaging. This is a typical inverse problem, especially because of the lack of measurement information, which exacerbates the ill-posed problem of reconstruction, which makes the accurate 3D reconstruction of light source become a challenging problem. In the existing bioluminescence tomography based on finite element method the number of grid nodes in the region of light source is much smaller than that in the whole reconstructed domain because the target is very sparse in biological tissue. Based on the theory and method of sparse signal restoration and compressed sensing reconstruction in signal processing, this paper compares and studies the sparse reconstruction algorithm which can efficiently reconstruct bioluminescence signals. The existing compression perception reconstruction algorithms include convex optimization algorithm, greedy algorithm and combination algorithm. The greedy algorithm approximates the global optimal solution step by iterative method and constructs and solves a local optimal solution each time. It has the advantages of low computational cost and high efficiency. This paper focuses on the comparison of several representative algorithms based on greedy thought. These include orthogonal matching pursuit, piecewise Orthogonal Matching pursuit, regularized Orthogonal Matching pursuit, Compressed-Perception-based orthogonal Sampling Matching pursuit algorithm, Compressed-Sampling Matching pursuit, etc., combined with a priori anatomical structure of biological tissue. Based on the finite element method, these representative algorithms are applied to the sparse light source reconstruction of bioluminescence tomography. In order to evaluate and compare the performance of each algorithm, a number of simulation experiments were designed to test the reconstruction ability of the above algorithms on the heterogeneous digital rat model. The experimental results show that they can accurately reconstruct the position of the light source in the case of noise, especially CoSaMP, as a depth improved matching tracking algorithm, shows better localization stability and robustness against noise. This study can provide guidance for practical bioluminescence tomography applications.
【學(xué)位授予單位】：陜西師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.41

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本文編號(hào)：1952619