在稀疏性约束下的非负矩阵分解来解调体内光谱分辨图像

Anne-Sophie Montcuquet, L. Hervé, F. Navarro, J. Dinten, J. Mars
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引用次数: 1

摘要

扩散介质中的荧光成像是一种用于医学应用的新兴成像方式,它使用注射荧光标记物(可以同时注射几种荧光标记物)结合到特定目标,如肿瘤。用近红外光照射感兴趣的区域,并分析发射的回荧光以定位荧光源。在研究厚介质时,由于荧光信号随光的传播距离减小,任何干扰信号,如生物组织的本征荧光(称为自体荧光)都是一个限制因素。为了去除自身荧光并将每个特定的荧光信号从其他信号中分离出来,我们探索了一种基于非负矩阵分解的光谱方法。我们对实验数据进行了具有稀疏性约束的NMF算法,并成功获得了分离的体内荧光光谱。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non-negative Matrix Factorization under sparsity constraints to unmix in vivo spectrally resolved acquisitions
Fluorescence imaging in diffusive media is an emerging imaging modality for medical applications which uses injected fluorescent markers (several ones may be simultaneously injected) that bind to specific targets, as tumors. The region of interest is illuminated with near infrared light and the emitted back fluorescence is analyzed to localize the fluorescence sources. To investigate thick medium, as the fluorescence signal decreases with the light travel distance, any disturbing signal, such as biological tissues intrinsic fluorescence — called autofluorescence —, is a limiting factor. To remove autofluorescence and isolate each specific fluorescent signal from the others, a spectroscopic approach, based on Non-negative Matrix Factorization, is explored. We ran an NMF algorithm with sparsity constraints on experimental data, and successfully obtained separated in vivo fluorescence spectra.
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