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首页> 外文会议>Molecular imaging III >Spectral unmixing using component analysis in multispectral optoacoustic tomography
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Spectral unmixing using component analysis in multispectral optoacoustic tomography

机译：使用多光谱光声层析成像中的成分分析进行光谱分解

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Multispectral optoacoustic (photoacoustic) tomography (MSOT) exploits high resolutions given by ultrasound detection technology combined with deeply penetrating laser illumination in the near infrared. Traces of molecules with different spectral absorption profiles, such as blood (oxy- and de-oxygenated) and biomarkers can be recovered using multiple wavelengths excitation and a set of methods described in this work. Three unmixing methods are examined for their performance in decomposing images into components in order to locate fluorescent contrast agents in deep tissue in mice. Following earlier works we find Independent Component Analysis (ICA), which relies on the strong criterion of statistical independence of components, as the most promising approach, being able to clearly identify concentrations that other approaches fail to see. The results are verified by cryosectioning and fluorescence imaging
机译：多光谱光声层析成像（MSOT）利用超声检测技术结合近红外中的深穿透激光照射获得的高分辨率。可以使用多波长激发和本工作中描述的一组方法来恢复具有不同光谱吸收曲线的分子的痕迹，例如血液（经氧和脱氧的）和生物标记。为了将荧光造影剂定位在小鼠深层组织中，研究了三种解混方法在将图像分解成成分方面的性能。在早期工作之后，我们发现独立成分分析（ICA）是最有希望的方法，它依赖于成分统计独立性的强大标准，能够清楚地识别其他方法看不到的浓度。通过冷冻切片和荧光成像验证了结果

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