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Spectral unmixing using component analysis in multispectral optoacoustic tomography

机译:使用多光谱光声断层扫描中的分量分析光谱解密

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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(光声)断层扫描(MSOT)利用超声检测技术给出的高分辨率,结合近红外线的深度渗透激光照明。可以使用多个波长激发和本作工作中描述的一组方法回收具有不同光谱吸收曲线的分子等痕量分子,例如血液(氧 - 和去氧化)和生物标志物。检查三种解混方法以进行分解成分的性能,以定位小鼠的深层组织中的荧光造影剂。遵循前面的工作我们发现独立的分量分析(ICA),依赖于组件统计独立性的强标准,作为最有希望的方法,能够清楚地识别其他方法未能看到的浓度。结果通过低温和荧光成像来验证

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