Performance evaluation of principal component analysis for dynamic fluorescence tomographic imaging in measurement space
Performance evaluation of principal component analysis for dynamic fluorescence tomographic imaging in measurement space
复制标题
测量空间动态荧光断层成像主成分分析性能评估
DOI：
10.1117/1.oe.54.5.053108
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发表时间：
2015
影响因子：
1.3
通讯作者：
Yan Zhuangzhi
中科院分区：
文献类型：
--
作者：
Liu Xin;He Xiaowei;Yan Zhuangzhi
抽象的。通过荧光漫射光学断层扫描(FDOT)解决药物(荧光生物标记物)在小动物体内的分布仍然是一个挑战。主成分分析(PCA)提供了从动态FDOT图像中检测器官(功能结构)的能力。然而，主成分分析的分辨性能可能会受到各种实验因素的影响，如测量数据中的噪声水平、光学特性的方差、采集的帧的数量等。为了解决这个问题，基于一个仿真模型，我们分析和比较了主成分分析在三组典型的实验条件(帧数目、噪声水平和光学特性)下的性能。结果表明，噪声是影响主成分分析性能的关键因素。当输入包含低噪声(<；5%)的数据时，通过短的(例如，6帧)投影序列，我们可以分辨出聚(DL-乳酸-共乙醇酸)/吲哚因绿(PLGA/ICG)在心脏和肺中的分布，即使在光学性质上有很大的变化。相反，当在输入数据中加入20%的高斯噪声时，即使使用了精确的光学性质，也很难解析PLGA/ICG在心脏和肺中的分布。但是，随着帧数的增加，PCA的分辨率可能会逐渐恢复。
Abstract. Challenges remain in resolving drug (fluorescent biomarkers) distributions within small animals by fluorescence diffuse optical tomography (FDOT). Principal component analysis (PCA) provides the capability of detecting organs (functional structures) from dynamic FDOT images. However, the resolving performance of PCA may be affected by various experimental factors, e.g., the noise levels in measurement data, the variance in optical properties, the number of acquired frames, and so on. To address the problem, based on a simulation model, we analyze and compare the performance of PCA when applied to three typical sets of experimental conditions (frames number, noise level, and optical properties). The results show that the noise is a critical factor affecting the performance of PCA. When input data containing a low noise (<5%), by a short (e.g., 6 frame) projection sequence, we can resolve the poly(DL-lactic-coglycolic acid)/indocynaine green (PLGA/ICG) distributions in heart and lungs, even though there are great variances in optical properties. In contrast, when 20% Gaussian noise is added to the input data, it hardly resolves the distributions of PLGA/ICG in heart and lungs even though accurate optical properties are used. However, with an increased number of frames, the resolving performance of PCA may gradually recover.