Computed simultaneous imaging of multiple biomarkers

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI:10.1109/NNSP.2003.1318026

Y. Wang, J. Xuan, R. Srikanchana, Junying Zhang, Z. Szabo, Z. Bhujwalla, P. Choyke, King C. Li

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引用次数: 2

Abstract

Functional-molecular imaging techniques promise powerful tools for the visualization and elucidation of important disease-causing physiologic-molecular processes in living tissue. Most applications aim to find temporal-spatial patterns associated with different disease stages. When multiple agents are used, imagery signals often represent a composite of more than one distinct source due to functional-molecular biomarker heterogeneity, independent of spatial resolution. We therefore introduce a hybrid decomposition algorithm, which allows for a computed simultaneous imaging of multiple biomarkers. The method is based on a combination of time-activity curve clustering, pixel subset selection, and independent component analysis. We demonstrate the principle of the approach on an image data set, and we then apply the method to the tumor vascular characterization using dynamic contrast-enhanced magnetic resonance imaging and brain neuro-transporter imaging using dynamic positron emission tomography.

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多种生物标志物的计算机同步成像

功能分子成像技术为可视化和阐明活组织中重要的致病生理分子过程提供了强有力的工具。大多数应用旨在发现与不同疾病阶段相关的时空模式。当使用多个代理时，由于功能分子生物标志物的异质性，图像信号通常代表一个以上不同来源的复合，与空间分辨率无关。因此，我们引入了一种混合分解算法，该算法允许对多种生物标志物进行计算机同步成像。该方法基于时间-活动曲线聚类、像素子集选择和独立分量分析相结合的方法。我们在图像数据集上演示了该方法的原理，然后我们将该方法应用于使用动态对比增强磁共振成像和使用动态正电子发射断层扫描的脑神经转运体成像的肿瘤血管表征。

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2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)

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