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Development and validation of factor analysis for dynamic in-vivo imaging data sets

机译:动态体内成像数据集因子分析的开发和验证

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In-vivo optical imaging method provides information about the anatomical structures and function of tissues ranging from single cell to entire organisms. Dynamic Fluorescent Imaging (DFI) is used to examine dynamic events related to normal physiology or disease progression in real time. In this work we improve this method by using factor analysis (FA) to automatically separate overlying structures.The proposed method is based on a previously introduced Transcranial Optical Vascular Imaging (TOVI), which employs natural and sufficient transparency through the intact cranial bones of a mouse. Fluorescent image acquisition is performed after intravenous fluorescent tracer a d ministration. A fterwards FA i s u sed to extract structures with different temporal characteristics from dynamic contrast enhanced studies without making any a priori assumptions about physiology. The method was validated by a dynamic light phantom based on the Arduino hardware platform and dynamic fluorescent cerebral hemodynamics data s ets. Using the phantom data FA can separate various light channels without user intervention. FA applied on an image sequence obtained after fluorescent t racer a dministration is allowing extracting valuable information about cerebral blood vessels anatomy and functionality without a-priory assumptions of their anatomy or physiology while keeping the mouse cranium intact. Unsupervised color-coding based on FA enhances visibility and distinguishing of blood vessels belonging to different compartments. DFI based on FA especially in case of transcranial imaging can be used to separate dynamic structures.
机译:体内光学成像方法可提供有关从单个细胞到整个生物体的组织的解剖结构和功能的信息。动态荧光成像(DFI)用于实时检查与正常生理或疾病进展相关的动态事件。在这项工作中,我们通过使用因子分析(FA)自动分离上覆结构来改进此方法。该方法基于先前介绍的经颅光学血管成像(TOVI),该技术通过完整的颅骨完整自然地透明化老鼠。静脉荧光示踪剂滴注后进行荧光图像采集。后来,FA从动态对比增强研究中提取了具有不同时间特征的结构,而没有对生理学做出任何先验假设。该方法已通过基于Arduino硬件平台的动态光幻影和动态荧光脑血流动力学数据集进行了验证。使用幻象数据FA可以分离各种光通道,而无需用户干预。将FA应用于荧光t消旋剂后获得的图像序列上,可以在不保留其颅骨完整的前提下,无需事先假设其解剖结构或生理状况就提取有关脑血管解剖结构和功能的有价值的信息。基于FA的无监督颜色编码可增强可视性并区分属于不同隔室的血管。基于FA的DFI,尤其是在经颅成像的情况下,可用于分离动态结构。

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