基于子空间决策优化的多目标重建,用于生物发光断层扫描

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Xiao Wei¹, Hongbo Guo¹, Jingjing Yu²

¹The School of Information Sciences and Technology, Northwest University, Xi'an 710069, China; Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an 710127, China.

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|July 14, 2023

概括

子空间决策优化 (SDO) 方法通过自动分离源来改进多目标生物发光断层扫描 (BLT) 重建. 这种方法提高了准确性和质量,可以在活体动物中更好地进行瘤成像.

科学领域:

生物医学成像技术生物医学成像技术
光学成像技术的成像
医学物理医学物理

背景情况:

生物发光断层扫描 (BLT) 是一种关键的非侵入性光学成像技术,用于在体内可视化瘤分布.
目前的BLT重建方法由于信号干扰和源分离的挑战而难以处理多目标场景.

研究的目的:

为了解决多目标BLT重建的局限性.
开发一种改进的方法来准确地定位和表征瘤.

主要方法:

提出子空间决策优化 (SDO) 方法,基于代的允许区域策略.
使用聚类分析将单个允许区域转换为多个子空间.
实现子空间收缩和合并,以实现空间连续性和重建中的稳定性.
使用正常分布模型优化代结果,以实现目标稀疏性和非偏差结果.

主要成果:

通过SDO方法,可以在BLT中自动识别和分离多个目标.
在多目标BLT重建中实现了更高的准确性和质量.
SDO与各种重建算法集成,无论参数选择如何,都提供稳定,高质量的结果.

结论:

SDO方法为多目标BLT重建提供了一个全面的解决方案.
可实现自动目标识别,分离,重建和结果增强.
扩大生物发光断层扫描的临床适用性和范围.

关键词:

生物发光断层扫描生物发光断层扫描集群集成是指集群集成.决策优化决策优化决策优化逆向方法逆向方法亚空间是一个次空间.

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