一种多模态图像配准与融合方法在药物发现研究中的应用

S. Makrogiannis, J. Wellen, Y. Wu, L. Bloy, S. Sarkar
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引用次数: 4

摘要

利用各种非侵入性成像模式的新方法的发展已经导致它们在药物化合物开发的馅饼临床阶段的使用和相关性增加。可能受益于使用成像技术的科学问题往往更有力地解决了从互补成像模式获得的数据,加强了执行跨模态图像配准和融合的需求。在这项工作中,提出了一种MRI和PET体积的多模态共配准和融合方法,目的是在高分辨率解剖参考图像(MRI)中可视化放射性标记化合物(来自PET)的生物分布。首先考虑使用动物平台和基准标记来定义空间对应关系,然后使用Parzen估计器和遗传算法优化计算和匹配基于形状的特征的初步自动对齐方法。本文还介绍了实验结果,并给出了结论和对未来工作的展望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Multimodal Image Registration and Fusion Methodology Applied to Drug Discovery Research
The development of novel methodologies that utilize various non-invasive imaging modalities has resulted in their increased use and relevance in the pie-clinical phase of pharmaceutical compound development. Scientific questions that may benefit from the use of imaging techniques are often more robustly addressed with data acquired from complementary imaging modalities, intensifying the need for performing cross-modality image registration and fusion. In this work, a methodology for multimodal coregistration and fusion of MRI and PET volumes is presented with the aim of visualizing the biodistribution of a radiolabeled compound (from PET) in a high-resolution anatomical reference image (from MRI). The use of an animal platform and fiducial markers for defining spatial correspondences is considered first, followed by a preliminary automated alignment approach that calculates and matches shape-based features using Parzen estimators and genetic algorithm optimization. Experimental results are also presented, followed by concluding remarks and future perspectives on our work.
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