基于知识的多模态脑三维图像分析。

A P Dhawan, L Arata
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引用次数: 0

摘要

随着医学成像的最新进展,现在可以通过MR/CT和PET/SPECT成像方式获得大脑的三维解剖和代谢图像。计算机化多模态三维脑图像配准与分析可为提高疾病的诊断和病理研究提供重要的相关信息。这样的分析也可以为计划脑部手术提供帮助。此外,基于内部结构量化和分析的解剖模型可用于开发计算机化解剖图谱。传统的解剖地图集提供了从单个主体提取的内部结构的刚性空间分布。所提出的计算机化解剖图谱提供了概率空间分布,可以很容易地更新,以纳入从预定义组中选择的受试者的大脑结构的可变性。本文首先回顾了基于知识的脑磁共振图像分割、标记和分析的当前趋势,然后描述了基于主轴变换的三维脑磁共振图像配准,以开发选定的脑内部结构的复合模型。复合模型可作为基于模型的脑磁共振图像分割和标记的计算机解剖图谱。大脑的三维标记MR图像也可以被注册并与PET图像相关联,以分析解剖学上选择的感兴趣体积中的代谢活动。另一方面,可以使用代谢信息选择感兴趣的体积,然后使用注册的MR-PET图像分析相关的解剖信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Knowledge-based multi-modality three-dimensional image analysis of the brain.

With the recent advances in medical imaging, three-dimensional anatomical and metabolic images of the brain are now available through MR/CT and PET/SPECT imaging modalities. Computerized multi-modality three-dimensional brain image registration and analysis can provide important correlated information for improving diagnosis and studying the pathology of disease. Such analysis may also provide help in planning brain surgery. Further, an anatomical model based quantification and analysis of internal structure can be used to develop a computerized anatomical atlas. Conventional anatomical atlases provide rigid spatial distribution of internal structures extracted from a single subject. The proposed computerized anatomical atlas provides probabilistic spatial distributions which can be easily updated to incorporate the variability of brain structures of subjects selected from pre-defined groups. This paper first presents a review of the current trends in knowledge-based segmentation, labeling, and analysis of MR brain images and then describes the Principal Axes Transformation based registration of three-dimensional MR brain images to develop composite models of selected internal brain structures. The composite models can be used as a computerized anatomical atlas in model-based segmentation and labeling of MR brain images. Three-dimensional labeled MR images of the brain can also be registered and correlated with PET images for analyzing the metabolic activity in the anatomically selected volume of interest. On the other hand, a volume of interest can be selected using the metabolic information and then analyzed for correlated anatomical information using the registered MR-PET images.

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