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Integrated Analysis and Probabilistic Registration of Medical Images with Missing Correspondences

缺失对应的医学图像的综合分析和概率配准

基本信息

批准号：
271947978
负责人：
Dr. Jan Ehrhardt
金额：
--
依托单位：
Institut für Medizinische Informatik (IMI)
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2015
资助国家：
德国
起止时间：
2014-12-31 至 2020-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/271947978?language=en
关键词：
Integrated Analysis Probabilistic Registration Medical

项目摘要

The automatic, robust and reliable registration of medical images is a central problem in medical image computing with high impact on image-guided diagnostics and therapy. Currently available registration methods reach their limits, if strong anatomical or pathologic discrepancies are present in the images and corresponding structures are missing in parts of the images. Another limitation of current registration methods is the lack of information they provide to the user about the local (un)certainty of the estimated transformation and therefore does not allow an assessment of the registration results. The aim of this project is to enable the robust and reliable registration of images even if one-to-one correspondences are missing in parts of the images. To achieve this, a general probabilistic registration framework based on correspondence probabilities is developed that does not only rely on image intensities but also on additional information extracted by image analysis methods like organ segmentations, landmarks and local image features to align images. The methods to develop will enable the registration of areas with missing local correspondences as well as the objective assessment of the reliability of the local registration results.The proposed methodical innovations extend the medical application spectrum of image registration algorithms, significantly. For example, the proposed method will facilitate and improve the quality of image-based follow-up studies and clinical monitoring, comparison of pre- and post-operative images as well as image-based statistical studies to reveal spatial distribution patterns of pathological tissues or neuronal activities.

医学图像的自动、稳健、可靠配准是医学图像计算的核心问题，对图像引导的诊断和治疗有着重要的影响。如果图像中存在强烈的解剖或病理差异，并且部分图像中缺少相应的结构，则现有的配准方法将达到其极限。当前配准方法的另一个局限性是它们向用户提供关于估计的变换的局部(非)确定性的信息，因此不允许对配准结果进行评估。该项目的目的是实现稳健可靠的图像配准，即使图像的某些部分缺少一对一的对应。为此，提出了一种基于对应概率的通用概率配准框架，该框架不仅依赖于图像强度，还依赖于器官分割、标志点和局部图像特征等图像分析方法提取的附加信息来对齐图像。所提出的方法将能够对缺失局部对应的区域进行配准，并对局部配准结果的可靠性进行客观评估。例如，该方法将促进和提高基于图像的后续研究和临床监测、手术前后图像的比较以及基于图像的统计研究的质量，以揭示病理组织或神经元活动的空间分布模式。