Integrated Analysis and Probabilistic Registration of Medical Images with Missing Correspondences

医学图像的自动、鲁棒和可靠的配准是医学图像计算中的核心问题，对图像引导的诊断和治疗有很大影响。如果在图像中存在强的解剖学或病理学差异并且在图像的部分中缺少对应的结构，则当前可用的配准方法达到其极限。当前配准方法的另一个限制是它们向用户提供的关于估计变换的局部（非）确定性的信息的缺乏，因此不允许对配准结果进行评估。该项目的目的是使图像的鲁棒性和可靠的配准，即使一对一的对应关系在图像的部分丢失。为了实现这一目标，开发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，而且依赖于通过图像分析方法提取的附加信息，如器官分割、地标和局部图像特征来对齐图像。开发的方法将使缺少局部对应的区域的配准以及局部配准结果的可靠性的客观评估。所提出的方法创新扩展了图像配准算法的医学应用范围，显着。例如，所提出的方法将促进和提高基于图像的随访研究和临床监测的质量，术前和术后图像的比较以及基于图像的统计研究，以揭示病理组织或神经元活动的空间分布模式。

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.