Image registration is an essential technique for the analysis of Computed Tomography (CT) images in clinical practice. However, existing methodologies are predominantly tailored to a specific organ of interest and often exhibit lower performance on other organs, thus limiting their generalizability and applicability. Multi-organ registration addresses these limitations, but the simultaneous alignment of multiple organs with diverse shapes, sizes and locations requires a highly complex deformation field with a multi-layer composition of individual deformations. This study introduces a novel field decomposition approach to address the high complexity of deformations in multi-organ whole-body CT image registration. The proposed method is trained and evaluated on a longitudinal dataset of 691 patients, each with two CT images obtained at distinct time points. These scans fully encompass the thoracic, abdominal, and pelvic regions. Two baseline registration methods are selected for this study: one based on optimization techniques and another based on deep learning. Experimental results demonstrate that the proposed approach outperforms baseline methods in handling complex deformations in multi-organ whole-body CT image registration.

翻译：图像配准是临床实践中计算机断层扫描（CT）图像分析的关键技术。然而，现有方法主要针对特定感兴趣器官进行设计，通常在其他器官上表现出较低的性能，从而限制了其泛化能力和适用性。多器官配准旨在解决这些局限性，但同时对具有不同形状、大小和位置的多个器官进行对齐，需要一种由多层独立形变组合而成的高度复杂的形变场。本研究引入了一种新颖的场分解方法，以应对多器官全身CT图像配准中形变的高度复杂性。所提出的方法在一个包含691名患者的纵向数据集上进行训练和评估，每名患者拥有在两个不同时间点获取的两幅CT图像。这些扫描完整覆盖了胸部、腹部和盆腔区域。本研究选择了两种基线配准方法进行比较：一种基于优化技术，另一种基于深度学习。实验结果表明，在处理多器官全身CT图像配准中的复杂形变方面，所提出的方法优于基线方法。