Image registration is a fundamental technique in the analysis of longitudinal and multi-phase CT images within clinical practice. However, most existing methods are tailored for single-organ applications, limiting their generalizability to other anatomical regions. This work presents TotalRegistrator, an image registration framework capable of aligning multiple anatomical regions simultaneously using a standard UNet architecture and a novel field decomposition strategy. The model is lightweight, requiring only 11GB of GPU memory for training. To train and evaluate our method, we constructed a large-scale longitudinal dataset comprising 695 whole-body (thorax-abdomen-pelvic) paired CT scans from individual patients acquired at different time points. We benchmarked TotalRegistrator against a generic classical iterative algorithm and a recent foundation model for image registration. To further assess robustness and generalizability, we evaluated our model on three external datasets: the public thoracic and abdominal datasets from the Learn2Reg challenge, and a private multiphase abdominal dataset from a collaborating hospital. Experimental results on the in-house dataset show that the proposed approach generally surpasses baseline methods in multi-organ abdominal registration, with a slight drop in lung alignment performance. On out-of-distribution datasets, it achieved competitive results compared to leading single-organ models, despite not being fine-tuned for those tasks, demonstrating strong generalizability. The source code will be publicly available at: https://github.com/DIAGNijmegen/oncology_image_registration.git.

翻译：图像配准是临床实践中分析纵向和多期相CT图像的一项基础技术。然而，现有方法大多针对单一器官应用定制，限制了其向其他解剖区域的泛化能力。本研究提出了TotalRegistrator，一种能够同时配准多个解剖区域的图像配准框架，它采用标准的UNet架构和一种新颖的形变场分解策略。该模型是轻量级的，训练仅需11GB GPU内存。为了训练和评估我们的方法，我们构建了一个大规模的纵向数据集，包含来自不同时间点采集的个体患者的695对全身（胸-腹-盆）配对CT扫描。我们将TotalRegistrator与一个通用的经典迭代算法以及一个近期提出的图像配准基础模型进行了基准测试。为了进一步评估鲁棒性和泛化能力，我们在三个外部数据集上评估了我们的模型：来自Learn2Reg挑战赛的公开胸部和腹部数据集，以及来自合作医院的私有多期相腹部数据集。在内部数据集上的实验结果表明，所提出的方法在多器官腹部配准中总体上超越了基线方法，仅在肺部对齐性能上略有下降。在分布外数据集上，尽管未针对这些任务进行微调，它仍取得了与领先的单器官模型相竞争的结果，展现了强大的泛化能力。源代码将公开于：https://github.com/DIAGNijmegen/oncology_image_registration.git。