Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo, Kyoto 606-8501, Japan.
Human Health Sciences, Graduate School of Medicine, Kyoto University, Shogoin-Kawahara-cho, Sakyo, Kyoto 606-8507, Japan.
Med Image Anal. 2021 Jan;67:101829. doi: 10.1016/j.media.2020.101829. Epub 2020 Oct 10.
Respiratory motion and the associated deformations of abdominal organs and tumors are essential information in clinical applications. However, inter- and intra-patient multi-organ deformations are complex and have not been statistically formulated, whereas single organ deformations have been widely studied. In this paper, we introduce a multi-organ deformation library and its application to deformation reconstruction based on the shape features of multiple abdominal organs. Statistical multi-organ motion/deformation models of the stomach, liver, left and right kidneys, and duodenum were generated by shape matching their region labels defined on four-dimensional computed tomography images. A total of 250 volumes were measured from 25 pancreatic cancer patients. This paper also proposes a per-region-based deformation learning using the non-linear kernel model to predict the displacement of pancreatic cancer for adaptive radiotherapy. The experimental results show that the proposed concept estimates deformations better than general per-patient-based learning models and achieves a clinically acceptable estimation error with a mean distance of 1.2 ± 0.7 mm and a Hausdorff distance of 4.2 ± 2.3 mm throughout the respiratory motion.
呼吸运动及腹部器官和肿瘤的相关变形是临床应用中的重要信息。然而，患者间和患者内的多器官变形较为复杂，尚未进行统计学描述，而单一器官变形已得到广泛研究。本文引入了一个多器官变形库，并基于多个腹部器官的形状特征，将其应用于变形重建。通过对四维 CT 图像上的区域标签进行形状匹配，生成了胃、肝、左肾、右肾和十二指肠的统计多器官运动/变形模型。本文还提出了一种基于区域的变形学习方法，使用非线性核模型来预测胰腺癌的位移，以实现自适应放疗。实验结果表明，与一般基于每个患者的学习模型相比，所提出的概念能更好地估计变形，且在整个呼吸运动过程中，其平均距离为 1.2 毫米±0.7 毫米，Hausdorff 距离为 4.2 毫米±2.3 毫米，达到了临床可接受的估计误差。
