Statistical deformation reconstruction using multi-organ shape features for pancreatic cancer localization
Statistical deformation reconstruction using multi-organ shape features for pancreatic cancer localization
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DOI：
10.1016/j.media.2020.101829
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发表时间：
2021-01-01
影响因子：
10.9
通讯作者：
Matsuda, Tetsuya
中科院分区：
文献类型：
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作者：
Nakao, Megumi;Nakamura, Mitsuhiro;Matsuda, Tetsuya
呼吸运动以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是复杂的，并没有统计公式，而单器官变形已被广泛研究。本文介绍了一个多器官变形库及其在基于腹部多器官形状特征的变形重建中的应用。统计多器官运动/变形模型的胃，肝，左，右肾，和十二指肠的形状匹配的区域标签上定义的四维计算机断层扫描图像。共测量了25例胰腺癌患者的250个体积。本文还提出了一种基于区域的变形学习，使用非线性核模型来预测胰腺癌的自适应放射治疗的位移。实验结果表明，所提出的概念比一般的基于每个患者的学习模型更好地估计变形，并实现了临床上可接受的估计误差，在整个呼吸运动中平均距离为1.2 +/- 0.7 mm，Hausdorff距离为4.2 +/- 2.3 mm。(C)2020作者（S）由爱思唯尔公司出版
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. (C) 2020 The Author(s). Published by Elsevier B.V.