iPhantom：一个自动创建个体化计算体模的框架及其在CT器官剂量学中的应用
iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry
摘要：
目的：本研究的目的是开发和验证一种新的框架，iPhantom，自动创建患者特定的幻影或数字双胞胎（DT）使用患者的医学图像。该框架适用于评估辐射剂量的放射敏感器官在CT成像的个人患者。方法：iPhantom使用为多器官CT分割开发的基于学习的模型，从患者CT图像中分割选定的锚定器官（例如肝脏、骨骼、胰腺）。具有挑战性的器官分割（例如肠）从匹配的体模模板，使用为多器官体模体素开发的仿射配准模型。由此产生的全患者体模用于评估常规CT检查期间的器官剂量。结果：iPhantom在XCAT（n=50）和独立临床（n=10）数据集上均得到验证，准确度相似。iPhantom精确地预测了所有器官位置，具有良好的准确度，对于锚定器官，Dice相似系数（DSC）>0.6，对于所有其他器官，DSC为0.3-0.9。iPhantom对大多数器官显示出小于10%的剂量误差，这明显优于最先进的基线方法（20-35%的剂量误差）。结论：iPhantom能够自动和准确地创建患者特定的体模，并首次为CT剂量测定提供充分和自动的患者特定剂量估计。重要性：新框架通过自动化将CHP的创建和应用提升到单个CHP的水平，实现更广泛和精确的器官定位，为临床监测、个性化优化和大规模研究铺平道路。
英文摘要：
Objective: This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or digital-twins (DT) using patient medical images. The framework is applied to assess radiation dose to radiosensitive organs in CT imaging of individual patients. Method: From patient CT images, iPhantom segments selected anchor organs (e.g. liver, bones, pancreas) using a learning-based model developed for multi-organ CT segmentation. Organs challenging to segment (e.g. intestines) are incorporated from a matched phantom template, using a diffeomorphic registration model developed for multi-organ phantom-voxels. The resulting full-patient phantoms are used to assess organ doses during routine CT exams. Result: iPhantom was validated on both the XCAT (n=50) and an independent clinical (n=10) dataset with similar accuracy. iPhantom precisely predicted all organ locations with good accuracy of Dice Similarity Coefficients (DSC) >0.6 for anchor organs and DSC of 0.3-0.9 for all other organs. iPhantom showed less than 10% dose errors for the majority of organs, which was notably superior to the state-of-the-art baseline method (20-35% dose errors). Conclusion: iPhantom enables automated and accurate creation of patient-specific phantoms and, for the first time, provides sufficient and automated patient-specific dose estimates for CT dosimetry. Significance: The new framework brings the creation and application of CHPs to the level of individual CHPs through automation, achieving a wider and precise organ localization, paving the way for clinical monitoring, and personalized optimization, and large-scale research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
