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Enhancing liver tumor localization accuracy by prior-knowledge-guided motion modeling and a biomechanical model

机译:通过先验知识指导的运动建模和生物力学模型提高肝脏肿瘤定位的准确性

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摘要

BackgroundPre-treatment liver tumor localization remains a challenging task for radiation therapy, mostly due to the limited tumor contrast against normal liver tissues, and the respiration-induced liver tumor motion. Recently, we developed a biomechanical modeling-based, deformation-driven cone-beam CT estimation technique (Bio-CBCT), which achieved substantially improved accuracy on low-contrast liver tumor localization. However, the accuracy of Bio-CBCT is still affected by the limited tissue contrast around the caudal liver boundary, which reduces the accuracy of the boundary condition that is fed into the biomechanical modeling process. In this study, we developed a motion modeling and biomechanical modeling-guided CBCT estimation technique (MM-Bio-CBCT), to further improve the liver tumor localization accuracy by incorporating a motion model into the CBCT estimation process.
机译:背景预处理肝肿瘤的定位对于放射治疗仍然是一项艰巨的任务,这主要是由于与正常肝组织的肿瘤对比有限,以及呼吸引起的肝肿瘤运动。最近,我们开发了一种基于生物力学建模的,变形驱动的锥形束CT估计技术(Bio-CBCT),该技术在低对比度肝肿瘤定位方面获得了显着提高的准确性。但是,Bio-CBCT的准确性仍然受到尾肝边界周围有限的组织对比度的影响,这降低了输入到生物力学建模过程中的边界条件的准确性。在这项研究中,我们开发了运动建模和生物力学建模指导的CBCT估计技术(MM-Bio-CBCT),以通过将运动模型纳入CBCT估计过程来进一步提高肝脏肿瘤定位的准确性。

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