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Fast Explicit Diffusion for Registration with Direction-Dependent Regularization

机译:快速显式扩散,用于注册方向依赖正规化

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The accurate estimation of respiratory lung motion by nonlinear registration is currently an important topic of research and required for many applications in pulmonary image analysis, e.g. for radiotherapy treatment planning. A special challenge for lung registration is the sliding motion between visceral an parietal pleurae during breathing, which causes discontinuities in the motion field. It has been shown that accounting for this physiological aspect by modeling the sliding motion using a direction-dependent regularization approach can significantly improve registration results. While the potential of such physiology-based regularization methods has been demonstrated in several publications, so far only simple explicit solution schemes were applied due to the computational complexity. In this paper, a numerical solution of the direction-dependent regularization based on Fast Explicit Diffusion (FED) is presented. The approach is tested for motion estimation on 23 thoracic CT images and a significant improvement over the classic explicit solution is shown.
机译:非线性登记的准确估计呼吸肺部运动是目前对肺图像分析中的许多应用的重要课题,例如,肺图像分析中的许多应用。用于放射治疗计划。肺部注册的特殊挑战是呼吸期间内脏胸膜胸膜内的滑动运动,这导致运动场中的不连续性。已经表明,通过使用方向依赖的正则化方法建模滑动运动来核对这种生理方面可以显着改善登记结果。虽然已经在几种出版物中证明了这种基于生理的正则化方法的潜力,但到目前为止,由于计算复杂性,仅应用简单的显式解决方案。本文提出了一种基于快速显式扩散(FED)的方向依赖正规化的数值解。该方法是测试23胸CT图像的运动估计,并显示了经典显式解决方案的显着改进。

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