Institute of Medical Informatics, University of Lübeck, D-23538 Lübeck, Germany.
Phys Med Biol. 2014 Mar 7;59(5):1147-64. doi: 10.1088/0031-9155/59/5/1147. Epub 2014 Feb 20.
Breathing-induced location uncertainties of internal structures are still a relevant issue in the radiation therapy of thoracic and abdominal tumours. Motion compensation approaches like gating or tumour tracking are usually driven by low-dimensional breathing signals, which are acquired in real-time during the treatment. These signals are only surrogates of the internal motion of target structures and organs at risk, and, consequently, appropriate models are needed to establish correspondence between the acquired signals and the sought internal motion patterns. In this work, we present a diffeomorphic framework for correspondence modelling based on the Log-Euclidean framework and multivariate regression. Within the framework, we systematically compare standard and subspace regression approaches (principal component regression, partial least squares, canonical correlation analysis) for different types of common breathing signals (1D: spirometry, abdominal belt, diaphragm tracking; multi-dimensional: skin surface tracking). Experiments are based on 4D CT and 4D MRI data sets and cover intra- and inter-cycle as well as intra- and inter-session motion variations. Only small differences in internal motion estimation accuracy are observed between the 1D surrogates. Increasing the surrogate dimensionality, however, improved the accuracy significantly; this is shown for both 2D signals, which consist of a common 1D signal and its time derivative, and high-dimensional signals containing the motion of many skin surface points. Eventually, comparing the standard and subspace regression variants when applied to the high-dimensional breathing signals, only small differences in terms of motion estimation accuracy are found.
呼吸引起的内部结构位置不确定性仍然是胸部和腹部肿瘤放射治疗中的一个相关问题。门控或肿瘤跟踪等运动补偿方法通常由实时采集的低维呼吸信号驱动。这些信号只是目标结构和危险器官内部运动的替代物，因此需要适当的模型来建立所采集的信号与所寻求的内部运动模式之间的对应关系。在这项工作中，我们提出了一种基于对数欧几里得框架和多元回归的对应建模的微分同胚框架。在该框架中，我们系统地比较了标准和子空间回归方法（主成分回归、偏最小二乘法、典型相关分析）对于不同类型的常见呼吸信号（1D：肺活量计、腹部带、膈肌跟踪；多维：皮肤表面跟踪）。实验基于 4DCT 和 4DMR 数据集，涵盖了周期内和周期间以及会话内和会话间的运动变化。在内部运动估计准确性方面，1D 替代品之间仅观察到很小的差异。然而，增加替代物的维度显著提高了准确性；这对于由共同的 1D 信号及其时间导数组成的 2D 信号以及包含许多皮肤表面点运动的高维信号都适用。最终，将标准和子空间回归变体应用于高维呼吸信号时进行比较，仅发现运动估计准确性方面的微小差异。
