Chen Dong, Xie Hongzhi, Zhang Shuyang, Gu Lixu
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
Phys Med Biol. 2017 Sep 21;62(19):7855-7873. doi: 10.1088/1361-6560/aa8841.
Respiration-introduced tumor location uncertainty is a challenge in the precise lung biopsy for lung lesions. Current statistical modeling approaches hardly capture the complex local respiratory motion information. In this study, we formulate a statistical respiratory motion model using biplane x-ray images to improve the accuracy of motion field estimation by efficiently preserving local motion details for specific patients. Given CT data sets of 18 healthy subjects at end-expiratory and end-inspiratory breathing phases, the respiratory motion field is constructed based on deformation vector fields which are extracted from these CT data sets, and a lung contour motion repository respiratory is generated dependent on displacements of boundary control points. By varying the sparse weight coefficients of the statistical sparse motion field presentation (SMFP) method, the newly-input motion field is approximately presented by a sparse linear combination of a subset of the motion repository. The SMFP method is employed twice in the coefficient optimization process. Finally, these non-zero coefficients are fine-tuned to maximize the similarity between the projection image of reconstructed volumetric images and the current x-ray image. We performed the proposed method for estimating respiratory motion field on ten subject datasets and compared the result with the PCA method. The maximum average target registration error of the PCA-based and the SMFP-based respiratory motion field estimation are 3.1(2.0) and 2.9(1.6) mm, respectively. The maximum average symmetric surface distance of two methods are 2.5(1.6) and 2.4(1.3) mm, respectively.
呼吸引入的肿瘤位置不确定性是肺部病变精确肺活检中的一个挑战。当前的统计建模方法很难捕捉复杂的局部呼吸运动信息。在本研究中，我们使用双平面X射线图像制定了一种统计呼吸运动模型，通过有效保留特定患者的局部运动细节来提高运动场估计的准确性。给定18名健康受试者在呼气末和吸气末呼吸阶段的CT数据集，基于从这些CT数据集中提取的变形矢量场构建呼吸运动场，并根据边界控制点的位移生成肺部轮廓运动库。通过改变统计稀疏运动场表示（SMFP）方法的稀疏权重系数，新输入的运动场由运动库子集中的稀疏线性组合近似表示。在系数优化过程中两次使用SMFP方法。最后，对这些非零系数进行微调，以最大化重建体积图像的投影图像与当前X射线图像之间的相似度。我们在十个受试者数据集上执行了所提出的估计呼吸运动场的方法，并将结果与主成分分析（PCA）方法进行了比较。基于PCA和基于SMFP的呼吸运动场估计的最大平均目标配准误差分别为3.1(2.0)和2.9(1.6)毫米。两种方法的最大平均对称表面距离分别为2.5(1.6)和2.4(1.3)毫米。
