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A novel approach for establishing benchmark CBCT/CT deformable image registrations in prostate cancer radiotherapy

机译:在前列腺癌放疗中建立基准CBCT / CT可变形图像配准的新方法

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Deformable image registration (DIR) is an integral component for adaptive radiation therapy. However, accurate registration between daily cone-beam computed tomography (CBCT) and treatment planning CT is challenging, due to significant daily variations in rectal and bladder fillings as well as the increased noise levels in CBCT images. Another significant challenge is the lack of 'ground-truth' registrations in the clinical setting, which is necessary for quantitative evaluation of various registration algorithms. The aim of this study is to establish benchmark registrations of clinical patient data. Three pairs of CT/CBCT datasets were chosen for this institutional review board approved retrospective study. On each image, in order to reduce the contouring uncertainty, ten independent sets of organs were manually delineated by five physicians. The mean contour set for each image was derived from the ten contours. A set of distinctive points (round natural calcifications and three implanted prostate fiducial markers) were also manually identified. The mean contours and point features were then incorporated as constraints into a B-spline based DIR algorithm. Further, a rigidity penalty was imposed on the femurs and pelvic bones to preserve their rigidity. A piecewise-rigid registration approach was adapted to account for the differences in femur pose and the sliding motion between bones. For each registration, the magnitude of the spatial Jacobian (|JAC|) was calculated to quantify the tissue compression and expansion. Deformation grids and finite-element-model-based unbalanced energy maps were also reviewed visually to evaluate the physical soundness of the resultant deformations.
机译:可变形图像配准(DIR)是自适应放射治疗不可或缺的组成部分。然而,由于直肠和膀胱充盈的每日显着变化以及CBCT图像中噪声水平的增加,每日锥形束计算机断层扫描(CBCT)和治疗计划CT之间的准确配准具有挑战性。另一个重大挑战是在临床环境中缺少“真实的”配准,这对于定量评估各种配准算法是必需的。这项研究的目的是建立临床患者数据的基准注册。为此机构审查委员会批准的回顾性研究选择了三对CT / CBCT数据集。在每个图像上,为了减少轮廓的不确定性,由五位医生手动绘制了十组独立的器官。每个图像的平均轮廓集​​是从十个轮廓中得出的。还手动识别出一组独特点(圆形自然钙化和三个植入的前列腺基准标记)。然后将平均轮廓和点特征作为约束并入基于B样条的DIR算法中。另外,对股骨和骨盆骨骼施加刚性惩罚以保持其刚性。分段刚性配准方法适用于解决股骨姿势和骨骼之间滑动运动的差异。对于每次配准,计算空间雅可比矩阵(| JAC |)的大小以量化组织压缩和扩展。还目视检查了变形网格和基于有限元模型的不平衡能量图,以评估所得变形的物理可靠性。

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