Phys Med Biol. 2013 Nov 21;58(22):8077-97. doi: 10.1088/0031-9155/58/22/8077. Epub 2013 Oct 31.
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. Organ DICE indices (indicating the degree of overlap between registered organs) and residual misalignments of the fiducial landmarks were quantified. Manual organ delineation on CBCT images varied significantly among physicians with overall mean DICE index of only 0.7 among redundant contours. Seminal vesicle contours were found to have the lowest correlation amongst physicians (DICE = 0.5). After DIR, the organ surfaces between CBCT and planning CT were in good alignment with mean DICE indices of 0.9 for prostate, rectum, and bladder, and 0.8 for seminal vesicles. The Jacobian magnitudes |JAC| in the prostate, rectum, and seminal vesicles were in the range of 0.4-1.5, indicating mild compression/expansion. The bladder volume differences were larger between CBCT and CT images with mean |JAC| values of 2.2, 0.7, and 1.0 for three respective patients. Bone deformation was negligible (|JAC| = ∼ 1.0). The difference between corresponding landmark points between CBCT and CT was less than 1.0 mm after DIR. We have presented a novel method of establishing benchmark DIR accuracy between CT and CBCT images in the pelvic region. The method incorporates manually delineated organ surfaces and landmark points as well as pixel similarity in the optimization, while ensuring bone rigidity and avoiding excessive deformation in soft tissue organs. Redundant contouring is necessary to reduce the overall registration uncertainty.
形变图像配准(DIR)是自适应放射治疗的一个组成部分。然而,由于直肠和膀胱充盈度的每日显著变化以及锥形束 CT(CBCT)图像中噪声水平的增加,每日 CBCT 与治疗计划 CT 之间的精确配准具有挑战性。另一个重大挑战是临床环境中缺乏“真实”配准,这对于各种配准算法的定量评估是必要的。本研究的目的是建立临床患者数据的基准配准。为此,选择了三对 CT/CBCT 数据集进行了这项经机构审查委员会批准的回顾性研究。在每张图像上,为了减少轮廓不确定性,由五名医生手动描绘了十组独立的器官。每个图像的平均轮廓集是从十个轮廓中得出的。还手动识别了一组独特的点(圆形自然钙化和三个植入的前列腺基准标记)。然后,将平均轮廓和特征点作为约束纳入基于 B 样条的 DIR 算法中。此外,还对股骨和骨盆施加刚性惩罚以保持其刚性。采用分段刚性配准方法来考虑股骨姿势和骨骼之间滑动运动的差异。对于每个注册,计算空间雅可比行列式的大小(|JAC|)以量化组织的压缩和扩张。还通过视觉审查变形网格和基于有限元模型的不平衡能量图来评估所得变形的物理合理性。量化了器官 DICE 指数(表示注册器官之间的重叠程度)和基准标记的残余未对准程度。CBCT 图像上的器官手动描绘在医生之间差异很大,冗余轮廓的总体平均 DICE 指数仅为 0.7。精囊腺轮廓在医生之间的相关性最低(DICE=0.5)。DIR 后,CBCT 和计划 CT 之间的器官表面对齐良好,前列腺、直肠和膀胱的平均 DICE 指数为 0.9,精囊腺为 0.8。前列腺、直肠和精囊腺的雅可比行列式大小|JAC|在 0.4-1.5 范围内,表明有轻度压缩/扩张。膀胱体积差异在 CBCT 和 CT 图像之间较大,三个患者的平均|JAC|值分别为 2.2、0.7 和 1.0。骨骼变形可忽略不计(|JAC|=∼1.0)。DIR 后,CBCT 和 CT 之间对应标记点之间的差异小于 1.0 毫米。我们提出了一种新的方法,用于在骨盆区域建立 CT 和 CBCT 图像之间的基准 DIR 准确性。该方法在优化中结合了手动描绘的器官表面和标记点以及像素相似度,同时确保骨骼刚性并避免软组织器官过度变形。冗余轮廓是减少整体注册不确定性所必需的。