Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, USA.
Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, USA.
Radiother Oncol. 2019 Apr;133:183-192. doi: 10.1016/j.radonc.2018.10.040. Epub 2018 Nov 14.
To improve the accuracy of liver tumor localization, this study tests a biomechanical modeling-guided liver cone-beam CT (CBCT) estimation (Bio-CBCT-est) technique, which generates new CBCTs by deforming a prior high-quality CT or CBCT image using deformation vector fields (DVFs). The DVFs can be used to propagate tumor contours from the prior image to new CBCTs for automatic 4D tumor localization.
METHODS/MATERIALS: To solve the DVFs, the Bio-CBCT-est technique employs an iterative scheme that alternates between intensity-driven 2D-3D deformation and biomechanical modeling-guided DVF regularization and optimization. The 2D-3D deformation step solves DVFs by matching digitally reconstructed radiographs of the 3D deformed prior image to 2D phase-sorted on-board projections according to imaging intensities. This step's accuracy is limited at low-contrast intra-liver regions without sufficient intensity variations. To boost the DVF accuracy in these regions, we use the intensity-driven DVFs solved at higher-contrast liver boundaries to fine-tune the intra-liver DVFs by finite element analysis-based biomechanical modeling. We evaluated Bio-CBCT-est's accuracy with seven liver cancer patient cases. For each patient, we simulated 4D cone-beam projections from 4D-CT images, and used these projections for Bio-CBCT-est based image estimations. After Bio-CBCT-est, the DVF-propagated liver tumor/cyst contours were quantitatively compared with the manual contours on the original 4D-CT 'reference' images, using the DICE similarity index, the center-of-mass-error (COME), the Hausdorff distance (HD) and the voxel-wise cross-correlation (CC) metrics. In addition to simulation, we also performed a preliminary study to qualitatively evaluate the Bio-CBCT-est technique via clinically acquired cone beam projections. A quantitative study using an in-house deformable liver phantom was also performed.
Using 20 projections for image estimation, the average (±s.d.) DICE index increased from 0.48 ± 0.13 (by 2D-3D deformation) to 0.77 ± 0.08 (by Bio-CBCT-est), the average COME decreased from 7.7 ± 1.5 mm to 2.2 ± 1.2 mm, the average HD decreased from 10.6 ± 2.2 mm to 5.9 ± 2.0 mm, and the average CC increased from -0.004 ± 0.216 to 0.422 ± 0.206. The tumor/cyst trajectory solved by Bio-CBCT-est matched well with that manually obtained from 4D-CT reference images.
Bio-CBCT-est substantially improves the accuracy of 4D liver tumor localization via cone-beam projections and a biomechanical model.
为了提高肝脏肿瘤定位的准确性，本研究测试了一种基于生物力学模型的肝脏锥形束 CT（CBCT）估计（Bio-CBCT-est）技术，该技术通过变形向量场（DVF）将高质量 CT 或 CBCT 图像变形，生成新的 CBCT。DVF 可用于将肿瘤轮廓从原始图像传播到新的 CBCT 中，以实现自动 4D 肿瘤定位。
方法/材料：为了解决 DVF 问题，Bio-CBCT-est 技术采用了一种迭代方案，交替进行基于强度的 2D-3D 变形和基于生物力学模型的 DVF 正则化和优化。2D-3D 变形步骤通过根据成像强度将三维变形前图像的数字重建射线图与二维相位排序的机载投影匹配，求解 DVF。此步骤在没有足够强度变化的低对比度肝内区域的准确性受到限制。为了提高这些区域的 DVF 精度，我们使用在具有较高对比度的肝边界处求解的基于强度的 DVF 通过基于有限元分析的生物力学模型对肝内 DVF 进行微调。我们使用七个肝癌患者病例评估了 Bio-CBCT-est 的准确性。对于每个患者，我们从 4D-CT 图像模拟 4D 锥形束投影，并使用这些投影进行基于 Bio-CBCT-est 的图像估计。在 Bio-CBCT-est 之后，使用 DICE 相似性指数、质心误差（COME）、Hausdorff 距离（HD）和体素交叉相关（CC）度量，将 DVF 传播的肝肿瘤/囊肿轮廓与原始 4D-CT“参考”图像上的手动轮廓进行定量比较。除了模拟之外，我们还通过临床采集的锥形束投影进行了初步研究，定性评估了 Bio-CBCT-est 技术。还使用内部可变形肝脏体模进行了定量研究。
使用 20 个投影进行图像估计，平均（±标准差）DICE 指数从 0.48±0.13（通过 2D-3D 变形）增加到 0.77±0.08（通过 Bio-CBCT-est），平均 COME 从 7.7±1.5mm 减少到 2.2±1.2mm，平均 HD 从 10.6±2.2mm 减少到 5.9±2.0mm，平均 CC 从-0.004±0.216 增加到 0.422±0.206。Bio-CBCT-est 求解的肿瘤/囊肿轨迹与从 4D-CT 参考图像手动获得的轨迹很好地匹配。
Bio-CBCT-est 通过锥形束投影和生物力学模型大大提高了 4D 肝脏肿瘤定位的准确性。
