Quality assurance solutions to complement available motion compensation technologies are central for their safe routine implementation and success of treatment. This work presents a dense feature-based method for soft-tissue tumor motion estimation in megavoltage (MV) beam’s-eye-view (BEV) projections for potential intra-treatment monitoring during dynamic tumor tracking (DTT). Dense sampling and matching principles were employed to track a gridded set of features landmarks (FLs) in MV-BEV projections and estimate tumor motion, capable to overcome reduced field aperture and partial occlusion challenges. The algorithm’s performance was evaluated by retrospectively applying it to fluoroscopic sequences acquired at ∼2 frames s⁻¹ (fps) for a dynamic phantom and two lung stereotactic body radiation therapy (SBRT) patients treated with DTT on the Vero SBRT system. First, a field-specific train image is initialized by sampling the tumor region at, S, pixel intervals on a grid using a representative frame from a stream of query frames. Sampled FLs are locally characterized in the form of descriptor vectors and geometric attributes representing the target. For motion tracking, subsequent query frames are likewise sampled, corresponding feature descriptors determined, and then patch-wise matched to the training set based on their descriptors and geometric relationships. FLs with high correspondence are pruned and used to estimate tumor displacement. In scenarios of partial occlusions, position is estimated from the set of correctly (visible) FLs on past observations. Reconstructed trajectories were benchmarked against ground-truth manual tracking using the root-mean-square (RMS) as a metric of positional accuracy. A total of 19 fluoroscopy sequences were analyzed. This included scenarios of field aperture obstruction during three-dimensional conformal, as well as step-and-shoot intensity modulated radiotherapy (IMRT) delivery assisted with DTT. The algorithm resolved target motion satisfactorily. The RMS was <1.2 mm and <1.8 mm for the phantom and the clinical dataset, respectively. Dense tracking showed promising results to overcome localization challenges at the field penumbra and partial obstruction by multi-leaf collimator (MLC). Motion retrieval was possible in ∼66% of the control points studied. In addition to MLC obstruction, changes in the external/internal breathing dynamics and baseline drifts were a major source of estimation bias. Dense feature-based tracking is a viable alternative. The algorithm is rotation-/scale-invariant and robust to photometric changes. Tracking multiple features may help overcome partial occlusion challenges by the MLC. This in turn opens up new possibilities for motion detection and intra-treatment monitoring during IMRT and potentially VMAT.

补充现有运动补偿技术的质量保证解决方案对于安全常规实施和治疗成功至关重要。这项工作提出了一种基于密集特征的方法，用于在兆电压 (MV) 光束的眼睛视图 (BEV) 投影中进行软组织肿瘤运动估计，以便在动态肿瘤跟踪 (DTT) 期间进行潜在的治疗内监测。采用密集采样和匹配原理来跟踪 MV-BEV 投影中的一组网格特征标志 (FL) 并估计肿瘤运动，从而能够克服减小的视野孔径和部分遮挡的挑战。该算法的性能通过回顾性应用到在 ∼2 帧 s ^{-1获得的透视序列来评估}(fps) 用于在 Vero SBRT 系统上接受 DTT 治疗的动态体模和两名肺部立体定向放射治疗 (SBRT) 患者。首先，通过使用来自查询流的代表性帧在网格上以S像素间隔对肿瘤区域进行采样来初始化特定领域的训练图像帧。采样的 FL 以描述符向量和表示目标的几何属性的形式进行局部表征。对于运动跟踪，同样对后续查询帧进行采样，确定相应的特征描述符，然后根据它们的描述符和几何关系对训练集进行补丁匹配。高度对应的 FL 被修剪并用于估计肿瘤位移。在部分遮挡的情况下，位置是根据过去观察的正确（可见）FL 集估计的。使用均方根 (RMS) 作为位置精度的度量，将重建的轨迹与地面实况手动跟踪进行基准测试。共分析了 19 个透视序列。这包括三维保形期间的视场孔径阻塞场景，以及由 DTT 辅助的步进式调强放射治疗 (IMRT) 递送。该算法令人满意地解决了目标运动。体模和临床数据集的 RMS 分别为 <1.2 mm 和 <1.8 mm。密集跟踪显示出有希望的结果，可以克服多叶准直器 (MLC) 的半影和部分障碍的定位挑战。在所研究的控制点中，大约 66% 的运动检索是可能的。除了 MLC 阻塞之外，外部/内部呼吸动力学和基线漂移的变化是估计偏差的主要来源。基于密集特征的跟踪是一种可行的替代方案。该算法是旋转/比例不变的，并且对光度变化具有鲁棒性。跟踪多个特征可能有助于克服 MLC 的部分遮挡挑战。