标题： 标记跟踪：用于呼吸暂停放疗中残余运动评估的精确标志物跟踪 显示英文标题

标题： Marker Track: Accurate Fiducial Marker Tracking for Evaluation of Residual Motions During Breath-Hold Radiotherapy

摘要： 在锥形束计算机断层扫描（CBCT）投影图像中的定位标记位置已被研究，以评估屏气放疗期间的每日残留运动。 定位标记迁移给准确定位标记带来了挑战，促使开发了一种新算法，该算法从投影的滤波梯度图中重建标记位置的体积概率图。 这引导开发了一个基于Python的算法，使用Meta AI的Segment Anything Model 2（SAM 2）在投影图像中检测定位标记。 对一名有2个定位标记的胰腺癌患者的回顾性数据进行了分析。 从模拟计算机断层扫描（CT）获得的三维（3D）标记位置与从CBCT图像重建的位置进行比较，显示出随着时间推移标记之间的相对距离减少。 在2786个投影帧中成功检测到2777个定位标记。 每次屏气的上下方向（SI）标记位置的平均标准偏差为0.56毫米，同一扫描中两次屏气之间的平均SI位置差异高达5.2毫米，第一次屏气结束和第二次屏气开始之间相差高达7.3毫米。 使用投影位置计算三维（3D）标记位置，并确认了标记迁移。 该方法有效计算标记概率体积，并在治疗过程中实现准确的定位标记跟踪，而无需任何专用设备、额外辐射剂量或手动初始化和标记。 它在自动评估每日残留运动以调整计划边缘方面具有重要意义，可作为自适应放疗工具。 显示英文摘要

摘要： Fiducial marker positions in projection image of cone-beam computed tomography (CBCT) scans have been studied to evaluate daily residual motion during breath-hold radiation therapy. Fiducial marker migration posed challenges in accurately locating markers, prompting the development of a novel algorithm that reconstructs volumetric probability maps of marker locations from filtered gradient maps of projections. This guides the development of a Python-based algorithm to detect fiducial markers in projection images using Meta AI's Segment Anything Model 2 (SAM 2). Retrospective data from a pancreatic cancer patient with two fiducial markers were analyzed. The three-dimensional (3D) marker positions from simulation computed tomography (CT) were compared to those reconstructed from CBCT images, revealing a decrease in relative distances between markers over time. Fiducial markers were successfully detected in 2777 out of 2786 projection frames. The average standard deviation of superior-inferior (SI) marker positions was 0.56 mm per breath-hold, with differences in average SI positions between two breath-holds in the same scan reaching up to 5.2 mm, and a gap of up to 7.3 mm between the end of the first and beginning of the second breath-hold. 3D marker positions were calculated using projection positions and confirmed marker migration. This method effectively calculates marker probability volume and enables accurate fiducial marker tracking during treatment without requiring any specialized equipment, additional radiation doses, or manual initialization and labeling. It has significant potential for automatically assessing daily residual motion to adjust planning margins, functioning as an adaptive radiation therapy tool.