用AI精准追踪放疗中标志物位置，自动评估呼吸暂停时的残留运动。
Marker Track: Accurate Fiducial Marker Tracking for Evaluation of Residual Motions During Breath-Hold Radiotherapy
- 基于SAM2模型和梯度图重建标志物概率体积，实现无标记自动检测。
- 在2786帧中成功检测2777帧，垂直方向误差仅0.56mm，最大偏差达7.3mm。
- 无需额外设备或辐射，适合实时调整放疗计划，推动自适应放疗发展。
锥形束CT（CBCT）投影图像中标志物位置被用于评估呼吸暂停放疗中的每日残留运动。标志物迁移增加了定位难度，因此提出一种新算法，通过投影图像的滤波梯度图重建标志物位置的三维概率体积。该方法基于Meta AI的Segment Anything Model 2（SAM 2）开发了Python算法，在一位胰腺癌患者（含两个标志物）的回顾性数据上验证。将模拟CT的三维标志物位置与CBCT重建结果对比，发现标志物间相对距离随时间减小。2777/2786个投影帧成功检测到标志物。单次屏气中标志物上下方向的标准差平均为0.56 mm，同一扫描内两次屏气间平均位置差最大达5.2 mm，首次屏气结束与第二次开始之间间隙最大达7.3 mm。利用投影位置计算出三维坐标，并确认标志物迁移现象。该方法有效生成标志物概率体积，可在不依赖专用设备、额外辐射或人工初始化的情况下实现精准标志物追踪，具备自动评估每日残留运动以调整计划范围的潜力，可作为自适应放疗工具。
原文摘要 · Abstract (English)
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.
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