Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.
Phys Med Biol. 2010 Sep 21;55(18):5585-98. doi: 10.1088/0031-9155/55/18/021. Epub 2010 Aug 31.
Methods that allow online lung tumor tracking during radiotherapy are desirable for a variety of applications that have the potential to vastly improve treatment accuracy, dose conformity and sparing of healthy tissue. Several publications have proposed the use of an on-board kV x-ray imager to assess the tumor location during treatment. However, there is some concern that this strategy may expose the patient to a significant amount of additional dose over the course of a typical radiotherapy treatment. In this paper we present an algorithm that utilizes the on-board portal imager of the treatment machine to track lung tumors. This does not expose the patient to additional dose, but is somewhat more challenging as the quality of portal images is inferior when compared to kV x-ray images. To quantify the performance of the proposed algorithm we retrospectively applied it to portal image sequences retrieved from a dynamic chest phantom study and an SBRT treatment performed at our institution. The results were compared to manual tracking by an expert. For the phantom data the tracking error was found to be smaller than 1 mm and for the patient data smaller than 2 mm, which was in the same range as the uncertainty of the gold standard.
方法，允许在线肺肿瘤跟踪放疗是可取的各种应用，有可能极大地提高治疗的准确性，剂量一致性和节约健康组织。有几个出版物提出使用机载千伏 X 射线成像仪来评估肿瘤的位置在治疗过程中。然而，人们担心这种策略可能会使患者在典型的放射治疗过程中暴露于大量额外的剂量。在本文中，我们提出了一种利用治疗机的机载门成像仪来跟踪肺肿瘤的算法。这不会使患者暴露于额外的剂量，但有点更具挑战性，因为与千伏 X 射线图像相比，门图像的质量较差。为了定量评估所提出算法的性能，我们回顾性地将其应用于从动态胸部体模研究和我们机构进行的 SBRT 治疗中获取的门图像序列。结果与专家的手动跟踪进行了比较。对于体模数据，跟踪误差被发现小于 1 毫米，对于患者数据小于 2 毫米，与金标准的不确定性处于同一范围。
