Chao Ming, Schreibmann Eduard, Li Tianfang, Wink Nicole, Xing Lei
Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California 94305-5847, USA.
Med Phys. 2007 Oct;34(10):4023-9. doi: 10.1118/1.2780105.
The purpose of this work is to develop a novel strategy to automatically map organ contours from one phase of respiration to all other phases on a four-dimensional computed tomography (4D CT). A region of interest (ROI) was manually delineated by a physician on one phase specific image set of a 4D CT. A number of cubic control volumes of the size of approximately 1 cm were automatically placed along the contours. The control volumes were then collectively mapped to the next phase using a rigid transformation. To accommodate organ deformation, a model-based adaptation of the control volume positions was followed after the rigid mapping procedure. This further adjustment of control volume positions was performed by minimizing an energy function which balances the tendency for the control volumes to move to their correspondences with the desire to maintain similar image features and shape integrity of the contour. The mapped ROI surface was then constructed based on the central positions of the control volumes using a triangulated surface construction technique. The proposed technique was assessed using a digital phantom and 4D CT images of three lung patients. Our digital phantom study data indicated that a spatial accuracy better than 2.5 mm is achievable using the proposed technique. The patient study showed a similar level of accuracy. In addition, the computational speed of our algorithm was significantly improved as compared with a conventional deformable registration-based contour mapping technique. The robustness and accuracy of this approach make it a valuable tool for the efficient use of the available spatial-tempo information for 4D simulation and treatment.
这项工作的目的是开发一种新策略，用于在四维计算机断层扫描（4D CT）上自动将一个呼吸阶段的器官轮廓映射到所有其他阶段。由医生在4D CT的一个特定阶段图像集上手动勾勒出感兴趣区域（ROI）。沿着轮廓自动放置一些大小约为1厘米的立方控制体积。然后使用刚性变换将这些控制体积集体映射到下一个阶段。为了适应器官变形，在刚性映射过程之后采用基于模型的控制体积位置调整。通过最小化一个能量函数来进一步调整控制体积位置，该能量函数平衡了控制体积移动到其对应位置的趋势与保持轮廓的相似图像特征和形状完整性的愿望。然后使用三角化表面构建技术基于控制体积的中心位置构建映射的ROI表面。使用数字体模和三名肺癌患者的4D CT图像对所提出的技术进行了评估。我们的数字体模研究数据表明，使用所提出的技术可实现优于2.5毫米的空间精度。患者研究显示了类似的精度水平。此外，与传统的基于可变形配准的轮廓映射技术相比，我们算法的计算速度有了显著提高。这种方法的稳健性和准确性使其成为有效利用4D模拟和治疗中可用的时空信息的有价值工具。
