Deformable Image Registration and Reconstruction

最佳的图像引导自适应放射治疗需要患者解剖结构的4D表示， 允许肿瘤和正常组织体素的位置通过生物学过程被跟踪 成像、计划和模拟、近距离放射治疗的递送以及每个IMRT部分的管理。的 本项目的科学目标是研究非刚性图像配准的新方法， 当患者的解剖结构在治疗期间改变时， 过程实际目标是创建一套图像处理资源， 图像引导自适应放射治疗技术在临床中的应用。具体目标1： 研究用于标测高剂量近距离放射治疗（HDR）轮廓驱动变形配准方法 将骨盆中的剂量分布与IMRT剂量分布进行比较，并将生物图像配准到外部 光束规划图像，包括开发一种新的表面匹配算法， 轮廓不确定性。为了有效地将来自计划CT图像的信息映射到机载CT图像， 在管理每个每日部分之前获取，我们将开发快速参数化图像变形 不需要手动轮廓标志的算法。具体目标2。我们将调查小说 通过匹配变形模型从不完全投影数据重建CT图像的方法， 平面图像投影序列，从而集成图像重建和可变形配准 变成一个单一的过程。这将用于开发患者呼吸的4D解剖表示， 提高时间分辨率，并从更高的时间分辨率估计分次内解剖变形 分辨率序列的2D图像。最后，在具体目标3中， 变形图像的配准误差也会增大。

Optimal image guided adaptive radiotherapy requires a 4D representation of the patients anatomy, that allows the position of tumor and normal tissue voxels to be tracked through the processes of biological imaging, planning and simulation, delivery of brachytherapy, and administration of each IMRT fraction. The scientific objective of this project is to investigate novel methods of nonrigid image registration for constructing and validating such representations of the patient's anatomy as it changes during the treatment process. The practical goal is to create a suite of image processing resources that will enable the routine application of image-guided adaptive radiotherapy techniques in the clinic. In specific aim 1, we will investigate contour-driven deformable registration methods for mapping high-dose brachytherapy (HDR) dose distributions in the pelvis to IMRT dose distributions, and for registering biological images to external beam planning images, including development of a novel surface matching algorithm that accounts for contouring uncertainties. To efficiently map information from planning CT images to onboard CT images, acquired prior to administering each daily fraction, we will develop fast parametric image deformation algorithms that do not require manually contoured landmarks. In Specific Aim 2. we will investigate novel methods for reconstructing CT images from incomplete projection data by matching deformation models to sequences of planar image projections, thereby integrating image reconstruction and deformable registration into a single process. This will be used to develop 4D anatomic representations of patient respiration with improved temporal resolution and to estimate intrafraction anatomic deformation from higher temporal resolution sequences of 2D images. Finally, in Specific Aim 3, novel methods for estimating the uncertainty and error of deformable image registration will be developed.