Deformable Image Registration and Reconstruction

最佳图像引导的自适应放射治疗需要患者解剖的4D表示，即 允许通过生物过程跟踪肿瘤和正常组织体素的位置 成像、计划和模拟、近距离放射治疗的实施，以及每个调强放射治疗部分的管理。这个 该项目的科学目标是研究非刚性图像配准的新方法 构建和验证患者在治疗过程中发生变化时的解剖表示 进程。实用的目标是创建一套图像处理资源，使例程能够 影像引导自适应放射治疗技术在临床中的应用。在具体目标1中，我们将 研究用于高剂量近距离放射治疗(HDR)的轮廓驱动可变形配准方法 盆腔内剂量分布到调强放射治疗剂量分布，以及用于将生物图像配准到外部 波束规划图像，包括开发一种新的表面匹配算法，该算法解释了 等高线上的不确定性。为了有效地将信息从规划CT图像映射到机载CT图像， 在管理每个每日分数之前，我们将开发快速的参数图像变形 不需要手动绘制地标等高线的算法。在特定的目标2.我们将调查小说 基于不完全投影数据的形变模型匹配重建CT图像方法 平面图像投影序列，从而集成了图像重建和可变形配准 变成了一个单一的过程。这将被用来开发患者呼吸的4D解剖表示 改进的时间分辨率和从更高的时间段估计分数内解剖变形 2D图像的分辨率序列。最后，在具体目标3中，提出了估计不确定度的新方法 并发展了可变形图像配准的误差。

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.