Proton CT Image Reconstruction with X-Ray CT Priors Second Supervisors: Simon Arridge, Jamie McClelland (UCL CMIC)

X 射线 CT 质子 CT 图像重建优先级第二主管：Simon Arridge、Jamie McClelland (UCL CMIC)

• 批准号: 1958303
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1958303
• 关键词: Proton; CT; Image; Reconstruction; Ray

roton beam therapy (PBT) offers potential clinical advantages over conventional X-ray radiotherapy for localised cancer due to the interaction characteristics of protons. Prior to treatment, a comprehensive dose delivery plan is formulated with 3D X-ray CT images of the patient. However, these treatment plans are suboptimal due to the uncertainty in converting between absorption (in Hounsfield Units) of an X-ray CT and the Relative Stopping Power of protons, resulting in a 3% error that must be factored into treatment plans. A solution is to not only treat but also image with protons: by selecting an energy that is suitably high enough that the protons pass through the patient and deposit minimal dose, a proton CT image can be reconstructed by tracking the incoming and outgoing protons and measuring their residual energy. Despite these advantages, the resolution of proton CT images is inherently limited as the exact path of the proton between entry and exit is unknown. This project seeks to improve the resolution of proton CT images by the novel use of a prior X-ray CT image upon which to base the reconstruction. By improving both the quality of the reconstructed image and also the reconstruction time, the use of X-ray CT priors for proton imaging would both improve the quality of treatment and reduce the time the patient spends in the treatment room for imaging and therefore improving patient throughput. In advanced imaging methods such as proton CT, nonlinearities and ill-posedness necessitate the careful use of prior information, including cross-modality information. Simple methods based on enforcing sparsity of local features are being extended to multi-scale and information-theoretic priors which build on statistical descriptions of big-data. Developing reconstruction techniques for such priors will involve adapting methods from machine-learning, including non-parametric probability models and deep-learning techniques.

由于质子的相互作用特性，与传统的X射线放射疗法相比，质子束疗法（PBT）为局部癌症提供了潜在的临床优势。在治疗之前，使用患者的3D X射线CT图像制定全面的剂量输送计划。然而，这些治疗计划是次优的，因为在X射线CT的吸收（以亨氏单位）和质子的相对阻止功率之间转换的不确定性，导致3%的误差，必须考虑到治疗计划。一种解决方案是不仅用质子治疗而且用质子成像：通过选择足够高的能量以使质子穿过患者并存款最小剂量，可以通过跟踪进入和离开的质子并测量其剩余能量来重建质子CT图像。尽管有这些优点，质子CT图像的分辨率本质上是有限的，因为质子在入口和出口之间的确切路径是未知的。该项目旨在提高质子CT图像的分辨率，通过新的使用之前的X射线CT图像重建的基础上。通过改善重建图像的质量和重建时间，将X射线CT先验用于质子成像将既改善治疗质量又减少患者在治疗室中花费用于成像的时间，从而改善患者吞吐量。在先进的成像方法，如质子CT，非线性和不适定性需要仔细使用的先验信息，包括跨模态信息。基于增强局部特征稀疏性的简单方法正在扩展到基于大数据统计描述的多尺度和信息论先验。开发此类先验的重建技术将涉及调整机器学习的方法，包括非参数概率模型和深度学习技术。