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

由于质子的相互作用特性，质子射线治疗(PBT)为局部癌症提供了比传统X射线放射治疗更多的潜在临床优势。在治疗前，根据患者的三维X射线CT图像制定全面的剂量分配计划。然而，由于在X射线CT的吸收率(Hounsfield单位)和质子的相对阻止能力之间转换存在不确定性，导致治疗计划中必须考虑3%的误差，因此这些治疗计划不是最优的。一种解决方案是不仅治疗质子，还用质子成像：通过选择足够高的能量，使质子通过患者并沉积最小剂量，通过跟踪质子的传入和传出并测量其剩余能量，可以重建质子CT图像。尽管有这些优点，质子CT图像的分辨率在本质上是有限的，因为质子在进入和离开之间的确切路径是未知的。该项目旨在通过创新地使用先前的X射线CT图像来提高质子CT图像的分辨率，以此作为重建的基础。通过改善重建图像的质量和重建时间，使用X射线CT先验进行质子成像将既提高治疗质量，又减少患者在治疗室进行成像的时间，从而提高患者的吞吐量。在先进的成像方法中，如质子CT，非线性和病态需要仔细使用先验信息，包括跨通道信息。基于局部特征稀疏性的简单方法正在扩展到建立在大数据统计描述基础上的多尺度和信息论先验。开发这种先验的重建技术将涉及采用机器学习的方法，包括非参数概率模型和深度学习技术。

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.