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

Lead Research Organisation: University College London
Department Name: Physics and Astronomy


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.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
ST/P006736/1 01/10/2017 30/09/2024
1958303 Studentship ST/P006736/1 25/09/2017 28/02/2022 Matthieu Hentz