Davis, Anne Teresa; (2024) Improving the quality of Computed Tomography images used for radiotherapy treatment planning. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

CT imaging is an important part of the radiotherapy treatment process. A clinical oncologist outlines the tumour / target and key organs on those images for treatment plan development. Good quality images showing the boundaries of organs are required for precise outlining and subsequent accurate treatment. Key to achieving good imaging is the optimisation of radiotherapy CT scanner protocols. However, there is very limited published research in this area. This work investigates methods to improve the quality of radiotherapy CT head & neck and prostate images using optimisation of scan protocols and a multicentre imaging intercomparison. To provide guidance for scan protocol adjustment, the following were investigated: the CT parameters affecting image quality and CT number calibration; the impact of CT number change on the dose in the treatment plan; the adjustment of scanner protocols to improve image quality. To implement a multi-centre intercomparison this work undertook: a national survey of radiotherapy CT doses to set national dose reference levels (DRLs); methodology development for the image quality survey with bespoke and Catphan phantoms, software development to measure noise and patient diameter from clinical images. A pilot study of 10 centres tested the intercomparison methods. The principal factors affecting image quality were contrast, resolution and noise. Provided CT number change resulting from scan parameter adjustment was limited to ± 20 HU for soft tissue and ± 50 HU for bone and air, dose change in the treatment plan was within ± 1%. Use of an additional reconstructed image set using optimised kernel and FOV improved image quality. National CT DRLs were proposed. The developed image quality phantom and intercomparison methods were assessed as effective, identifying a range of performance levels across centres, and enabled feedback on protocol adjustment for image quality improvement. The method is scalable for a future nationwide intercomparison.

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