A concept for personalised CT dosimetry using methods of machine learning

In the context of increasing number of computer tomography (CT) examinations, also the importance for fast and patient specific dose assessment grows. Machine learning (ML) offers a promising approach to achieve fast and user-friendly organ dose assessment in clinical CT workflows. Previous studies have shown that neural networks can reproduce Monte Carlo (MC)-calculated organ doses with reasonable accuracy; however, the representation of the radiation field properties, defining the spatial and energy distribution of the X-ray beam during acquisition, and the characterisation of the training data distributions vary widely. Furthermore, a comprehensive uncertainty assessment over the entire dosimetry process has not been addressed. This thesis presents a framework for personalised CT dosimetry based on ML methods, combining a validated newly implemented particle source for MC simulations, measurement-based radiation field characterisation and systematic uncertainty assessment at every stage of the process. In addition, the work investigated the influence of training data composition in regarding the role of synthetic and real patient geometry data. The proposed approach achieves accuracy comparable to previous studies, while providing a complete uncertainty assessment methodology.