Preclinical radiation studies play a crucial role in cancer research because they serve as an experimental system for investigating the biological, chemical, and physical aspects of the radiation response. These studies aim to address open questions regarding long term side effects, regional tissue radiosensitivities, immunomodulatory effects, and new treatment strategies such as very high dose rate (FLASH) and spatially fractionated irradiations. However, small animal experiments present a unique practical challenge. Unlike in the clinic where imaging, contouring, and treatment planning are conducted over a period of several days, the preclinical workflow typically requires these steps to be carried out consecutively while the animal is in the irradiation position under sedation. To mitigate effects of the anaesthesia and ensure the animal’s wellbeing, a fast workflow is thus essential.
This thesis Justin Malimban explores the application of deep learning for autocontouring and fast proton dose engines for dose calculations, with the primary goal of streamlining irradiation planning for small animal studies. These tools greatly benefit the preclinical community by enhancing workflow efficiency, increasing experiment capacity, and reducing the overall workload of physicists and biologists. The reduction in planning time not only boosts animal throughput but also contributes positively to animal welfare. Additionally, the research on CT HU calibration methods has provided valuable insights into the benefits of SECT and DECT calibration for proton irradiation planning in preclinical settings. Ultimately, the work described in this thesis brings us one step closer to achieving more accurate and efficient image-guided irradiations of small animals for radiobiological studies.
