Modeling ray angles in deep learning based dose calculation algorithms

A fundamental tool in radiotherapy treatment planning is the dose calculation algorithm, which models the dose that will be distributed for given beam parameters and patient geometry. Various available algorithms include Monte Carlo simulations (MC) and pencil beam algorithms (PBA), with the former being computationally expensive but offering...

Improving Error Detection in Deep Learning Based Radiotherapy Autocontouring Using Bayesian Uncertainty

Bayesian Neural Nets (BNN) are increasingly used for robust organ auto-contouring. Uncertainty heatmaps extracted from BNNs have been shown to correspond to inaccurate regions. To help speed up the mandatory quality assessment (QA) of contours in radiotherapy, these heatmaps could be used as stimuli to direct visual attention of clinicians to...

A physics guided neural network approach for dose prediction in automated radiation therapy treatment planning

Radiotherapy treatment planning is a complex and time consuming process prone to differences as result of choices of individual planners. Autoplanning systems have been introduced to both reduce the time consumption and to counteract the influence of individual planning choices. Although autoplanning generally increases performance of the...