Quick and robust LINAC commissioning and beam data modeling

Researchers at Stanford University have developed a quick, robust, machine learning based method for linear accelerator (LINAC) commissioning and beam data modeling. High precision LINAC commissioning data is crucial for the safe setup of new LINACs, verification of proper operation, continuing quality assurance, and as input to treatment planning software. Current commissioning is a laborious process that lacks operational standards and commercial validation tools. Stanford's fast and accurate method uses a few sampling points to generate accurate percentage depth dose (PDD) and depth dose profile (DDP) for the LINAC beam. It was developed and tested on three medical LINACs with 1000s of data points. This approach greatly simplifies the tedious linac commissioning procedure and provides a robust and safe way of introducing new LINACs into the clinic.

Stage of Development – Software Tool in use with Clinical LINAC