ARCHER-a Monte Carlo code for multi-particle radiotherapy through GPU-accelerated simulation and DL-based denoising

The ARCHER project was initiated about 14 years ago to explore the use of emerging GPU technologies for fast Monte Carlo (MC) calculations. This paper presents the latest work to integrate the newly developed deep conventional neural network (dCNN) based MC denoising method with GPU-based MC multi-particle radiation transport simulation method to demonstrate a real-time dose computing capability for clinically realistic radiotherapy examples. The computing process involves GPU-based dose calculations that is followed by dCNN-based denoising. The dCNN-based dose denoiser is designed and employed to reduce the statistical uncertainty in dose distributions in patient anatomy defined by 3D computed tomography (CT) images. The training data include a range of dose distributions covering low-count/high-noise (DoseLCHN) and high-count/low-noise (DoseHCLN). The extremely large DoseLCHN and DoseHCLN dataset was generated from ARCHER. The DoseLCHN dataset is input into the trained model to output a predicted DoseHCLN dataset. For the evaluation, the DoseHCLN dataset produced by ARCHER is considered to be the ground truth. Experimental results show that the dose distributions generated from newly proposed method agreed consistently with the DoseHCLN produced from ARCHER. For hundreds of patient radiation treatment cases involving photons and protons, the average running time for one patient (GPU-based dose simulation followed by dCNN-based denoising) is about 200 ms. These preliminary results have demonstrated the feasibility of real-time Monte Carlo dose computing using an integrated dCNN-based denoising and GPU-based dose calculational approach. On-going studies involving more radiation types and clinical procedures are expected to facilitate the use of real-time MC dose planning and verification in the clinical workflow.