Deep Dose Plugin Towards Real-time Monte Carlo Dose Calculation Through a Deep Learning based Denoising Algorithm
Ti Bai, Biling Wang, Dan Nguyen, Steve Jiang

TL;DR
This paper presents a deep learning-based denoising algorithm integrated with GPU Monte Carlo dose calculation, achieving real-time radiotherapy dose computation within 0.15 seconds, suitable for online adaptive treatments.
Contribution
The study introduces a novel deep learning denoiser with acceleration strategies and weakly supervised training, enabling real-time Monte Carlo dose calculation for radiotherapy.
Findings
Denoiser runs in 39 ms, 11.6 times faster than baseline.
Whole dose calculation pipeline completes within 0.15 seconds.
Enables real-time dose calculation for online adaptive radiotherapy.
Abstract
Monte Carlo (MC) simulation is considered the gold standard method for radiotherapy dose calculation. However, achieving high precision requires a large number of simulation histories, which is time consuming. The use of computer graphics processing units (GPUs) has greatly accelerated MC simulation and allows dose calculation within a few minutes for a typical radiotherapy treatment plan. However, some clinical applications demand real time efficiency for MC dose calculation. To tackle this problem, we have developed a real time, deep learning based dose denoiser that can be plugged into a current GPU based MC dose engine to enable real time MC dose calculation. We used two different acceleration strategies to achieve this goal: 1) we applied voxel unshuffle and voxel shuffle operators to decrease the input and output sizes without any information loss, and 2) we decoupled the 3D…
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Taxonomy
TopicsAdvanced Radiotherapy Techniques · Medical Imaging Techniques and Applications · Radiation Detection and Scintillator Technologies
MethodsAxial Attention · Convolution
