Diffusion-based Radiotherapy Dose Prediction Guided by Inter-slice Aware Structure Encoding
This work addresses a domain-specific problem for radiotherapy planning by providing a more accurate and efficient dose prediction method, though it is incremental as it builds on existing diffusion model techniques.
The paper tackles the over-smoothing problem in deep learning-based radiotherapy dose prediction by proposing a diffusion model (DiffDose) that predicts dose distribution maps, achieving improved accuracy with concrete metrics such as reduced mean absolute error and higher gamma passing rates compared to existing methods.
Deep learning (DL) has successfully automated dose distribution prediction in radiotherapy planning, enhancing both efficiency and quality. However, existing methods suffer from the over-smoothing problem for their commonly used L1 or L2 loss with posterior average calculations. To alleviate this limitation, we propose a diffusion model-based method (DiffDose) for predicting the radiotherapy dose distribution of cancer patients. Specifically, the DiffDose model contains a forward process and a reverse process. In the forward process, DiffDose transforms dose distribution maps into pure Gaussian noise by gradually adding small noise and a noise predictor is simultaneously trained to estimate the noise added at each timestep. In the reverse process, it removes the noise from the pure Gaussian noise in multiple steps with the well-trained noise predictor and finally outputs the predicted dose distribution maps...