FDDM: Frequency-Decomposed Diffusion Model for Rectum Cancer Dose Prediction in Radiotherapy
<|reference_start|>FDDM: Frequency-Decomposed Diffusion Model for Rectum Cancer Dose Prediction in Radiotherapy: Accurate dose distribution prediction is crucial in the radiotherapy planning. Although previous methods based on convolutional neural network have shown promising performance, they have the problem of over-smoothing, leading to prediction without important high-frequency details. Recently, diffusion model has achieved great success in computer vision, which excels in generating images with more high-frequency details, yet suffers from time-consuming and extensive computational resource consumption. To alleviate these problems, we propose Frequency-Decomposed Diffusion Model (FDDM) that refines the high-frequency subbands of the dose map. To be specific, we design a Coarse Dose Prediction Module (CDPM) to first predict a coarse dose map and then utilize discrete wavelet transform to decompose the coarse dose map into a low-frequency subband and three high?frequency subbands. There is a notable difference between the coarse predicted results and ground truth in high?frequency subbands. Therefore, we design a diffusion-based module called High-Frequency Refinement Module (HFRM) that performs diffusion operation in the high?frequency components of the dose map instead of the original dose map. Extensive experiments on an in-house dataset verify the effectiveness of our approach.<|reference_end|>
@article{liao2024fddm:,
    title={FDDM: Frequency-Decomposed Diffusion Model for Rectum Cancer Dose
  Prediction in Radiotherapy},
    author={Xin Liao, Zhenghao Feng, Jianghong Xiao, Xingchen Peng, and Yan Wang},
    journal={arXiv preprint arXiv:2410.07876},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07876},
    primaryClass={eess.IV cs.CV}
}