FDDM: Frequency-Decomposed Diffusion Model for Rectum Cancer Dose Prediction in Radiotherapy

• URL: http://arxiv.org/abs/2410.07876v1
• Date: Thu, 10 Oct 2024 12:48:42 GMT
• Title: FDDM: Frequency-Decomposed Diffusion Model for Rectum Cancer Dose Prediction in Radiotherapy
• Authors: Xin Liao, Zhenghao Feng, Jianghong Xiao, Xingchen Peng, Yan Wang,
• Abstract summary: Diffusion model has achieved great success in computer vision, which excels in generating images with more high-frequency details. We propose Frequency-Decomposed Diffusion Model that refines the high-frequency subbands of the dose map. There is a notable difference between the coarse predicted results and ground truth in high?frequency subbands.
• Score: 12.025221208748308
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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