# DiffDP: Radiotherapy Dose Prediction via a Diffusion Model

arXiv: https://arxiv.org/abs/2307.09794 · HTML: https://mlsanity.com/paper/2307.09794/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 19 Jul 2023.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 10 of 10)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models](https://mlsanity.com/paper/2104.14951/) (2021)
- [Diffusion Models for Medical Anomaly Detection](https://mlsanity.com/paper/2203.04306/) (2022)
- [Diffusion Deformable Model for 4D Temporal Medical Image Generation](https://mlsanity.com/paper/2206.13295/) (2022)
- [Diffusion Model for Generative Image Denoising](https://mlsanity.com/paper/2302.02398/) (2023)
- [Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks](https://mlsanity.com/paper/1807.06489/) (2018)
- [A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning](https://mlsanity.com/paper/1709.09233/) (2017)

## Built on by (most cited first, 13 of 13)

- [I-MedSAM: Implicit Medical Image Segmentation with Segment Anything](https://mlsanity.com/paper/2311.17081/) (2023)
- [MD-Dose: A diffusion model based on the Mamba for radiation dose prediction](https://mlsanity.com/paper/2403.08479/) (2024)
- [Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation](https://mlsanity.com/paper/2508.09177/) (2025)
- [Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy](https://mlsanity.com/paper/2406.01853/) (2024)
- [Diffusion-based Radiotherapy Dose Prediction Guided by Inter-slice Aware Structure Encoding](https://mlsanity.com/paper/2311.02991/) (2023)
- [Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation](https://mlsanity.com/paper/2403.03512/) (2024)
- [SP-DiffDose: A Conditional Diffusion Model for Radiation Dose Prediction Based on Multi-Scale Fusion of Anatomical Structures, Guided by SwinTransformer and Projector](https://mlsanity.com/paper/2312.06187/) (2023)
- [Triplet-constraint Transformer with Multi-scale Refinement for Dose Prediction in Radiotherapy](https://mlsanity.com/paper/2402.04566/) (2024)
- [DAMM-Diffusion: Learning Divergence-Aware Multi-Modal Diffusion Model for Nanoparticles Distribution Prediction](https://mlsanity.com/paper/2503.09491/) (2025)
- [Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study](https://mlsanity.com/paper/2605.09622/) (2026)
- [ARANet: Attention-based Residual Adversarial Network with Deep Supervision for Radiotherapy Dose Prediction of Cervical Cancer](https://mlsanity.com/paper/2408.13981/) (2024)
- [Alleviating Class Imbalance in Semi-supervised Multi-organ Segmentation via Balanced Subclass Regularization](https://mlsanity.com/paper/2408.14047/) (2024)
- [FDDM: Frequency-Decomposed Diffusion Model for Rectum Cancer Dose Prediction in Radiotherapy](https://mlsanity.com/paper/2410.07876/) (2024)

## Abstract (arXiv)

Currently, deep learning (DL) has achieved the automatic prediction of dose distribution in radiotherapy planning, enhancing its efficiency and quality. However, existing methods suffer from the over-smoothing problem for their commonly used L_1 or L_2 loss with posterior average calculations. To alleviate this limitation, we innovatively introduce a diffusion-based dose prediction (DiffDP) model for predicting the radiotherapy dose distribution of cancer patients. Specifically, the DiffDP model contains a forward process and a reverse process. In the forward process, DiffDP gradually transforms dose distribution maps into Gaussian noise by adding small noise and trains a noise predictor to predict the noise added in each timestep. In the reverse process, it removes the noise from the original Gaussian noise in multiple steps with the well-trained noise predictor and finally outputs the predicted dose distribution map. To ensure the accuracy of the prediction, we further design a structure encoder to extract anatomical information from patient anatomy images and enable the noise predictor to be aware of the dose constraints within several essential organs, i.e., the planning target volume and organs at risk. Extensive experiments on an in-house dataset with 130 rectum cancer patients demonstrate the s