mlsanityData to 14 Oct 2026

DoseBridge: Denoising Diffusion Bridge Model for Dose Prediction in Lung Intensity-Modulated Proton Therapy

arXiv 2608.10173 ↗First posted 10 Aug 2026cs.CVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Most radiotherapy dose-prediction models use only CT images and anatomical structures, although intensity-modulated proton therapy (IMPT) dose also depends strongly on beam geometry and available clinical datasets are often small. We present DoseBridge, a denoising diffusion bridge model that uses the patient CT as a structured bridge endpoint and encodes plan-specific beam geometry in a spatially aligned beam mask. Multiscale fusion combines CT, target, organ-at-risk, and beam-mask representations with 1.95% additional parameters. DoseBridge was retrospectively evaluated on single-institution CT images and treatment plans from 52 patients with advanced-stage lung cancer treated with 60 Gy in 30 fractions; 42 cases were used for training and 10 for testing. Performance was assessed using image-similarity, dose-volume, and Lyman-Kutcher-Burman normal-tissue complication probability (NTCP) metrics and compared with two deep-learning models. On the test cohort, DoseBridge achieved a mean absolute error of 4.170 Gy, peak signal-to-noise ratio of 23.06 dB, and structural similarity index of 0.798, outperforming both comparison models on these metrics. Clinical target volume D95 differed from the reference dose by 0.62 +/- 1.6 Gy; signed organ-at-risk mean-dose differences ranged from -0.32 to 0.24 Gy, and NTCP differences were -0.40 +/- 2.2 and 0.52 +/- 3.4 percentage points for acute esophagitis and radiation pneumonitis, respectively. Changing only the beam mask redirected predicted low-dose entrance regions while preserving the high-dose target region. To our knowledge, DoseBridge is the first denoising diffusion bridge model for radiotherapy dose prediction. These results support its feasibility as a beam-aware planning prior for lung IMPT, pending evaluation in larger external cohorts.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 16 of its 36 references that are in the map. First posted on arXiv 10 Aug 2026.

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16 of 36 references.

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Same direction, newest first

  1. 2023DoseDiff: Distance-aware Diffusion Model for Dose Prediction in Radiotherapy28 Jun
  2. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Survey14 Nov

Other references · 14

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  5. 2017Squeeze-and-Excitation Networkscited by 4,789
  6. 2022A ConvNet for the 2020scited by 3,579
  7. 2017Revisiting Unreasonable Effectiveness of Data in Deep Learning Eracited by 919
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  9. 2022BBDM: Image-to-image Translation with Brownian Bridge Diffusion Modelscited by 225
  10. 2023Denoising Diffusion Bridge Modelscited by 178
  11. 2020OpenKBP: The open-access knowledge-based planning grand challengecited by 30
  12. 2018Three-Dimensional Radiotherapy Dose Prediction on Head and Neck Cancer Patients with a Hierarchically Densely Connected U-net Deep Learning Architecturecited by 18
2 more
  1. 2018Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networkscited by 11
  2. 2026Robustness of Transformer-Based Fluence Map Prediction Under Clinically Realistic Perturbationscited by 1

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  1. 2025Diffusion Transformer-based Universal Dose Denoising for Pencil Beam Scanning Proton Therapy
  2. 2025Conditional Diffusion Model with Anatomical-Dose Dual Constraints for End-to-End Multi-Tumor Dose Prediction
  3. 2024MD-Dose: A diffusion model based on the Mamba for radiation dose prediction
  4. 2023SP-DiffDose: A Conditional Diffusion Model for Radiation Dose Prediction Based on Multi-Scale Fusion of Anatomical Structures, Guided by SwinTransformer and Projector
  5. 2025FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoising
  6. 2023Diffusion-based Radiotherapy Dose Prediction Guided by Inter-slice Aware Structure Encoding
  7. 2026Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study
  8. 2023DiffDP: Radiotherapy Dose Prediction via a Diffusion Model