D3M: Deformation-Driven Diffusion Model for Synthesis of Contrast-Enhanced MRI with Brain Tumors

In this study, we propose a deformation-driven diffusion model (D3M) for CEMRI synthesis with brain tumors from NCMRIs. Instead of modeling enhancement errors as intensity errors, we formulate them as incorrect interpretation of tumor subcomponents, where enhanced tumors are misinterpreted as non-enhanced tumors and vice versa. In this way, the enhancement can be geometrically corrected with spatial deformation. This reduces the difficulty of CEMRI synthesis, as the intensity error is usually large to correct whereas the geometry correction is relatively small. Specifically, we first introduce a multi-step spatial deformation module (MSSDM) in D3M. MSSDM performs image deformation to adjust the enhancement, displacing enhanced regions to remove false positive and false negative enhancement. Moreover, as the denoising process of diffusion models is stepwise, MSSDM is applied at these multiple diffusion steps. Second, to further guide the spatial deformation, we incorporate an auxiliary task of segmenting the enhanced tumor, which aids the model understanding of contrast enhancement. Accordingly, we introduce a dual-stream image-mask decoder (DSIMD) that jointly produces intermediate enhanced images and masks of enhanced tumors.

Dataset

BraSyn: https://www.synapse.org/Synapse:syn53708249/wiki/627507

BraTS-PEDs: https://www.synapse.org/Synapse:syn53708249/wiki/627505

Data Preprocessing

python preprocess.py

Training

python train.py

Inference

python test.py

Citation

If you use this code, please cite:

@inproceedings{pang2025d,
  title={D3M: Deformation-Driven Diffusion Model for Synthesis of Contrast-Enhanced MRI with Brain Tumors},
  author={Pang, Haowen and Zhang, Peng and Hong, Xiaoming and Chen, Shannan and Ye, Chuyang},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={151--160},
  year={2025},
  organization={Springer}
}