# cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesis

arXiv: https://arxiv.org/abs/2411.17203 · HTML: https://mlsanity.com/paper/2411.17203/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 26 Nov 2024.

## Placed in

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

## Builds on (most cited first, 5 of 5)

- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)](https://mlsanity.com/paper/2305.09011/) (2023)
- [Diffusion Models for Contrast Harmonization of Magnetic Resonance Images](https://mlsanity.com/paper/2303.08189/) (2023)
- [Cycle-guided Denoising Diffusion Probability Model for 3D Cross-modality MRI Synthesis](https://mlsanity.com/paper/2305.00042/) (2023)
- [Deep Generative Models for 3D Medical Image Synthesis](https://mlsanity.com/paper/2410.17664/) (2024)

## Built on by (most cited first, 19 of 19)

- [WFM: 3D Wavelet Flow Matching for Ultrafast Multi-Modal MRI Synthesis](https://mlsanity.com/paper/2604.21146/) (2026)
- [Towards MR-Based Trochleoplasty Planning](https://mlsanity.com/paper/2508.06076/) (2025)
- [NOIR: Neural Operator mapping for Implicit Representations](https://mlsanity.com/paper/2603.13118/) (2026)
- [Knowledge-Guided 3D CT Generation: A Conditioning-Centric Taxonomy](https://mlsanity.com/paper/2608.09992/) (2026)
- [Enhancing Privacy: The Utility of Stand-Alone Synthetic CT and MRI for Tumor and Bone Segmentation](https://mlsanity.com/paper/2506.12106/) (2025)
- [Towards Diagnostic Quality Flat-Panel Detector CT Imaging Using Diffusion Models](https://mlsanity.com/paper/2508.16252/) (2025)
- [K-Syn: K-space Data Synthesis in Ultra Low-data Regimes](https://mlsanity.com/paper/2509.06997/) (2025)
- [SLaM-DiMM: Shared Latent Modeling for Diffusion Based Missing Modality Synthesis in MRI](https://mlsanity.com/paper/2509.16019/) (2025)
- [DEMIST: Decoupled Multi-stream latent diffusion for Quantitative Myelin Map Synthesis](https://mlsanity.com/paper/2511.12396/) (2025)
- [POWDR: Pathology-preserving Outpainting with Wavelet Diffusion for 3D MRI](https://mlsanity.com/paper/2601.09044/) (2026)
- [Brain MR Image Synthesis with 3D Multi-Contrast Self-Attention GAN](https://mlsanity.com/paper/2604.00070/) (2026)
- [3D Ultrasound-Derived Pseudo-CT Synthesis Using a Transformer-Augmented Residual Network for Real-Time Operator Guidance](https://mlsanity.com/paper/2605.04856/) (2026)
- [LiFT: Lifted Inter-slice Feature Trajectories for 3D Image Generation from 2D Generators](https://mlsanity.com/paper/2605.19060/) (2026)
- [Wavelet Phase Diffusion for Structurally and Semantically Consistent Sim-to-Real Translation](https://mlsanity.com/paper/2607.21628/) (2026)
- [Large-Scale Pretraining for Improving Deep Learning-Based Geometric Distortion Correction of Diffusion-Weighted Imaging](https://mlsanity.com/paper/2609.06437/) (2026)
- [AWR-Net: Decoupling Anatomy and Appearance for 3D Fetal Brain Ultrasound Synthesis](https://mlsanity.com/paper/2609.22635/) (2026)
- [CATCH: Counterfactual Anatomical Tissue Inpainting with Conditional Haar Diffusion](https://mlsanity.com/paper/2609.29591/) (2026)
- [Latent Space Is Not Flat: Rethinking Latent Structure for 3D Medical Image Synthesis](https://mlsanity.com/paper/2609.32794/) (2026)
- [Unified Multi-plane Autoregressive Diffusion for 3D Multi-contrast MRI Synthesis](https://mlsanity.com/paper/2610.09376/) (2026)

## Abstract (arXiv)

This paper contributes to the "BraTS 2024 Brain MR Image Synthesis Challenge" and presents a conditional Wavelet Diffusion Model (cWDM) for directly solving a paired image-to-image translation task on high-resolution volumes. While deep learning-based brain tumor segmentation models have demonstrated clear clinical utility, they typically require MR scans from various modalities (T1, T1ce, T2, FLAIR) as input. However, due to time constraints or imaging artifacts, some of these modalities may be missing, hindering the application of well-performing segmentation algorithms in clinical routine. To address this issue, we propose a method that synthesizes one missing modality image conditioned on three available images, enabling the application of downstream segmentation models. We treat this paired image-to-image translation task as a conditional generation problem and solve it by combining a Wavelet Diffusion Model for high-resolution 3D image synthesis with a simple conditioning strategy. This approach allows us to directly apply our model to full-resolution volumes, avoiding artifacts caused by slice- or patch-wise data processing. While this work focuses on a specific application, the presented method can be applied to all kinds of paired image-to-image translation problems, such as CT leftrightarrow MR and MR leftrightarrow PET translation, or mask-conditioned anatomically guided image generation.