# WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis

arXiv: https://arxiv.org/abs/2402.19043 · HTML: https://mlsanity.com/paper/2402.19043/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 28 of its 37 references that are in the map.
- First posted on arXiv 29 Feb 2024.

## Placed in

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

## Builds on (most cited first, 28 of 28)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [Tackling the Generative Learning Trilemma with Denoising Diffusion GANs](https://mlsanity.com/paper/2112.07804/) (2021)
- [Med3D: Transfer Learning for 3D Medical Image Analysis](https://mlsanity.com/paper/1904.00625/) (2019)
- [Brain Imaging Generation with Latent Diffusion Models](https://mlsanity.com/paper/2209.07162/) (2022)
- [Diffusion Models for Medical Anomaly Detection](https://mlsanity.com/paper/2203.04306/) (2022)
- [Diffusion Models for Implicit Image Segmentation Ensembles](https://mlsanity.com/paper/2112.03145/) (2021)
- [MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model](https://mlsanity.com/paper/2211.00611/) (2022)
- [Wavelet Diffusion Models are fast and scalable Image Generators](https://mlsanity.com/paper/2211.16152/) (2022)
- [Generative models improve fairness of medical classifiers under distribution shifts](https://mlsanity.com/paper/2304.09218/) (2023)
- [MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation](https://mlsanity.com/paper/2110.01406/) (2021)
- [Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks](https://mlsanity.com/paper/1908.02498/) (2019)
- [Wavelet Score-Based Generative Modeling](https://mlsanity.com/paper/2208.05003/) (2022)
- [Wavelet Integrated CNNs for Noise-Robust Image Classification](https://mlsanity.com/paper/2005.03337/) (2020)
- [Hierarchical Amortized Training for Memory-efficient High Resolution 3D GAN](https://mlsanity.com/paper/2008.01910/) (2020)
- [Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis](https://mlsanity.com/paper/2307.10094/) (2023)
- [3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Images](https://mlsanity.com/paper/2107.09700/) (2021)
- [Memory-Efficient 3D Denoising Diffusion Models for Medical Image Processing](https://mlsanity.com/paper/2303.15288/) (2023)
- [Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model](https://mlsanity.com/paper/2212.08034/) (2022)
- [Augmenting medical image classifiers with synthetic data from latent diffusion models](https://mlsanity.com/paper/2308.12453/) (2023)
- [Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE](https://mlsanity.com/paper/2007.04780/) (2020)
- [Point Cloud Diffusion Models for Automatic Implant Generation](https://mlsanity.com/paper/2303.08061/) (2023)
- [Diffusion Models for Contrast Harmonization of Magnetic Resonance Images](https://mlsanity.com/paper/2303.08189/) (2023)
- [FakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomy](https://mlsanity.com/paper/2207.01390/) (2022)

## Built on by (most cited first, 30 of 56)

- [3D MedDiffusion: A 3D Medical Latent Diffusion Model for Controllable and High-quality Medical Image Generation](https://mlsanity.com/paper/2412.13059/) (2024)
- [I2I-Mamba: Multi-modal medical image synthesis via selective state space modeling](https://mlsanity.com/paper/2405.14022/) (2024)
- [Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting](https://mlsanity.com/paper/2403.14499/) (2024)
- [MAISI-v2: Accelerated 3D High-Resolution Medical Image Synthesis with Rectified Flow and Region-specific Contrastive Loss](https://mlsanity.com/paper/2508.05772/) (2025)
- [FairDiff: Fair Segmentation with Point-Image Diffusion](https://mlsanity.com/paper/2407.06250/) (2024)
- [MedSegFactory: Text-Guided Generation of Medical Image-Mask Pairs](https://mlsanity.com/paper/2504.06897/) (2025)
- [fastWDM3D: Fast and Accurate 3D Healthy Tissue Inpainting](https://mlsanity.com/paper/2507.13146/) (2025)
- [MedFuncta: A Unified Framework for Learning Efficient Medical Neural Fields](https://mlsanity.com/paper/2502.14401/) (2025)
- [Back-in-Time Diffusion: Unsupervised Detection of Medical Deepfakes](https://mlsanity.com/paper/2407.15169/) (2024)
- [FlowLet: Conditional 3D Brain MRI Synthesis using Wavelet Flow Matching](https://mlsanity.com/paper/2601.05212/) (2026)
- [Non-Reference Quality Assessment for Medical Imaging: Application to Synthetic Brain MRIs](https://mlsanity.com/paper/2407.14994/) (2024)
- [Brain Tumour Removing and Missing Modality Generation using 3D WDM](https://mlsanity.com/paper/2411.04630/) (2024)
- [LAND: Lung and Nodule Diffusion for 3D Chest CT Synthesis with Anatomical Guidance](https://mlsanity.com/paper/2510.18446/) (2025)
- [WFM: 3D Wavelet Flow Matching for Ultrafast Multi-Modal MRI Synthesis](https://mlsanity.com/paper/2604.21146/) (2026)
- [Denoising Diffusion Models for Anomaly Localization in Medical Images](https://mlsanity.com/paper/2410.23834/) (2024)
- [3D Wavelet Latent Diffusion Model for Whole-Body MR-to-CT Modality Translation](https://mlsanity.com/paper/2507.11557/) (2025)
- [Scalable next-scale autoregression for medical image generation across anatomical regions](https://mlsanity.com/paper/2602.14512/) (2026)
- [Diffusion-Driven Generation of Minimally Preprocessed Brain MRI](https://mlsanity.com/paper/2510.26834/) (2025)
- [Multiscale Structure-Guided Latent Diffusion for Multimodal MRI Translation](https://mlsanity.com/paper/2603.12581/) (2026)
- [Pixel-Level Residual Diffusion Transformer: Scalable 3D CT Volume Generation](https://mlsanity.com/paper/2606.20112/) (2026)
- [Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review](https://mlsanity.com/paper/2505.07866/) (2025)
- [RealDeal: Enhancing Realism and Details in Brain Image Generation via Image-to-Image Diffusion Models](https://mlsanity.com/paper/2507.18830/) (2025)
- [Towards MR-Based Trochleoplasty Planning](https://mlsanity.com/paper/2508.06076/) (2025)
- [Tooth-Diffusion: Guided 3D CBCT Synthesis with Fine-Grained Tooth Conditioning](https://mlsanity.com/paper/2508.14276/) (2025)
- [Contrastive Anatomy-Contrast Disentanglement: A Domain-General MRI Harmonization Method](https://mlsanity.com/paper/2509.06592/) (2025)
- [Cosmo3DFlow: Wavelet Flow Matching for Spatial-to-Spectral Compression in Reconstructing the Early Universe](https://mlsanity.com/paper/2602.10172/) (2026)
- [MeDUET: Disentangled Unified Pretraining for 3D Medical Image Synthesis and Analysis](https://mlsanity.com/paper/2602.17901/) (2026)
- [NOIR: Neural Operator mapping for Implicit Representations](https://mlsanity.com/paper/2603.13118/) (2026)
- [Everything at Every Scale: Scale-Invariant Diffusion with Continuous Super-Resolution](https://mlsanity.com/paper/2605.26032/) (2026)
- [WaveDiT: Distribution-Aware Wavelet Flow Matching for Efficient 3D Brain MRI Synthesis](https://mlsanity.com/paper/2606.08670/) (2026)

## Abstract (arXiv)

Due to the three-dimensional nature of CT- or MR-scans, generative modeling of medical images is a particularly challenging task. Existing approaches mostly apply patch-wise, slice-wise, or cascaded generation techniques to fit the high-dimensional data into the limited GPU memory. However, these approaches may introduce artifacts and potentially restrict the model's applicability for certain downstream tasks. This work presents WDM, a wavelet-based medical image synthesis framework that applies a diffusion model on wavelet decomposed images. The presented approach is a simple yet effective way of scaling 3D diffusion models to high resolutions and can be trained on a single SI40gigabyte GPU. Experimental results on BraTS and LIDC-IDRI unconditional image generation at a resolution of 128 × 128 × 128 demonstrate state-of-the-art image fidelity (FID) and sample diversity (MS-SSIM) scores compared to recent GANs, Diffusion Models, and Latent Diffusion Models. Our proposed method is the only one capable of generating high-quality images at a resolution of 256 × 256 × 256, outperforming all comparing methods.