# Slice-Consistent 3D Volumetric Brain CT-to-MRI Translation with 2D Brownian Bridge Diffusion Model arXiv: https://arxiv.org/abs/2407.05059 ยท HTML: https://mlsanity.com/paper/2407.05059/ ## Key facts - Cited by 16 papers in the map (data through 14 Oct 2026). - Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. - Builds on 21 of its 33 references that are in the map. - First posted on arXiv 6 Jul 2024. ## Placed in - Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/) ## Builds on (most cited first, 21 of 21) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020) - [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020) - [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021) - [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019) - [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018) - [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021) - [Score-based Generative Modeling in Latent Space](https://mlsanity.com/paper/2106.05931/) (2021) - [AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generation](https://mlsanity.com/paper/1802.05384/) (2018) - [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) - [BBDM: Image-to-image Translation with Brownian Bridge Diffusion Models](https://mlsanity.com/paper/2205.07680/) (2022) - [SynthStrip: Skull-Stripping for Any Brain Image](https://mlsanity.com/paper/2203.09974/) (2022) - [MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion](https://mlsanity.com/paper/2203.12621/) (2022) - [Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network](https://mlsanity.com/paper/1803.01417/) (2018) - [Adaptive Latent Diffusion Model for 3D Medical Image to Image Translation: Multi-modal Magnetic Resonance Imaging Study](https://mlsanity.com/paper/2311.00265/) (2023) - [Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis](https://mlsanity.com/paper/2307.10094/) (2023) - [Deep CT to MR Synthesis using Paired and Unpaired Data](https://mlsanity.com/paper/1805.10790/) (2018) - [Reversible GANs for Memory-efficient Image-to-Image Translation](https://mlsanity.com/paper/1902.02729/) (2019) - [Structure-Preserving Synthesis: MaskGAN for Unpaired MR-CT Translation](https://mlsanity.com/paper/2307.16143/) (2023) - [SMRD: SURE-based Robust MRI Reconstruction with Diffusion Models](https://mlsanity.com/paper/2310.01799/) (2023) ## Built on by (most cited first, 16 of 16) - [Regression is all you need for medical image translation](https://mlsanity.com/paper/2505.02048/) (2025) - [Benchmarking GANs, Diffusion Models, and Flow Matching for T1w-to-T2w MRI Translation](https://mlsanity.com/paper/2507.14575/) (2025) - [SAGCNet: Spatial-Aware Graph Completion Network for Missing Slice Imputation in Population CMR Imaging](https://mlsanity.com/paper/2508.07041/) (2025) - [TextDiffSeg: Text-guided Latent Diffusion Model for 3d Medical Images Segmentation](https://mlsanity.com/paper/2504.11825/) (2025) - [XOCT: Enhancing OCT to OCTA Translation via Cross-Dimensional Supervised Multi-Scale Feature Learning](https://mlsanity.com/paper/2509.07455/) (2025) - [Diffusion-Driven Generation of Minimally Preprocessed Brain MRI](https://mlsanity.com/paper/2510.26834/) (2025) - [Generating Synthetic Contrast-Enhanced Chest CT Images from Non-Contrast Scans Using Slice-Consistent Brownian Bridge Diffusion Network](https://mlsanity.com/paper/2508.16897/) (2025) - [Cross Modality Image Translation In Medical Imaging Using Generative Frameworks](https://mlsanity.com/paper/2605.13686/) (2026) - [Structure-Accurate Medical Image Translation via Dynamic Frequency Balance and Knowledge Guidance](https://mlsanity.com/paper/2504.09441/) (2025) - [pyMEAL: A Multi-Encoder Augmentation-Aware-Learning Toolbox for Robust Medical Image Translation](https://mlsanity.com/paper/2505.24421/) (2025) - [Score-based Generative Diffusion Models to Synthesize Full-dose FDG Brain PET from MRI in Epilepsy Patients](https://mlsanity.com/paper/2506.11297/) (2025) - [ReBrain: Brain MRI Reconstruction from Sparse CT Slice via Retrieval-Augmented Diffusion](https://mlsanity.com/paper/2511.17068/) (2025) - [Likelihood-Separable Diffusion Inference for Multi-Image MRI Super-Resolution](https://mlsanity.com/paper/2601.14030/) (2026) - [Volumetric Directional Diffusion: Anchoring Uncertainty Quantification in Anatomical Consensus for Ambiguous Medical Image Segmentation](https://mlsanity.com/paper/2603.04024/) (2026) - [LiFT: Lifted Inter-slice Feature Trajectories for 3D Image Generation from 2D Generators](https://mlsanity.com/paper/2605.19060/) (2026) - [Spectral Consistent Flow for One-step 3D Medical Image Translation](https://mlsanity.com/paper/2607.10627/) (2026) ## Abstract (arXiv) In neuroimaging, generally, brain CT is more cost-effective and accessible imaging option compared to MRI. Nevertheless, CT exhibits inferior soft-tissue contrast and higher noise levels, yielding less precise structural clarity. In response, leveraging more readily available CT to construct its counterpart MRI, namely, medical image-to-image translation (I2I), serves as a promising solution. Particularly, while diffusion models (DMs) have recently risen as a powerhouse, they also come with a few practical caveats for medical I2I. First, DMs' inherent stochasticity from random noise sampling cannot guarantee consistent MRI generation that faithfully reflects its CT. Second, for 3D volumetric images which are prevalent in medical imaging, naively using 2D DMs leads to slice inconsistency, e.g., abnormal structural and brightness changes. While 3D DMs do exist, significant training costs and data dependency bring hesitation. As a solution, we propose novel style key conditioning (SKC) and inter-slice trajectory alignment (ISTA) sampling for the 2D Brownian bridge diffusion model. Specifically, SKC ensures a consistent imaging style (e.g., contrast) across slices, and ISTA interconnects the independent sampling of each slice, deterministically achieving style and shape consistent 3D CT-to-MRI translation. To the best of our knowledge, this study is the first to achieve high-quality 3D medical I2I based only on a 2D DM with no extra architectural models. Our experimental results show superior 3D medical I2I than existing 2D and 3D baselines, using in-house CT-MRI dataset and BraTS2023 FLAIR-T1 MRI dataset.