# Cross-modality image synthesis from TOF-MRA to CTA using diffusion-based models

arXiv: https://arxiv.org/abs/2409.10089 · HTML: https://mlsanity.com/paper/2409.10089/

## Key facts

- Cited by 4 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 42 references that are in the map.
- First posted on arXiv 16 Sep 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)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [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)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [GLU Variants Improve Transformer](https://mlsanity.com/paper/2002.05202/) (2020)
- [Root Mean Square Layer Normalization](https://mlsanity.com/paper/1910.07467/) (2019)
- [Language Modeling with Gated Convolutional Networks](https://mlsanity.com/paper/1612.08083/) (2016)
- [Scalable Adaptive Computation for Iterative Generation](https://mlsanity.com/paper/2212.11972/) (2022)
- [Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers](https://mlsanity.com/paper/2401.11605/) (2024)
- [Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network](https://mlsanity.com/paper/1802.09655/) (2018)
- [Generative models improve fairness of medical classifiers under distribution shifts](https://mlsanity.com/paper/2304.09218/) (2023)
- [Do Transformer Modifications Transfer Across Implementations and Applications?](https://mlsanity.com/paper/2102.11972/) (2021)
- [Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models](https://mlsanity.com/paper/2209.12104/) (2022)
- [Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis](https://mlsanity.com/paper/2307.10094/) (2023)
- [Feature Extraction for Generative Medical Imaging Evaluation: New Evidence Against an Evolving Trend](https://mlsanity.com/paper/2311.13717/) (2023)
- [A Deep Learning Approach for Virtual Contrast Enhancement in Contrast Enhanced Spectral Mammography](https://mlsanity.com/paper/2308.00471/) (2023)
- [Generative Adversarial Training for MRA Image Synthesis Using Multi-Contrast MRI](https://mlsanity.com/paper/1804.04366/) (2018)
- [Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation](https://mlsanity.com/paper/2405.12223/) (2024)
- [Cross-Modality Neuroimage Synthesis: A Survey](https://mlsanity.com/paper/2202.06997/) (2022)

## Built on by (most cited first, 4 of 4)

- [VasTSD: Learning 3D Vascular Tree-state Space Diffusion Model for Angiography Synthesis](https://mlsanity.com/paper/2503.12758/) (2025)
- [MUST: Modality-Specific Representation-Aware Transformer for Diffusion-Enhanced Survival Prediction with Missing Modality](https://mlsanity.com/paper/2603.26071/) (2026)
- [Semantically Conditioned Diffusion Models for Cerebral DSA Synthesis](https://mlsanity.com/paper/2602.11703/) (2026)
- [VeCAS: Vessel-Focused Contrast-Free Angiogram Synthesis for Vascular Interventions](https://mlsanity.com/paper/2608.22828/) (2026)

## Abstract (arXiv)

Cerebrovascular disease often requires multiple imaging modalities for accurate diagnosis, treatment, and monitoring. Computed Tomography Angiography (CTA) and Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) are two common non-invasive angiography techniques, each with distinct strengths in accessibility, safety, and diagnostic accuracy. While CTA is more widely used in acute stroke due to its faster acquisition times and higher diagnostic accuracy, TOF-MRA is preferred for its safety, as it avoids radiation exposure and contrast agent-related health risks. Despite the predominant role of CTA in clinical workflows, there is a scarcity of open-source CTA data, limiting the research and development of AI models for tasks such as large vessel occlusion detection and aneurysm segmentation. This study explores diffusion-based image-to-image translation models to generate synthetic CTA images from TOF-MRA input. We demonstrate the modality conversion from TOF-MRA to CTA and show that diffusion models outperform a traditional U-Net-based approach. Our work compares different state-of-the-art diffusion architectures and samplers, offering recommendations for optimal model performance in this cross-modality translation task.