# Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models

arXiv: https://arxiv.org/abs/2403.13890 · HTML: https://mlsanity.com/paper/2403.13890/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 20 Mar 2024.

## Placed in

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

## Builds on (most cited first, 13 of 13)

- [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)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains](https://mlsanity.com/paper/2210.04133/) (2022)
- [Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models](https://mlsanity.com/paper/2402.05210/) (2024)
- [Generative AI for Medical Imaging: extending the MONAI Framework](https://mlsanity.com/paper/2307.15208/) (2023)
- [medigan: a Python library of pretrained generative models for medical image synthesis](https://mlsanity.com/paper/2209.14472/) (2022)
- [You Don't Have to Be Perfect to Be Amazing: Unveil the Utility of Synthetic Images](https://mlsanity.com/paper/2305.18337/) (2023)
- [Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation](https://mlsanity.com/paper/2311.10879/) (2023)
- [Synthesis of Contrast-Enhanced Breast MRI Using Multi-b-Value DWI-based Hierarchical Fusion Network with Attention Mechanism](https://mlsanity.com/paper/2307.00895/) (2023)

## Built on by (most cited first, 11 of 11)

- [A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations](https://mlsanity.com/paper/2406.13844/) (2024)
- [Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets](https://mlsanity.com/paper/2412.01496/) (2024)
- [Fairness-Aware Data Augmentation for Cardiac MRI using Text-Conditioned Diffusion Models](https://mlsanity.com/paper/2403.19508/) (2024)
- [Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2409.18872/) (2024)
- [Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data](https://mlsanity.com/paper/2407.12669/) (2024)
- [Comparing Conditional Diffusion Models for Synthesizing Contrast-Enhanced Breast MRI from Pre-Contrast Images](https://mlsanity.com/paper/2508.13776/) (2025)
- [HepatoGEN: Generating Hepatobiliary Phase MRI with Perceptual and Adversarial Models](https://mlsanity.com/paper/2504.18405/) (2025)
- [Predictive Enhancement Calibration for Latent Breast MRI Virtual Contrast Enhancement](https://mlsanity.com/paper/2608.03612/) (2026)
- [Pre- to Post-Contrast Synthesis of Breast DCE-MRI using Latent Bridge Matching](https://mlsanity.com/paper/2608.10000/) (2026)
- [Anatomy-Aware Synthesis of Post-Contrast Breast MRI from Pre-Contrast Images](https://mlsanity.com/paper/2609.27015/) (2026)
- [Anguinus Sculpturae: Compositional Synthesis of Peak-Enhancement Breast DCE-MRI Scans](https://mlsanity.com/paper/2609.33611/) (2026)

## Abstract (arXiv)

Contrast agents in dynamic contrast enhanced magnetic resonance imaging allow to localize tumors and observe their contrast kinetics, which is essential for cancer characterization and respective treatment decision-making. However, contrast agent administration is not only associated with adverse health risks, but also restricted for patients during pregnancy, and for those with kidney malfunction, or other adverse reactions. With contrast uptake as key biomarker for lesion malignancy, cancer recurrence risk, and treatment response, it becomes pivotal to reduce the dependency on intravenous contrast agent administration. To this end, we propose a multi-conditional latent diffusion model capable of acquisition time-conditioned image synthesis of DCE-MRI temporal sequences. To evaluate medical image synthesis, we additionally propose and validate the Fr'echet radiomics distance as an image quality measure based on biomarker variability between synthetic and real imaging data. Our results demonstrate our method's ability to generate realistic multi-sequence fat-saturated breast DCE-MRI and uncover the emerging potential of deep learning based contrast kinetics simulation. We publicly share our accessible codebase at https://github.com/RichardObi/ccnet and provide a user-friendly library for Fr'echet radiomics distance calculation at https://pypi.org/project/frd-score.