Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models
This work addresses a critical issue for patients who cannot receive contrast agents due to health risks, such as those with kidney malfunction or pregnancy, by enabling contrast kinetics simulation without actual administration.
The paper tackles the problem of reducing dependency on intravenous contrast agents in dynamic contrast-enhanced MRI for cancer diagnosis by proposing a multi-conditional latent diffusion model to synthesize DCE-MRI temporal sequences, demonstrating the ability to generate realistic breast DCE-MRI images and introducing the Fréchet radiomics distance as a new image quality measure.
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 Fréchet 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 Fréchet radiomics distance calculation at https://pypi.org/project/frd-score.