Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models
<|reference_start|>Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models: 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.<|reference_end|>
@article{osuala2024towards,
    title={Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion
  Models},
    author={Richard Osuala and Daniel M. Lang and Preeti Verma and Smriti Joshi
  and Apostolia Tsirikoglou and Grzegorz Skorupko and Kaisar Kushibar and Lidia
  Garrucho and Walter H. L. Pinaya and Oliver Diaz and Julia A. Schnabel and
  Karim Lekadir},
    journal={arXiv preprint arXiv:2403.13890},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13890},
    primaryClass={eess.IV cs.AI cs.CV cs.LG}
}