Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion
  Models
Richard Osuala Daniel Lang Preeti Verma Smriti Joshi Apostolia Tsirikoglou Grzegorz Skorupko Kaisar Kushibar Lidia Garrucho Walter H. L. Pinaya Oliver Diaz Julia Schnabel Karim Lekadir
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.