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Osuala, R. ; Lang, D.M. ; Verma, P.* ; Joshi, S.* ; Tsirikoglou, A.* ; Skorupko, G.* ; Kushibar, K.* ; Garrucho, L.* ; Pinaya, W.H.L.* ; Diaz, O.* ; Schnabel, J.A. ; Lekadir, K.*

Towards learning contrast kinetics with multi-condition latent diffusion models.

In: (Medical Image Computing and Computer Assisted Intervention – MICCAI 2024). Berlin [u.a.]: Springer, 2024. 713-723 (Lect. Notes Comput. Sc. ; 15005 LNCS)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Contrast Agent ; Dce-mri ; Generative Models ; Synthesis
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Medical Image Computing and Computer Assisted Intervention – MICCAI 2024
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 15005 LNCS, Heft: , Seiten: 713-723 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
Institute of Radiation Medicine (IRM)
Förderungen Helmholtz Information and Data Science Academy
Ministry of Science and Innovation of Spain
European Union