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Lang, D.M. ; Osuala, R.* ; Spieker, V. ; Lekadir, K.* ; Braren, R.* ; Schnabel, J.A.

Temporal Neural Cellular Automata: Application to Modeling of Contrast Enhancement in Breast MRI.

In: (28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, 23-27 September 2025, Daejeon). Berlin [u.a.]: Springer, 2026. 604-614 (Lect. Notes Comput. Sc. ; 15963 LNCS)
DOI
Synthetic contrast enhancement offers fast image acquisition and eliminates the need for intravenous injection of contrast agent. This is particularly beneficial for breast imaging, where long acquisition times and high cost are significantly limiting the applicability of (MRI) as a widespread screening modality. Recent studies have demonstrated the feasibility of synthetic contrast generation. However, current (SOTA) methods lack sufficient measures for consistent temporal evolution. (NCA) offer a robust and lightweight architecture to model evolving patterns between neighboring cells or pixels. In this work we introduce TeNCA (Temporal Neural Cellular Automata), which extends and further refines NCAs to effectively model temporally sparse, non-uniformly sampled imaging data. To achieve this, we advance the training strategy by enabling adaptive loss computation and define the iterative nature of the method to resemble a physical progression in time. This conditions the model to learn a physiologically plausible evolution of contrast enhancement. We rigorously train and test TeNCA on a diverse breast MRI dataset and demonstrate its effectiveness, surpassing the performance of existing methods in generation of images that align with ground truth post-contrast sequences. Code: https://github.com/LangDaniel/TeNCA.
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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Dynamic Contrast Enhancement ; Image Synthesis ; Nca
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025
Konferzenzdatum 23-27 September 2025
Konferenzort Daejeon
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 15963 LNCS, Heft: , Seiten: 604-614 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)