A Learnable Prior Improves Inverse Tumor Growth Modeling

Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high comp...

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Veröffentlicht in:	IEEE transactions on medical imaging Jg. 44; H. 3; S. 1297 - 1307
Hauptverfasser:	Weidner, Jonas, Ezhov, Ivan, Balcerak, Michal, Metz, Marie-Christin, Litvinov, Sergey, Kaltenbach, Sebastian, Feiner, Leonhard, Lux, Laurin, Kofler, Florian, Lipkova, Jana, Latz, Jonas, Rueckert, Daniel, Menze, Bjoern, Wiestler, Benedikt
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.03.2025
Schlagworte:	Algorithms Biological system modeling Brain - diagnostic imaging Brain modeling Brain Neoplasms - diagnostic imaging Brain Neoplasms - pathology CMA-ES Computational modeling Deep Learning evolutionary sampling Humans Image Processing, Computer-Assisted - methods Image segmentation Individualized brain tumor modeling inverse biophysics learnable prior Magnetic Resonance Imaging - methods Mathematical models Models, Biological MRI Predictive models Radiation therapy Robustness Training Tumors
ISSN:	0278-0062, 1558-254X, 1558-254X
Online-Zugang:	Volltext
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