Physics-informed nuclear imaging for glioma localization and recurrence prediction

dc.contributor.author	Sepulveda Leon
dc.contributor.editor	Mašata, David
dc.date.accessioned	2025-09-22T08:51:39Z
dc.date.available	2025-09-22T08:51:39Z
dc.date.issued	2025
dc.description.abstract-translated	Tumor recurrence in high-grade gliomas remains one of the major challenges in neuro-oncology. This work presents a hybrid model combining multimodal MRI and physics-informed equations based on nuclear energy transport, implemented using PINNs (Physics-Informed Neural Networks). Using the BraTS 2021 dataset, the model learns tumor morphology and infers recurrence-prone regions based on the assumption of non-uniform energy deposition similar to that of radionuclide-based therapies in nuclear medicine. This approach aims to bridge medical imaging, nuclear transport modeling, and deep learning to move toward personalized dosimetry and recurrence prediction.	en
dc.format	2 s.	cs
dc.identifier.isbn	978-80-261-1307-2
dc.identifier.isbn	978-80-261-1308-9 (printed)
dc.identifier.uri	http://hdl.handle.net/11025/62830
dc.language.iso	en	en
dc.publisher	University of West Bohemia in Pilsen	en
dc.rights	© University of West Bohemia in Pilsen	en
dc.rights.access	openAccess	en
dc.subject	poster	cs
dc.subject.translated	poster	en
dc.title	Physics-informed nuclear imaging for glioma localization and recurrence prediction	en
dc.type	konferenční příspěvek	cs
dc.type	conferenceObject	en
dc.type.status	Peer reviewed	en
local.files.count	2	*
local.files.size	2675567	*
local.has.files	yes	*

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