ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2024
Stiffness analysis of meningiomas using neural network-based inversion on MR Elastography.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Meningiomas are the most prevalent benign intracranial tumors, and surgical intervention is the primary treatment. The physical characteristics of meningiomas, such as tumor stiffness or consistency, play a crucial role in the surgical approach. This study introduces a machine learning-based MR Elastography (MRE) inversion method, employing an artificial neural network trained with model-based synthetic displacement fields data to estimate mechanical properties of meningiomas. This framework enables accurate stiffness estimation by reducing partial volume effects near the tumor boundary and eliminating the assumption of material homogeneity in simulations (R
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