During my PhD, I learned assessed, and applied several aspects, and new potential applications of AI in molecular imaging: from ML image classification, prediction of disease outcome, prediction of response to therapy, to DL segmentation. Finally, during my period at the Universitatsspital of Zurich (USZ) I also accomplished, in collaboration with the Swiss Federal Institute of Technology (ETH), an innovative study (in submission) regarding the prediction of PET volumes from MRI images, assessing simultaneous PET/MRI. In this thesis, I will describe the main results of the studies published during my PhD time following an anatomical and computational order.

Artificial intelligence in molecular imaging: from machine to deep learning

LAUDICELLA, Riccardo
2022-11-30

Abstract

During my PhD, I learned assessed, and applied several aspects, and new potential applications of AI in molecular imaging: from ML image classification, prediction of disease outcome, prediction of response to therapy, to DL segmentation. Finally, during my period at the Universitatsspital of Zurich (USZ) I also accomplished, in collaboration with the Swiss Federal Institute of Technology (ETH), an innovative study (in submission) regarding the prediction of PET volumes from MRI images, assessing simultaneous PET/MRI. In this thesis, I will describe the main results of the studies published during my PhD time following an anatomical and computational order.
30-nov-2022
AI; radiomic; machine learning; deep learning; PET/CT; PET/MRI; PSMA; FDG; Amyloid; RLT
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/3244659
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