文献类型： 会议论文
第一作者： Ramy Merabet
作者： Ramy Merabet 1 ; Florent Sureau 2 ; Alain Trouvé 3 ;
作者机构： 1.Centre Borelli, ENS Paris-Saclay|BioMaps, Université Paris-Saclay, CEA, CNRS, INSERM
2.BioMaps, Université Paris-Saclay, CEA, CNRS, INSERM
3.Centre Borelli, ENS Paris-Saclay
关键词： Geometry;Deep learning;Uncertainty;Three-dimensional displays;Perturbation methods;Biological system modeling;Phantoms
会议名称： IEEE International Symposium on Biomedical Imaging
主办单位：
页码： 1-5
摘要： Intrinsic uncertainties related to the ill-posedness of the PET reconstruction problem are investigated in this work. These uncertainties could lead to artifacts in the reconstructed images in particular when Deep Learning approaches are employed. We propose a framework enabling to define a distinguishability measure between PET images and propose a local analysis of such a measure in a neighborhood of a reference image. This approach is numerically tested on a synthetic experiment using a 3D brain PET phantom derived from a [18F]-FDG exam with 100 anatomo-functional regions extracted. Our analysis allows us to highlight the key factors impacting the detectability of variations and to exhibit concrete examples of clinically meaningful directions along which variations may not be detectable. In addition, we quantitatively analyze the role played by the injected radiotracer dose and show that low-dose scenarios are particularly prone to the presence of reconstruction artifacts.
分类号： tp391.4
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