%Aigaion2 BibTeX export from HES SO Valais Publications
%Monday 31 August 2026 11:03:04 AM

@MISC{,
        author = {Amirian, Mohammadreza and Chevalley, Arthur and Andrearczyk, Vincent and Klein, Ran and DeKemp, Robert and Moulton, Eric and Kamani, Christel H. and Prior, John O. and Jreige, Mario and Depeursinge, Adrien},
         month = oct,
         title = {Left Ventricle Segmentation in Dynamic 82Rb PET/CT Using Deep Convolutional Neural Networks},
          year = {2025},
  howpublished = {Annual Congress of the European Association of Nuclear Medicine (EANM)},
      abstract = {Precise delineation of the left ventricle (LV) enables advanced analyses of endo-to-epicardium blood perfusion patterns and their gradients in PET, which can support various clinical applications, including coronary microvascular dysfunction. Approaches like simple thresholding or semi-automatic curve fitting often fall short in accurately capturing the LV boundaries in dynamic $^{82}$Rb PET/CT. They also require expert manual input.
This study presents a reliable manual, multi-modal LV delineation strategy, and a fully automatic segmentation algorithm using deep convolutional neural networks (CNNs).}
}