Clinical validation of an AI-based automatic quantification tool for lung lobes in SPECT/CT.
Fiche publication
Date publication
septembre 2023
Journal
EJNMMI physics
Auteurs
Membres identifiés du Cancéropôle Est :
Dr RETIF Paul
Tous les auteurs :
Verrecchia-Ramos E, Morel O, Ginet M, Retif P, Ben Mahmoud S
Lien Pubmed
Résumé
Lung lobar ventilation and perfusion (V/Q) quantification is generally obtained by generating planar scintigraphy images and then imposing three equally sized regions of interest on the data of each lung. This method is fast but not as accurate as SPECT/CT imaging, which provides three-dimensional data and therefore allows more precise lobar quantification. However, the manual delineation of each lobe is time-consuming, which makes SPECT/CT incompatible with the clinical workflow for V/Q estimation. An alternative may be to use artificial intelligence-based auto-segmentation tools such as AutoLung3D (Siemens Healthineers, Knoxville, USA), which automatically delineate the lung lobes on the CT data acquired with the SPECT data. The present study assessed the clinical validity of this approach relative to planar scintigraphy and manual quantification in SPECT/CT.
Mots clés
AI-based segmentation, Lobar quantification, Perfusion SPECT/CT, Ventilation SPECT/CT
Référence
EJNMMI Phys. 2023 09 21;10(1):57